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Maintenance Location Routing for Rolling Stock Under Line and Fleet Planning Uncertainty


Abstract and Figures

Rolling stock needs regular maintenance in a maintenance facility. Rolling stock from different fleets are routed to maintenance facilities by interchanging the destinations of trains at common stations and by using empty drives. We consider the problem of locating maintenance facilities in a railway network under uncertain or changing line planning, fleet planning, and other uncertain factors. These uncertainties and changes are modeled by a discrete set of scenarios. We show that this new problem is NP-hard and provide a two-stage stochastic programming and a two-stage robust optimization formulation. The second-stage decision is a maintenance routing problem with similarity to a minimum cost-flow problem. We prove that the facility location decisions remain unchanged under a simplified routing problem, and this gives rise to an efficient mixed-integer programming (MIP) formulation. This result also allows us to find an efficient decomposition algorithm for the robust formulation based on scenario addition (SA). Computational work shows that our improved MIP formulation can efficiently solve instances of industrial size. SA improves the computational time for the robust formulation even further and can handle larger instances due to more efficient memory usage. Finally, we apply our algorithms on practical instances of the Netherlands Railways and give managerial insights.
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Maintenance location routing for rolling stock under line and
fleet planning uncertainty
Denise D. T¨onissen
School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
Joachim J. Arts
Luxembourg Centre for Logistics and Supply Chain Management, University of Luxembourg, Luxembourg City, Luxembourg;
and School of Industrial Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands,
Zuo-Jun (Max) Shen
Department of Industrial Engineering and Operations Research, Department of Civil and Environmental Engineering,
Tsinghua-Berkeley Shenzhen Institute, University of California, Berkeley, Berkeley, California 94720, USA,
August 4, 2018
Rolling stock needs regular maintenance in a maintenance facility. Rolling stock from different
fleets are routed to maintenance facilities by interchanging the destinations of trains at common
stations and by using empty drives. We consider the problem of locating maintenance facilities in
a railway network under uncertain or changing line planning, fleet planning and other uncertain
factors. These uncertainties and changes are modeled by a discrete set of scenarios. We show that
this new problem is NP-hard and provide a two-stage stochastic programming and a two-stage robust
optimization formulation. The second-stage decision is a maintenance routing problem with similarity
to a minimum cost-flow problem. We prove that the facility location decisions remain unchanged
under a simplified routing problem and this gives rise to an efficient mixed integer programming
(MIP) formulation. This result also allows us to find an efficient decomposition algorithm for the
robust formulation based on scenario addition (SA). Computational work shows that our improved
MIP formulation can efficiently solve instances of industrial size. SA improves the computational
time for the robust formulation even further and can handle larger instances due to more efficient
memory usage. Finally, we apply our algorithms on practical instances of the Netherlands Railways
and give managerial insights.
Keywords: Two-stage optimization, column-and-constraint generation, facility location, rolling
stock, maintenance routing
1 Introduction
We study the optimal location of maintenance facilities for rolling stock in a railway network. This
new problem came to us through a 4 year collaboration with the Netherlands railways (NS). Like most
facility location problems, there is a set of candidate facilities and their annual costs, and we have to
decide which facilities to open. However, the maintenance location routing problem has essential features
that make it substantially different from other facility location problems. In particular, the customers
of facilities (train units) have to travel over a rigid railway network. The transportation costs of these
train units are more intricate than can be modeled by fixed allocation costs. The problem of routing
these train units to the maintenance facilities is called the maintenance routing problem (MRP). The
MRP cannot be separated from the facility location problem because the ease with which a facility can
be reached depends intricately on the railway infrastructure and the line plan.
A line plan consists of a set of train lines, where each line is a path in the railway network that is
operated with a certain frequency by one rolling stock type. The line and fleet plan within a railway
network change regularly to accommodate changing travel demands. As a consequence, any reasonable
facility location plan must work well under a wide variety of line and fleet plan scenarios. This includes
changes in how lines run, up and down-scaling of service frequencies on any given line, the rolling stock
types assigned to the lines, and the introduction of new rolling stock types.
To deal with the features we outline above (maintenance location routing, line planning, and fleet
planning), we provide two novel models to help managers decide where to locate maintenance facilities.
These two models reflect different attitudes to risk that the decision maker may hold. The first model is
a two-stage stochastic programming model that seeks to minimize the annual cost of the facilities and
the annual maintenance routing cost averaged over a set of line plan scenarios. We call this the stochastic
maintenance location routing problem (SMLRP). This model is appropriate for decision makers that are
risk neutral. The second model is similar to the first, however it seeks to minimize the performance of
the worst-case situation. Such an objective is often called a minimax or an absolute robust objective
(Kouvelis and Yu, 2013). This model is therefore called the robust maintenance location routing problem
(RMLRP) and can be seen as a two-stage robust optimization model (Ben-Tal et al., 2004). This model
is appropriate for risk averse decision makers. The attitude of the NS towards risk is neither entirely
risk neutral nor entirely risk averse. Therefore, they would like to study the solutions of both models.
The first-stage decision for the SMLRP and RMLRP is to open a facility, given a set of candidate
facilities, annual facility depreciation costs, and a discrete set of line planning scenarios. In the second-
stage the annual maintenance routing costs for a first-stage location decision are determined for each
line plan scenario. The second-stage problem corresponds with the MRP. We give the deterministic
equivalents of the two-stage models. The deterministic equivalents are multi-commodity flow models and
we prove that exactly the same facilities are opened when we simplify these models to single-commodity
flow models. This simplification decreases the number of variables and constraints for practical sized
instances by millions. We call this formulation the improved mixed integer program (IMIP). The IMIP
makes it possible to fit practical sized instances into memory and to solve them with CPLEX within
reasonable time. Furthermore, we improve the computational time for the RMLRP further, by providing
a scenario addition (SA) method. The SA method is a column-and-constraint (also called row-and-
column) generation method that is specifically developed for problems with discrete scenario sets. SA
adds the scenario constraints iteratively to the IMIP until an optimal solution is found, which improves
the computational time and memory requirements for most instances. A similar idea is presented in
Zeng and Zhao (2013) and Chan et al. (2017) for polyhedral uncertainty sets.
The main contributions of this paper are as follows.
1. We are the first to study a maintenance location problem on a railway network. In this setting it
is necessary to incorporate the annual maintenance routing cost in the facility location decision.
This leads to a new class of problems that we call maintenance location routing problems.
2. We provide a two-stage stochastic programming formulation and a two-stage robust optimization
formulation for the maintenance location routing problem that can deal with uncertainties that are
inherent in any application. In particular, our model can deal with uncertainties in line and fleet
3. We provide a mixed integer formulation and an even more efficient SA algorithm that can solve
instances of practical size.
4. We perform a case study for the Netherlands Railways (NS) and give managerial insights.
The paper starts with a literature review, followed by a detailed description of the MRP. In Section 4,
we model the RMLRP and the SMLRP as two-stage problems and in Section 5.1 we provide our solution
methodology. In Section 6, we perform computational experiments on randomly generated instances.
We study the influence of the number of scenarios and facilities on the solution time and compare the
algorithms. In Section 7, we present our case study for the NS and provide managerial insights.
2 Literature review
Facility location models have been studied extensively in the literature (Drezner and Hamacher, 2001;
Laporte et al., 2016). The combination with operational supply chain decisions (Melo et al., 2009),
vehicle routing (Nagy and Salhi, 2007), and uncertainty have also been studied in the literature (Snyder,
2006). However, the combination of facility location with maintenance routing has only been studied
in the context of aviation applications (Feo and Bard, 1989; Gopalan, 2014). Furthermore, to the best
of our knowledge, there are only four papers that combine maintenance with facility location (Lieckens
et al., 2013; Rappold and Van Roo, 2009; van Ommeren and Bumb, 2006; Xie et al., 2016). However, the
settings of these papers differ considerably from ours, and the authors use heuristics while we seek exact
optimal solutions. We first describe the maintenance routing literature and we continue to the two-stage
robust and stochastic facility location literature.
2.1 Maintenance routing
Many papers have been written about maintenance routing for railway applications. Anderegg et al.
(2003), consider the situation in the German and Swiss Federal Railways where maintenance routing is
part of the long-term vehicle scheduling problem. The authors use a minimum cost flow formulation that
is often used for vehicle routing. Maintenance cannot be adapted into the flow model and a heuristic
modification is used to satisfy the maintenance constraints. Mar´oti and Kroon (2005, 2007) consider
maintenance routing for the NS, where maintenance routing is not part of the vehicle scheduling problem.
They use a two- to five-day time window during which a train unit is routed to the maintenance facility.
The main reason for this is that timetables and rolling stock schedules are dense, and that there are many
disturbances. Consequently, long-term models cannot take shunting issues and disturbances into account.
The authors formulate this NP-hard problem as two different multicommodity flow problems, and they
solve the models with CPLEX. Other papers consider variants of the locomotive planning problem. The
locomotive planning problem assigns locomotives to a set of train units in such a way that it minimizes
the cost and satisfies a number of business and operational constraints. For a recent survey on the
locomotive assignment problem, that also includes variants with some maintenance constraints, see Piu
and Speranza (2014). Furthermore, there are many papers about maintenance routing for aviation such
as those of Clarke et al. (1997), Talluri (1998), and Sarac et al. (2006).
2.2 Two-stage robust and stochastic facility location
Uncertainty for facility location models can be classified into three categories (Shen et al., 2011): receiver-
side uncertainty; in-between uncertainty; and provider-side uncertainty. Like most stochastic facility
location models (see the references in Snyder (2006) and Swamy and Shmoys (2006)), our paper focuses
on the first two uncertainties. These uncertainties are related to customer uncertainty (for example,
customer demand or customer location) and incomplete knowledge about the transportation network
topology, transportation times, or costs between facilities and customers. A common feature of the
receiver-side and in-between uncertainties is that the uncertainty does not change the topology of the
provider-receiver network once the facilities have been built. Our problem does not share this feature
as the different line plans do change the topology of the provider-receiver network; a different line plan
changes the network over which rolling stock are routed to a maintenance facility.
The SMLRP bears some resemblance to supply chain network design under uncertainty, which in-
cludes the location of facilities within a supply chain. Santoso et al. (2005) design a supply chain network
consisting of suppliers, processing facilities, and customers under cost, demand, supply and capacity un-
certainty. A sample average approximation is used to generate a discrete set of scenarios and the model is
solved with Benders decomposition. Santoso et al. (2005) describe and test many acceleration techniques,
that we also implement in the online appendix of this paper. The accelerated Benders decomposition
method works well for their problem, but is less successful for our model as compared with the IMIP.
Other examples of Benders decomposition for supply chain network design include fixed charge network
design (Costa, 2005), freight-forwarding network design (¨
Uster and Agrahari, 2011), reverse supply chain
design (Santibanez-Gonzalez and Diabat, 2013), and closed loop supply chain network design (Khatami
et al., 2015).
Two types of methods are generally used for two-stage robust problems. The first method is similar to
Benders decomposition and uses constraint generation based on the dual information of the slave problem.
Alvarez-Miranda et al. (2015), use Benders decomposition accelerated with additional cuts and a primal
heuristic on a two-stage robust facility location problem with a discrete set of scenarios. An important
difference to our paper is that their allocation of customers to facilities does not include the routing of
customers (rolling stock) to (maintenance) facilities. Another difference is the considered uncertainty
and recoverability of the model. In our model, we open facilities in the first-stage and allocate customers
to these facilities in the second-stage. ´
Alvarez-Miranda et al. (2015) open facilities and assign customers
in the first-stage and the second-stage is used to recover the facilities and customer assignment to the
revealed scenario. Gabrel et al. (2014) solve a non-linear convex two-stage robust location transportation
problem. In this problem a commodity has to be transported from each of the mpotential sources to
each of the ndestinations. The demand of the destinations is uncertain. The authors use a cutting plane
algorithm based on Benders decomposition, where the slave problem is NP-hard. Furthermore, Benders
decomposition has been applied to two-stage robust unit commitment problems (Bertsimas et al., 2013;
Jiang et al., 2011).
The second method consists of column-and-constraint generation procedures. Zeng and Zhao (2013)
show that a column-and-constraint generation procedure solves a two-stage robust location transporta-
tion problem with demand levels in a polyhedral uncertainty set an order of magnitude faster than
Benders decomposition. Chan et al. (2017) apply a column-and-constraint generation procedure to a
robust facility location problem where the demand points are uncertain and An et al. (2014) apply it
to the reliable p-median facility location problem. The strategy is also used for two-stage robust unit
commitment (An and Zeng, 2015; Zhao and Zeng, 2012) and a two-stage robust distribution network
reconfiguration problem (Lee et al., 2015).
3 Maintenance routing
The goal of the MRP is to determine the annual routing costs for train units to enter given maintenance
facilities with given capacities. A maintenance facility is a facility that is responsible for the planned
inspections and maintenance of rolling stock. The frequency of inspections and maintenance is dependent
on the rolling stock type and typically occurs once every half year up to every month. The maintenance
routing is influenced by the current line plan, a set of routes (paths) in a rail network, operated with a
certain frequency by a specific rolling stock type. The stations where a line starts or ends are called end
stations. At an end station, all passengers leave the train and the train unit drives back or continues on
another line after a break. Between the end stations, a line often has many other regular train stations
where passengers can leave or enter the train. The transport from the train lines to the maintenance
facilities is done with help of interchanges. An interchange swaps the destinations of two train units of
the same rolling stock type that are on connecting train lines. Connecting train lines share an end station
and at that end station the train unit can be interchanged. The train units continue on each other’s
train line after such an interchange. A train that requires maintenance is interchanged with another
train unit until it reaches a train line connected to a maintenance facility. When a maintenance facility
cannot be reached by a specific train unit via these interchanges, deadheading is used for the remaining
trip. Deadheading is driving with an empty train (no passengers), and is undesirable. Deadheading gives
additional driving cost and the train unit is not available for public transport, which can result in shorter
trains and passenger discomfort.
The operational problem of routing specific train units to their maintenance facilities is studied by
Mar´oti and Kroon (2005, 2007). The operational maintenance routing problem is solved daily and its
decisions are based on operational information such as the arrival and departure time of train units and
the shunting possibilities of the end stations. In this section, we introduce a new maintenance routing
model that determines the annual operational maintenance routing cost by taking maintenance routing
decisions on an aggregate level. This enables us to combine the maintenance routing model with facility
location decisions in Section 4. Our objective is to minimize the annual interchange and deadheading
cost. Consequently, we do not consider separate train units, but work with the average number of
maintenance visits departing from each line per year, the routes for each visit, and the annual number
of interchanges.
3.1 Problem description
Given is a physical rail network GP= (NP, EP), consisting of nodes (stations) NPand edges (railway
tracks) EP, a set of end stations ENP, and a set with opened facilities OE, where every opened
facility has a capacity that represents the maximum number of maintenance visits the location can handle
per year. Furthermore, we are given a line plan that consists of a set of lines L, with for each line the type
of rolling stock, the maintenance frequency per year, its end stations and the deadheading cost to each
maintenance facility. The line plan also specifies the set of possible interchanges, with a coordination
cost for each interchange, end station interchange capacities, and a network interchange budget.
The network interchange budget Grestricts the annual number of interchanges in the network. The
interchange budget is highly dependent on the frequently changing rolling stock schedule and it is possible
to improve certain interchanges by making small changes to the rolling stock schedule. Modeling this
interchange budget allows us to gain insight into which interchanges should be made possible or improved,
while also constraining the number of interchanges that can be used. Furthermore, we constrain the
annual number of interchanges at each end station.
In the left-hand side of Figure 1, we show an example of a physical railway network. Here we exclude
all stations that are not an end station in at least one of the line plans shown in the middle and on the
right of Figure 1. For the MRP, only one line plan for the network GPis given. However, in Section
4, the different line plans for network GPplay an important role. Each line (edge) connects two end
stations (nodes). The number of end stations can differ between the line plans; e.g. station Wis an end
station in the middle of Figure 1, while it is an in-between station of the line (X, V ) on the right-hand
side. Furthermore, there are two rolling stock types in this example. Rolling stock type a, is a regional
train, stopping at every small station, while type bis an intercity train that only stops at the large cities.
An example of an interchange for the middle picture is line (U, V ) to line (V, W ), while an interchange
from (U, V ) to line (U, W ) is not possible because the rolling stock types do not match.
Figure 1: The physical rail network on the left and two line plan possibilities.
3.2 Maintenance routing model
We model the MRP as a flow-based model. We only allow interchanges followed by deadheading directly
to the already opened maintenance facilities. The reason for this is practical, and not a restriction of
the model. Routes with deadheading followed by interchanges are not often used in practice, can be
very expensive, and cause imbalances in the number of train units per line, which need to be solved.
Consequently, we do not include these kinds of routes in our model. We use the line plan and physical
railway network to create a directed maintenance routing flow graph GF= (NF, AF).
For every line, we make a node; the set with these nodes is denoted by NLNF.
We create one source Sthat is connected with a directed arc to each node in NL.
We create an arc between lines where an interchange is possible. The cost of this arc are the
interchange coordination cost. The set of these interchange arcs is denoted by AIAF.
We create a node for every open facility and denote the set of open facilities by NONF. Each
node in NOis connected with an arc to the sink T.
For each node nNL, we make an arc to each facility. The cost of the arc is 0 when a line is
connected to the facility and it is the deadheading costs from a line to a facility when it is not
connected. The set of these incoming facility arcs is denoted by AOAF.
In Figure 2, we demonstrate how to apply this transformation. The left-hand side depicts a line plan
for the physical railway network in Figure 1, where aand bare the rolling stock types and 0-8 are the
line numbers. The right-hand side depicts the maintenance routing flow graph GF= (NF, AF) for the
line plan on the left when facilities NO={U, W, Y }are opened.
Figure 2: Left a possible line plan for the physical railway network in Figure 1. Right the resulting
maintenance routing flow graph (GF= (NF, AF)). The arcs from and to the source Sand sink Tare
dashed blue, the interchange arcs (AI) solid red, and the arcs to the facilities (AO) are dotted black.
The aforementioned additional combinations of interchanges and deadheading can easily be allowed by
creating an additional arc from every line node to every other line node, which represents the deadheading
from one line to another.
Proposition 1. The number of nodes and arcs in the maintenance routing flow graph is polynomial in
the number of lines and end stations.
Proof. The number of nodes in the maintenance routing flow graph is equal to |L|+|NO|+ 2. The
number of arcs is equal to |L|+|L||NO|+|AI|+|NO|, where |AI|is bounded by |L|(|L| − 1).
The maintenance frequency of a line lis the annual number of train units originating from that line
that require maintenance. The maintenance frequency for line lLis defined by the parameter ml,
and nlNLis the node associated with line l. The flow through arc aAFthat is associated with the
yearly maintenance frequency originating from line lLis represented by the decision variable zl(a).
For example, z4(2,7) represents the frequency of interchanges from line 2 to line 7 for a maintenance
visit originating from line 4, and z1(8, y) represents the frequency of maintenance visits originating from
line 1 that reach maintenance facility yvia line 8.
We define δin(n) and δout (n) as the set of ingoing and outgoing arcs of node nNF, and the set
of arcs representing the interchanges going through end station eis defined as AeAI. The cost of
arc afor flow of type lis cl(a), and is only defined for the arcs that represent the interchanges and
the deadheading (AIAO). The capacity for the facilities, qnnNO, is measured by the yearly
maintenance frequency that the facility can handle. Furthermore, the number of interchanges at end
station eEis restricted by the parameter ge, and the annual number of maintenance visits over the
entire network by G.
We summarize the notation in Table 1 and formulate the following linear program:
(MRP) min X
s.t. X
zl(a) = X
zl(a)nNF\ {S,T },lL, (2)
zl(a) = mllL, a δin(nl)\AI,(3)
zl(a) = X
zl(a)geeE, (5)
zl(a)G, (6)
zl(a)0aAF,lL. (7)
Constraints (1) restrict the number of yearly maintenance visits that can be assigned to opened facility
nto its capacity. Constraints (2) are the flow conservation constraints, while Constraints (3) and (4)
guarantee that every maintenance visit is assigned to a facility. Constraints (4) are necessary to exclude
alternative solutions with more flow than the number of yearly maintenance visits; these solutions are
possible because some of the routes to the facilities have zero costs. Constraints (5) and (6) are the end
station and interchange budget constraints.
We can solve the MRP in polynomial time because it is a linear program. Furthermore, the problem
is similar to the minimum cost flow problem with the exception of the multiple commodities lLand
Constraints (1), (5), and (6). We show that even with these additional constraints the MRP has an
optimal integer solution zl(a) if ge(eE), G,ml(lL), and qn(nNF) are integers.
Proposition 2. If ge(eE),G,ml(lL), and qn(nNF)are integers, then for any basic
feasible solution of the MRP each flow zl(a) (aAF,lL)is an integer.
The proof of Proposition 2 can be found in online Appendix A.
Graph notation and sets:
GP= (NP, EP) physical railway network consisting of nodes NPand edges EP
Eset with end stations (ENP)
Oset with opened locations (OE)
GF= (NF, AF) maintenance routing flow graph consisting of nodes NFand arcs AF
Ssource node
Tsink node
NOset of nodes associated with the opened facilities O(NONF)
Lset with all train lines
NLset of nodes associated with the lines L(NLNF)
nlthe node associated with line l
AOset of arcs that go to the opened facility nodes (AOAF)
AIset of interchange arcs (AIAF)
Aeset of interchange arcs for end station e(AeAI)
δin(n) set containing the incoming arcs of node n(δin(n)AF)
δout(n) set containing the outcoming arcs of node n(δout(n)AF)
mlmaintenance frequency for line lL
cl(a) cost for using arc afor a maintenance visit originating from line lL
qncapacity of facility nNO
geinterchange budget of end station eE
Gnetwork interchange budget
Decision variables:
zl(a) maintenance frequency decision variable for arc aand line l
Table 1: Graph notation, sets, parameters, and decision variables for the MRP.
4 Maintenance location routing
In this section, we introduce the SMRLP and RMRLP. We model both problems as two-stage models.
We use a discrete scenario set D, in which every scenario defines a line plan (see also Figure 1). The
first-stage decision is the decision to open a facility, given a set of candidate facilities, annual facility
depreciation costs, and a discrete set of scenarios. The second-stage decision is taken for each scenario
and corresponds to the MRP discussed in Section 3. The RMLRP optimizes the cost for the worst-case
scenario dD, and the SMLRP optimizes the cost for the average case.
The SMLRP and RMLRP are NP-hard because the capacitated facility location problem (CFLP) is
a special case of these problems. In the CFLP, a set of demand points, a set of facilities, the capacities
of the locations and their cost, and the cost of assigning a demand point to a facility are given. The
objective is to open a set of facilities and to assign each demand point to a facility, while not exceeding the
capacity of each facility and minimizing the cost. When our model has only one scenario and the network
interchange budget for this scenario is 0, then only deadheading from the lines to the locations is possible.
When we interpret the lines as demand points, with the required maintenance visit frequency as demand,
then the deadheading cost is exactly the same as the assignment costs, and the SMLRP/RMLRP can
be used to solve the CFLP.
4.1 The two-stage models
As in Section 3, we are given a physical rail network GP= (NP, EP), consisting of nodes (stations) NP
and edges (railway tracks) EP. Furthermore, we are given a set of discrete scenarios dD, in which each
scenario defines a line plan: a set of lines LddD, with for each line the type of rolling stock and the
maintenance frequency per year, the end stations EdNPdD, and their location in the physical
rail network. A line plan for a scenario specifies the set of possible interchanges, with a coordination cost
for each interchange, the deadheading cost from each line to each facility, the end station interchange
capacities, and the interchange network budget. Finally, we are given a set of candidate facilities C, with
for every candidate facility a capacity qnnNC, that represents the maximum annual maintenance
frequency the location can handle. The first-stage decision is represented by the binary decision variable
y∈ {0,1}|C|, which is 1 when a facility is opened and 0 otherwise.
For every first-stage decision y∈ {0,1}|C|and scenario dD, there is a directed maintenance
location routing flow graph Gdy
F= (Ndy
F, Ady
F). The notation of the variables and parameters is similar
to those in Section 3. The maintenance frequency for line lLand scenario dDis defined by
the parameter md
l, and nd
lis the node associated with line land scenario d. The flow through arc a
associated with the annual maintenance frequency from line lLd, in scenario dD, is represented
by the decision variable zd
l(a). We define δd
in(n) and δd
out(n) as the set of ingoing and outgoing arcs of
node nfor scenario d. Here Ad
Iis the set of interchange arcs and Ad
in(n), the set of arcs
going to the candidate facilities. The cost of arc afor line land scenario dis cd
l(a). The arc costs are
only defined for arcs in the set SdD(Ad
C). The end station interchange budget gd
eis a capacity
on the interchange frequency at end station eEdfor scenario dD. The set of arcs representing the
interchanges going through end station efor scenario dis denoted by Ad
e. Furthermore, the number of
annual interchanges in the entire network for each scenario dDis restricted by the parameter Gd. The
weights wddDdenote the expected fraction of time that a line plan is used during the life time of
the facilities and is only relevant for the SMLRP. We formulate the following two-stage models:
(SMLRP-2SSP) min X
(RMLRP-2SRO) min X
cnyn+ max
s.t. X
yn∈ {0,1} ∀nNC,(9)
MRPd(y) = min X
s.t. X
l(a) = X
F\ {S,T },lLd,(11)
l(a) = md
llLd, a δd
l(a) = X
The objectives of the SMLRP-2SSP and RMLRP-2SRO formulations optimize the annual facility cost
in combination with the average/worst-case maintenance routing cost, under Constraint (8). Constraint
(8) guarantees that the opened facilities have sufficient combined capacity to handle all maintenance
visits, so that the second-stage problem has a feasible solution. Here MRPd(y) optimizes the maintenance
routing cost for scenario dand first-stage decision y. Constraints (10) guarantee that maintenance visits
can only be assigned to opened facilities up to their capacity. Constraints (11) are the flow conservation
constraints, while Constraints (12) and (13) guarantee that every maintenance visit is assigned to a
facility. Constraints (14) and (15) are the end station and network interchange budgets constraints.
The recourse matrices of the SMLRP-2SSP and RMLRP-2SRO do not have a fixed size which is
different from most two-stage problems. The network topology for each MRPd(y) is different and as a
consequence the values and dimensions of the recourse matrices are different for each scenario. Further-
more, Constraint (8) guarantees that each feasible first-stage solution is feasible for the MRPd(y) for any
scenario dD. Consequently, we have relatively complete recourse.
5 Solution methodology
The two-stage formulations of Section 4 are not practical in terms of computation. In this section, we
reformulate these problems into a mixed integer programming formulation, that is also known as the
deterministic equivalent or extensive form (Birge and Louveaux, 1997). Then we provide an improved
mixed integer programming formulation (IMIP) that is equivalent in terms of the optimal objective and
optimal facility decisions, but not with respect to the second-stage decisions. This improved formulation
is very efficient in finding the optimal facility location decisions.
In addition, we design a column-and-constraint generation algorithm (similar to Zeng and Zhao, 2013;
Chan et al., 2017) for the RMLRP. Our method, called scenario addition (SA), solves the IMIP with a
subset of scenarios and adds an additional scenario in each iteration. The algorithm reaches optimality
in a finite number of iterations. SA uses the fact that for the RMLRP only the facility solution yand
the worst-case scenario are required for the solution.
We also applied accelerated Benders decomposition to the SMLRP and RMLRP. However, accelerated
Benders decomposition performed very poorly in the computational experiments and therefore the details
of this approach are reported in online Appendix B.
5.1 Mixed integer programming formulation
The first step to reformulate the two-stage models SMLRP-2SSP and RMLRP-2SRO to mixed integer
programming formulations is to make one large directed maintenance location routing flow graph. This
graph contains all scenarios, instead of one graph for every scenario. The steps used in Section 3 are
adapted to generate the directed graph GM= (NM, AM) as follows.
For every scenario and line, we make a node. The set with all line nodes belonging to a scenario
dDis denoted by Nd
We make one source Sthat is shared for all scenarios. The source is connected with an arc to each
node in SdDNd
We make an arc between every line where an interchange is possible (connected, same rolling stock
type, same scenario), and the cost is the interchange coordination cost.
We make a node for every candidate facility, and each of these nodes is connected with an arc to
the sink T. The set of candidate facility nodes is denoted by NC.
For each node nSdDNd
L, we make an arc to each facility. The cost of this arc is the deadheading
cost of the line to the facility. The cost of the arc is zero when deadheading is not necessary because
the line associated with the node is connected to the facility.
The sets Nd
Mand Ad
Mcontain all nodes or arcs that can be reached by flow from scenario dD.
Furthermore, by Proposition 2, the flow zd
l(a) will be integer when ge(eE), G,ml(lL), and qn
(nNF) are integers. The two-stage models SMLRP-2SSP and RMLRP-2SRO can be reformulated
to the following mixed integer programming formulations:
s.t. X
l(a)ynqndD, nNC,(17)
l(a) = X
l(a)dD, nNd
M\ {S,T },lLd,(18)
l(a) = md
ldD, lLd,aδd
l(a) = X
edD, eEd,(21)
l(a)GddD, (22)
l(a)0dD, aAd
yn∈ {0,1} ∀nNC,(24)
with the following additional constraints for the RMLRP-MIP:
zmax X
l(a)dD. (25)
Constraints (17)-(23) are comparable to (10)-(16) from Section 4, however now defined for the graph
GM= (NM, AM) and for all scenarios. Note that in the maintenance location routing flow graph GM,
all arcs besides those going to the sink can only be traversed by flow from one scenario. Consequently,
by the use of Ad
Mand Nd
Min place of AMand NM, we have removed many unnecessary constraints
in Constraints (18) and (23). Constraints (25), which are only used for the RMLRP-MIP, guarantee
that zmax equals the routing cost of the worst-case scenario. The SMLRP-2SSP and SMLRP-MIP are
equivalent. However, for the RMLRP-2SRO and RMLRP-MIP only the opened and closed facilities y
and the objective value are equivalent. Because we are not interested in the second-stage decisions, the
RMLRP-MIP can be used as if it is equivalent to the RMLRP-2SRO.
5.2 Improved mixed integer programming formulation
The RMLRP-MIP and SMLRP-MIP require so much available memory that only very small instances
can be solved. However, the required memory can be significantly reduced by two observations. The
first observation is that we do not need to distinguish the scenario for each flow. Without the index d,
we can still determine to which scenario a flow belongs based on the incoming arcs of the facility nodes
as they always belong to only one scenario. For the second observation, we use practical knowledge of
the cost structure of the problem. For practical instances the cost of an interchange or deadheading
C) depends on the rolling stock type and the scenario, and not on the originating
line. As a consequence cd
m(a) = cd
n(a)m, n L, when the rolling stock type assigned to line mand
nare the same. Because it is not allowed to interchange train units with different rolling stock types,
dropping the lindex will not change the cost. Consequently, the incoming flow per facility will be the
same, resulting in the same facility decisions and the same optimal objective value.
Furthermore, Constraints (20) can be omitted, because without the lindices, all flow is already
restricted by Constraints (19). Additionally, in Constraints (17) qncan be much higher than the total
number of maintenance visits (PlLdmd
l) for a scenario dD. Tightening Constraints (17), by replacing
qnby ˆ
n= min(qn,PlLdmd
l) for every scenario dimproves the initial LP bound. Finally, we denote
in(n) and SdDδd
out(n) as δin (n) and δout(n) respectively. This gives the following improved mixed
integer programming (IMIP) formulations:
s.t. X
ndD, nNC,(26)
z(a) = X
z(a)nNM\ {S,T },(27)
z(a) = md
ldD, lLd,aδd
edD, eEd,(29)
z(a)GddD, (30)
yn∈ {0,1} ∀nNC,(32)
with the following additional constraints for the (RMLRP-IMIP):
zmax X
c(a)z(a)dD. (33)
Note that the lindex is still required for Constraints (28). The reason for this is that although during
routing we need not be concerned with the origin of the flow, we still need to make sure that the right
amount of flow starts from each origin. As a consequence, the parameter md
lis needed to specify the
amount of flow that originates from each line.
Theorem 1. The SMLRP-IMIP and RMLRP-IMIP have identical optimal costs and facility decisions
yas the SMLRP-MIP and RMLRP-MIP, respectively, when cd
m(a) = cd
n(a)m, n Lif the rolling
stock types assigned to line mand nare the same.
Proof. For every aδout(S), zd
l(a)0 for only one scenario dand line l, and for all other scenarios
and lines zd
l(a) = 0. This is the case because of Constraints (19) and (20) that guarantee that every
line node receives only md
lflow for one land dand 0 for all other lines and scenarios. These line nodes
are only connected to the line nodes of the same scenario and the facility nodes. Consequently, the line
nodes can only receive flow of one scenario type, and the scenario of the incoming flow of the facilities, is
equal to the scenario to which the line node from that the incoming facility flow originates from belongs
to. Furthermore, dropping the indices will not influence the amount of flow and cost for every arc. The
amount of flow for each arc z(a) will become equal to PdDPlLdzd
l(a). Furthermore, the cost of the
flow remains the same because every arc aAd
Ccan only be reached by maintenance visits of
exactly one rolling stock type and scenario. Consequently, the same facilities will be opened and the
objective value will be the same.
Theorem 2. The SMLRP-IMIP and RMLRP-IMIP reduce the number of variables by
I|)compared with the SMLRP-MIP and RMLRP-MIP, respectively.
Proof. Note that |AM|=PdD(|Ld|+|Ld||NC|+|Ad
I|) + |NC|and |Ad
We subtract the number of variables in the IMIP from the number of MIP variables. The number of
removed variables is
M|− |AM|
I|)− |NC|
I|)− |NC|
I|− |NC|
I|− |NC|
Consequently, the number of removed variables is of the order of Θ(PdD|Ld|2|NC|+|Ld||Ad
Theorem 3. The SMLRP-IMIP and RMLRP-IMIP reduce the number of constraints by
Θ(PdD|Ld|2+|NC||Ld|)compared with the SMLRP-MIP and RMLRP-MIP, respectively.
Proof. We subtract the number of constraints in the IMIP from the number of MIP constraints. Note
that we removed Constraint (20) and that only Constraints (18) and (27) are different. Furthermore,
|NM|=PdD|Ld|+|NC|+ 2 and |Nd
M|=|Ld|+|NC|+ 2. The number of removed constraints is
M| − 2)+ 1 − |NM| − 2
dD|Ld|2+|NC||Ld|+ 1 X
dD|Ld|2+|NC||Ld|−|Ld|− |NC|+ 1.
Consequently, the number of removed constraints is of order Θ(PdD|Ld|2+|NC||Ld|).
To summarize, the IMIP formulation reduces the number of variables and constraints substantially
and simplifies a multi-commodity model to a single-commodity model. As a consequence, the required
memory and solution time are significantly reduced.
5.3 Scenario addition
It is necessary to solve the second-stage problem (MRP) from Section 4 many times for SA. Therefore,
we use the improvements from Section 5.2 for the MRP and introduce the IMRP. Although the MRP
can be solved in polynomial time, this change yields a substantial reduction in solution time. For larger
instances (100 facilities, 64 scenarios), the solution time decreases from multiple seconds per scenario, to
solving all scenarios in approximately one second. Furthermore, similar to Section 5.1, the capacity qn,
can be replaced by ˆ
These changes together give us the following improved maintenance routing problem formulation:
IMRPd(y) = min X
s.t. X
z(a) = X
F\ {S,T },(ν)
z(a) = md
llLd, a δd
The Greek symbols next to the constraints are the corresponding dual variables, which are used for
the Benders decomposition algorithm described in online Appendix B.
SA uses the fact that for the RMLRP, only the opened facilities and the worst-case scenario are
relevant for the solution. Consequently, SA cannot be used for the SMLRP where all scenarios contribute
to the objective function. Let ibe an iteration counter that starts at 0 and Didenote the scenario set
used in iteration i. The set D0contains one randomly chosen scenario dD. Because the scenarios
differ in the total number of maintenance visits, the following feasibility constraint is added to the
where ˆqn= min{qn,maxdDPlLdmd
With these preliminaries, the SA algorithm consists of the following steps.
1. Compute the solution to the RMLRP-IMIP with Dreplaced by Di, and add Constraint (34) to
the formulation. Denote the optimal objective of this problem as LBiand the solution of iteration
ias yi.
2. Set UBi:=cTyi+ maxdDIMRPd(yi) and let di
max := arg max
IMRPd(yi). If di
max Di, then
UBi= LBi. Stop and return yias the optimal solution and UBias the optimal objective value.
Otherwise (di
max /Di) the algorithm proceeds to the next step.
3. Let Di+1 :={di
max} ∪ Di. Update i:=i+ 1 and go back to Step 1.
These steps give a very diverse set of scenarios as the most detrimental solution for the incumbent
solution is added in each iteration. We observe that the algorithm generally converges quickly with only
a small number of scenarios in Di.
6 Computational experiments
In this section, we report computational experiments on randomly generated instances to test the com-
putational performance of our algorithms. We are particularly interested in the size of instances that
can be solved by the IMIP and SA. The results of our accelerated Benders decomposition algorithm can
be found in the online Appendix B. Although we generate instances randomly, the fixed and random
parameters are based on those found in practice to create reasonable instances. All experiments are
programmed in Java with the CPLEX library version 12.6.3, and run on a laptop with an Intel Core
i7-4710MQ Quad Core 2.5 GHz processor with 8 GB of RAM. All mentioned solution times include
the time necessary to build the model and CPLEX standard settings are used (e.g. for preprocessing,
branching, accuracy tolerance etc.).
6.1 Test instance generation
An instance of the SMLRP or the RMLRP requires a railway network, several line plans on this railway
network, parameters such as the sizes of facilities, interchange budgets etc. Subsection 6.1.1 explains
how we create physical railway networks and possible assignments of end stations within the line plan
(basic line plan). Then in Section 6.1.2 we explain how we generate many line planning scenarios for a
physical railway network with basic line plans. Finally, Section 6.1.3 explains how interchange budgets
and candidate locations for an instance are generated.
6.1.1 Physical railway network and basic line plan generation
We create random instances by first generating graphs of physical railway networks in the Cartesian
plane. We do this by manually generating five archetypical graphs on a one by one plane with 5, 10,
25, 50, and 100 nodes, respectively, as shown in Figure 3. Each node is a potential end station and
candidate facility location, while each edge is a set of rails between end stations. For each archetypical
railway network, we also manually design “basic line plan” scenarios. A basic line plan consists only
of origin-destination pairs and the route traveled between them. We also determine for each basic line
whether the corresponding rolling stock type will be regional or intercity. Note, however, that there
may be several regional and intercity rolling stock types, and that the actual rolling stock type will be
assigned in Section 6.1.2.
We create physical railway networks by perturbing and scaling these archetypical networks. We
perturb these archetypical graphs by relocating each node uniformly at random within a square around
that node. The square is centered around the original node except at the boundaries of the 1 ×1 plane
where the square is relocated to fit within the 1 ×1 plane. The dimension of this square decreases with
the number of nodes in the network. Next, the obtained graph is scaled to either a (1) 200 ×400 km,
(2) 750 ×1000 km, or (3) 3000 ×3000 km rectangle to model different railway network sizes. The length
of railway tracks (edges) is then determined by multiplying the Euclidean length by a number drawn
uniformly between 1.0 and 1.2. Note that at this point, the length in kilometers of each basic line is
fixed. A graph thus obtained is denoted GP= (NP, EP).
0 1
(a) 5 nodes
0 1
(b) 10 nodes
0 1
(c) 25 nodes
0 1
(d) 50 nodes
(e) 100 nodes
Figure 3: The five archetypical graphs.
6.1.2 Line plan generation
Recall that a train line consists of a rolling stock type, the maintenance frequency per year and the
deadheading cost to each facility. Before we can generate a line plan, we first need to generate different
types of rolling stock. We first generate the number of different regional and intercity rolling stock
types. This number is generated uniformly between 1 and the number of edges |NP|divided by 8. The
deadheading cost per kilometer for each rolling stock type is calculated by generating the components
of which it consists: the fuel, driver salary, unavailability costs, and fees. Each component is generated
uniformly at random between the lower and upper bounds that are seen in practice for regional and
intercity trains. Furthermore, the calculation of the number of maintenance visits for a line later on
requires the annual number of maintenance visits of the rolling stock type on that line. The annual
number of maintenance visits is generated uniformly at random between 1 and 12 for each rolling stock
type; which are bounds that we observed at the NS.
Next, we generate line plan scenarios until we have reached the fixed number of scenarios for the
instance. To generate a line plan scenario, we first take a random line plan possibility from the fixed
basic line plan set. For each line of the chosen basic line plan, we generate the rolling stock type and
maintenance visit frequency. The line is either an intercity or regional train line, and the rolling stock
type is generated from the corresponding set. The rolling stock type is the same as that of a connecting
line of the same line type with a probability of 0.5 for each line. When there are no defined lines yet,
or when the rolling stock type will not be the same as an already defined connecting line, we choose
uniformly at random from the set with rolling stock types for the basic rolling stock type of the line.
The annual maintenance frequency is the average number of train units on that line times the yearly
maintenance frequency of the rolling stock type used for that line. An estimation for the average number
of train units for a train line is made based on its length in kilometers, the average speed of the trains
and the hourly train frequency of the line, which is generated uniformly at random between 1 and 8 (the
lowest and highest hourly train frequencies considered by the NS). The deadheading cost for a line to a
facility is the deadheading cost per kilometer of the rolling stock type times the number of kilometers
for the shortest path in GPfrom one of the end stations nodes to the facility node. For example, when
one of the end stations is the candidate facility, the shortest path is 0 kilometers and the cost is 0.
6.1.3 Interchange budgets and candidate facilities
All interchange coordination costs between lines are set at 10 euros. We define U(a, b) as a uniform
random variable on (a, b). Furthermore, we define the total annual number of maintenance visits for end
station eas Cd
l, where Ld
econtains all lines that have eas an end station.
The interchange budget for end station ein scenario dis randomly generated as
0 with a probability of 0.05,
ewith a probability of 0.2,
ewith a probability of 0.65,
with a probability of 0.1.
Note that U(1,3)Cd
eis only a restriction when there are many interchanges originating from non-
connecting lines. We define Md:=PlLdmd
land randomly generate Gdas
U(0,1)Mdwith a probability of 0.25,
U(1,3)Mdwith a probability of 0.65,
with a probability of 0.1.
The annual facility costs are generated uniformly at random between 1/5th and 5 times the esti-
mated average annual facility cost for the NS. We define Mdmax := maxdDPlLdmd
land generate the
maintenance facility capacities as
U(0,0.5)Mdmax with a probability of 0.1,
U(0.5,1)Mdmax with a probability of 0.4,
with a probability of 0.5.
Consequently, on average half of the maintenance facilities have no capacity restriction. For the
SMLRP, we also generate the weights wd. These weights are generated uniformly at random between 0
and 1, followed by scaling the weights such that they sum up to 1.
6.2 Computational results for the IMIP
The test instance generation method from Section 6.1 can generate instances with any number of can-
didate facilities and line planning scenarios. For our computational tests, we created a full factorial of
instances where the number of candidate facilities takes values in {5,10,25,50,100}and the number
of line planning scenarios takes values of 2ifor i∈ {0,1,...,15}. For each combination of number of
facilities and line planning scenarios, 15 instances are generated. These 15 instances are split equally
between the 3 archetypical railway networks in Section 6.1.1.
For each number of candidate facilities, we try to solve each instance within an hour. When at least
80% (13 out of 15) of the instances can be solved, we move to the set of instances where the number
of scenarios is doubled. When an instance cannot be solved within an hour, a fail is registered and a
solution time of 3600 seconds is used for the average time calculations. The average time in seconds
for each set for the SMLRP and RMLRP can be found in Table 2. The top row shows the number of
candidate facilities per set and the first column the number of scenarios.
Scenarios 5 10 25 50 100 5 10 25 50 100
1<0.1<0.1 0.1 1.1 19.4 <0.1<0.1 0.1 0.7 13.4
2<0.1<0.1 0.2 1.7 110 <0.1<0.1 0.2 1.8 103.4
4<0.1<0.1 0.3 8.6 94.2 <0.1 0.1 0.6 9.1 175.5
8<0.1 0.1 0.6 20.6 202.6 0.1 0.1 2.2 35.2 874.3
16 0.1 0.2 2.5 33.2 956.6 0.2 0.2 2.8 67.6 fails
32 0.1 0.3 5.7 112.5 fails 0.2 0.3 5.3 231.0
64 0.2 0.5 10.4 296.7 0.3 0.6 13.5 715.4
128 0.4 1.4 37.6 785.8 0.5 1.7 71.4 1259.3
256 1.1 3.0 112.9 fails 1.6 4.2 140.1 fails
512 2.9 11.8 507.4 5.0 15.2 452
1024 10.1 36.0 1076.8 16.3 44.9 1259.8
2048 31.1 154.8 fails 54.7 178.5 fails
4096 126.8 653.4 214.4 531.2
8192 556.7 fails 733.2 1978.1
16384 2428.3 fails fails
32768 fails
Table 2: Solution time in seconds for the SMLRP-IMIP and RMLRP-IMIP, while varying the number
of scenarios and facilities.
For up to 50 facilities, the IMIP solved with CPLEX works well. Note that these instances are
already large. An instance with 25 facilities and 1024 scenarios has on average 35k (35765.5) nodes and
a million (1007822.6) edges in the maintenance location routing flow graph for the RMLRP. Furthermore,
the CPLEX model has a million (1007848.6) columns and 100k (100123.1) rows on average. For 100
candidate facilities, we can only solve the instances with a limited number of scenarios within an hour.
For these instances, we extended the solution time to 48 hours (2880 minutes), to explore the boundary
of what is computationally feasible. Because of time considerations, we limit the instances of the sets to
the first six instances, two instances from each size. These results are shown in Table 3, where RAM is
used for instance sets where an out-of-memory error occurred.
Instances with up to 64 scenarios can generally be solved. Furthermore, the SMLRP-IMIP seems
to be computationally faster than the RMLRP-IMIP. A possible explanation for this are Constraints
(33) from Section 5.2, which are only used for the RMLRP-IMIP. These constraints create degeneracy,
because with them only one scenario contributes to the transportation cost, giving many solutions with
the same objective value.
Furthermore, we note that the IMIP (solved with CPLEX standard branch-and-cut algorithm) uses
Scenarios Min Average Max Failed Min Average Max Failed
16 0.4 12.1 56.2 0 17.4 105.6 252.6 0
32 1.5 72.3 331.5 0 22.8 429.6 791.4 0
64 4.6 209.2 892.7 0 123.0 631.8 2541.0 0
128 6.6 595.9 >2880.0 2 168.7 2337.7 >2880.0 4
256 18.5 774.5 >2880.0 1 RAM RAM RAM 6
Table 3: Results for instances with 100 facilities.
many cuts and few nodes in the branch and bound tree. Some of the instances can be solved without
branching. For example, the average number of nodes for the SMLRP with 50 candidate facilities is
72, while it uses 4226 cuts on average. The number of nodes increases with the number of candidate
facilities, while the number of cuts increases by both the number of facilities and scenarios. CPLEX
mainly uses flow and MIP rounding cuts, followed by implied bound cuts, and to a lesser extent clique,
Gomory, zero-half, and lift and project cuts.
6.3 Computational results for SA
We compare SA with the IMIP for the instances with 25, 50, and 100 candidate facilities from the
randomly generated instance set. The instances with 5 and 10 facilities are excluded because the IMIP
can already solve instances with up to 8192 scenarios within an hour. Again more than 80% (13 or more
out of 15) of the instances have to be solved within an hour to go to the next set. The average time
in seconds for each set for the RMLRP-IMIP and SA can be found in Table 4; the top row shows the
number of candidate facilities per set and the first column the number of scenarios.
For the instances with 25 and 50 candidate facilities, it can be seen in Table 4 that the SA algorithm
can solve instances with more scenarios within the hour. For the instances with 25 candidate facilities,
we stopped the test after 32768 scenarios, although they can still easily be solved within the hour. There
is no difference between SA and IMIP for the instances with 100 facilities. This can be explained by the
fact that we can only solve instances with a limited number of scenarios for instances with 100 facilities.
Consequently, an SA iteration with fewer scenarios added already takes a considerable amount of time.
When we extend the time from 1 hour to 48 hours (we limit the sets to the first six instances, as
explained in Section 6.2), the SA algorithm performs better in both the best and average case, but its
worst-case performance is as expected worse. We show these results in Table 5.
The number of iterations (is equal to the number of added scenarios) that the SA algorithm uses and
the time in seconds are shown in Table 6. The number of iterations initially grows when the number of
scenarios increases, but stabilizes at some point. The maximum number of iterations is low compared
Scenarios 25 50 100 25 50 100
1 0.1 0.7 13.4 0.1 0.7 13.5
2 0.2 1.8 103.4 0.3 2.4 129.1
4 0.6 9.1 175.5 1.2 14.1 220.9
8 2.2 35.2 874.3 2.7 42.2 719.7
16 2.8 67.6 fails 4.4 76.4 fails
32 5.3 231.0 5.1 328.7
64 13.5 715.4 5.3 357.4
128 71.4 1259.3 8.4 267.9
256 140.1 fails 11.9 485.3
512 452 12.8 528.9
1024 1259.8 19.1 851.9
2048 fails 40.5 503.6
4096 54.9 1095.0
8192 75.3 1073.2
16384 144.3 fails
32768 288.8
Table 4: Solution time in seconds for the IMIP and SA, while varying the number of scenarios and
Scenarios Min Average Max Failed Min Average Max Failed
16 17.4 105.6 252.6 0 1.2 74.4 198.0 0
32 22.8 429.6 791.4 0 5.4 560.4 2247.0 0
64 123.0 631.8 2541.0 0 8.4 511.2 >2880.0 1
128 168.7 2337.7 >2880.0 4 2.4 807.0 >2880.0 2
256 RAM RAM RAM 6 66.0 1103.7 >2880.0 2
Table 5: Results for instances with 100 facilities.
with the total number of scenarios |D|of the instances. The maximum number of scenarios for 25
facilities is 32768, while for the worst-case, the scenario set only contains 20 scenarios. When we increase
the number of candidate facilities to 50, the maximum number of scenarios is 8192, while the maximum
number of iterations is 24.
25 50 100
Scenarios Iterations Time Iterations Time Iterations Time
1 1.0 0.1 1.0 0.7 1 13.5
2 1.9 0.3 1.9 2.4 2 129.1
4 2.9 1.2 3.5 14.1 3.4 220.9
8 4.6 2.7 5.3 42.2 4.9 719.7
16 5.9 4.4 6.9 76.4
32 7.3 5.1 9.3 328.7
64 7.8 5.3 11.7 357.4
128 7.1 8.4 11.2 267.9
256 7.5 11.9 11.8 485.3
512 7.9 12.8 10.9 528.9
1024 7.9 19.1 10.9 851.9
2048 8.1 40.5 11.6 503.6
4096 8.3 54.9 12.5 1095.0
8192 7.0 75.3 12.1 1073.2
16384 6.7 144.3
32768 6.7 288.8
max 21 983.1 24 >3600 8 >3600
Table 6: The average and maximum number of iterations needed for SA.
7 Case study: NS
In this section, we perform a case study for the NS. We use a green field approach, where existing facilities
of the NS are kept out of scope. We first describe the NS instances and continue with our experiments.
In our experiments, we investigate the benefit of including interchanges, the expected value of perfect
information, the value of the stochastic or robust solution, and provide a robust solution for the case
7.1 NS instances
The NS instances each have 59 end stations and 55 candidate facilities. Four end stations (Utrecht,
Breukelen, Amsterdam, and Schiphol) have been excluded, because a maintenance facility cannot be
built at these locations. The facility costs are an estimation of the average annual cost of land, the
necessary infrastructure, and the maintenance facility itself including all side buildings. Furthermore, we
altered the facility cost based on which province the end station is located. The cost is either decreased
or increased dependent on the province’s average land price. The capacity restrictions of the facilities
unfortunately only become clear in a later stage when a chosen location is investigated in detail. Because
also no rough estimations could be given by the experts of the NS, we assume unlimited capacities for the
facilities such that managerial insights can still be provided. (We conduct a sensitivity analysis on this
assumption later and find that facility capacities are not an important issue.) Furthermore, we assume
unlimited interchange capacities for all end stations except Utrecht, Amsterdam, and Schiphol, where
interchanges are not possible. The network interchange budget has a different value for each experiment,
and will be described in detail for each experiment.
We use four basic line plans: the current situation (2015), an estimation of 2018, and two possibilities
for approximately 2025. These basic line plans contain all the lines (97, 97, 99, and 100 lines) the rolling
stock serving the line and an estimate of the number of annual maintenance visits per line. The future
scenarios, are based on the plan “Beter en Meer” (Prorail and NS, 2014), where the NS intends to
increase the frequency of the lines in the Randstad or larger Randstad. The Randstad is a megalopolis
in the Netherlands consisting of the four largest cities and their surrounding areas. The larger Randstad
also includes the cities Arnhem, ’s-Hertogenbosch, and Eindhoven.
The rolling stock types consist of all current rolling stock types and the future rolling stock types
(FLIRT, SNG, and ICNG). For each rolling stock type, a rough estimation is made for the deadheading
cost per kilometer. This estimation is based on many components such as driver cost, energy consump-
tion, an estimation on the average deadheading velocity, and availability costs. The availability costs
are based on the life cycle cost of the rolling stock and the passenger dissatisfaction cost due to the
unavailability of the train. The cost of the interchanges is set at 10 euro per interchange.
Scenarios are made by picking a basic line plan, and altering the number of maintenance visits and
rolling stock type. For each line of the line plan, the number of maintenance visits is from a triangular
distribution, where the number of maintenance visits of the basic line plan is the mode of this distribution.
We assume that the number of maintenance visits can decrease with 32.5% and increase with 75%, due
to uncertainty in the annual number of maintenance visits of the train unit and the number of passengers
using a certain line. A maximum of 20% of the rolling stock types of the lines can be swapped with
each other. To do that, we generate for both regional and intercity trains a separated integer list, with
its rolling stock number in the list for every maintenance visit it makes. These lists and the lines are
shuffled. Then we go through all train lines and with a 10% probability, we change the rolling stock
type to the first different rolling stock type of the list, all previous items of the list are removed. The
maintenance frequency of the line is divided by the maintenance frequency of the current rolling stock
type and multiplied by those of the new rolling stock type. We stop when there are no more lines or the
limit of 20% has been reached.
The information that is available about the expected duration for each scenario is too limited to make
any reasonable estimation. As a consequence, to demonstrate the SMLRP and to be able to compare
a weighted solution (SMLRP) with a worst-case solution (RMLRP), we generate the weights randomly.
We generate the weights wduniformly at random between 0 and 1, followed by scaling the weights such
that they sum up to 1.
7.2 Experiments
In this subsection, we compare the SMLRP and RMLRP and we provide managerial insights. We test the
influence of including maintenance routing by varying the network interchange budget. We continue with
the expected value of perfect information and the stochastic/robust solution and we provide a solution
to the case study. In addition, we test the influence of the number of scenarios in online Appendix C.
Before we can test the influence of the network interchange budget, we need to know how many
scenarios those instances should have. Consequently, we start with some initial computational exper-
iments. We do those experiments with sets of 10 instances, increasing the number of scenarios by a
factor 2 for each set. We fixed the network interchange budget for every scenario at 0.75M, where
M=PlL2015 m2015
l, which is the number of maintenance visits in the current (2015) scenario. For the
RMLRP instances, the most often opened facilities are Eindhoven and Almere Oostvaarders (less than
32 scenarios), Eindhoven and Hilversum (64 up to 4096 scenarios) and ’s-Hertogenbosch and Hoofddorp
for 8192 or more scenarios. However, for each number of scenarios, there are always other solutions.
We evaluated the mentioned three solutions, with 10 instances that have 65536 scenarios, the difference
between the optimal solution values is at most 2%. This small difference explains why we obtain different
solutions, even if we include many scenarios. We show these solutions in Figure 4, within the first black
circle are the stations in the north Randstad (Hilversum, Hoofddorp, and Almere Oostvaarders) and the
stations in the south of the larger Randstad (Eindhoven, ’s-Hertogenbosch) are in the second black circle.
From now on, we use instances with 8192 scenarios that can be solved in approximately 10 minutes.
When we relax the infinite capacity assumption and vary the capacities of the facilities, the solution
remains the same when the capacity of the facilities are larger than 1.20M. Between 0.89Mand 1.19M
three facilities are opened for some of the instances and for 0.88Mthree facilities are opened for all
instances. Evidently even more facilities will be opened when we decrease the capacity of facilities
For the SMLRP instances, the average cost is always the same independent of the number of scenarios,
and Eindhoven and Almere Oostvaarders are opened the most often (this follows from the weak law of
large numbers). From 8 scenarios onwards, Eindhoven and Almere Oostvaarders are opened for all 10
instances. However, increasing the number of scenarios still decreases the standard deviation of the
optimal objective values. Owing to time and stability reasons (see online Appendix C), we use instances
with 16 scenarios from now on. These instances can be solved in approximately 20 minutes. When
we drop the infinite capacity assumption and vary the capacities of the facilities, the solution remains
Eindhoven and Almere Oostvaarders for all 10 instances when the capacity of all facilities is above 0.85M.
Between 0.78Mand 0.85MGroningen or Maastricht is opened as third facility for some of the instances
and for 0.77MGroningen or Maastricht is opened as third facility for all instances. Further decreasing
the capacity obviously results in more facilities being opened.
Figure 4: Railway map of the Netherlands with all stations. The larger black circles are the best
locations to open a maintenance facility. The small circles are the larger intercity stations and the tiny
circles are the smaller regional stations. This picture is adapted from
7.2.1 The benefit of the network interchange budget
We make 10 instances with 16 (SMLRP) or 8192 (RMLRP) scenarios according to the plan described in
Section 7.1. For each of these instances, the budget is Gd= 0,0.25M, 0.5M, 0.75M , M, 2M, and for
all scenarios. Note that a budget of Gd= 0 for all scenarios corresponds to a model where maintenance
routing is not possible. Furthermore, we include 10 instances where the station interchange restrictions
geare removed, while also having an infinite network interchange budget. These instances demonstrate
the maximum gain that can be achieved by improving the station interchange budgets.
Table 7 shows the influence of the annual network interchange for the SMLRP. Here #sols denotes
the number of different facility location decisions among the 10 problem instances. The next column
gives the facility location decision that occurs most often and the last column shows the average costs
(optimal objective value) in million euros per year. Two of the three earlier mentioned solutions return in
Table 7. Starting from a network interchange budget of 0.50M, the solution is almost always Eindhoven
and Almere Oostvaarders. The interchange budget is not binding for GdMdDand including
maintenance routing can decreases the cost by a total of 25.4%. Removing the interchange station budget
decreases the cost with another 13.6%.
Budget #sols Opened facilities Cost (millions)
0 2 ’s-Hertogenbosch and Hoofddorp (6/10) 5.9
0.25M3 Eindhoven and Hilversum (5/10) 5.2
0.50M2 Eindhoven and Almere Oostvaarders (9/10) 4.7
0.75M1 Eindhoven and Almere Oostvaarders (10/10) 4.5
M1 Eindhoven and Almere Oostvaarders (10/10) 4.4
2M1 Eindhoven and Almere Oostvaarders (10/10) 4.4
1 Eindhoven and Almere Oostvaarders (10/10) 4.4
No gs3 Eindhoven and Almere Oostvaarders (8/10) 3.8
Table 7: Network interchange budget results for the SMLRP.
Table 8 shows the influence of the annual network interchange budget for the RMLRP. Again, all
solutions consist of one maintenance facility in the south of the large Randstad and one in the north.
’s-Hertogenbosch and Hoofddorp may be preferable for instances with a low network interchange budget,
while Eindhoven and Hilversum may be preferable for instances with a high network interchange budget.
In almost all cases, two facilities are opened, one in the north of the Randstad and one at the south
of the large Randstad. The network interchange budget is not binding for Gd0.75MdDand
increasing the budget decreases the cost by 15.7%. When we remove the station interchange restrictions,
we can decrease the cost by another 10.2%. Furthermore, the RMLRP solution values are higher than
the solution values of the SMLRP, as expected.
7.2.2 Expected value of perfect information and stochastic/robust solution
In the initial experiments, the combination of two facilities – one in the north and one in the south of
the randstad in respectively circle 1 and 2 of Figure 4 – provide a good solution. The optimality gaps of
these solutions are usually around 2%. Similar solutions are found in Section 7.2.1 and online Appendix
C independent of the network interchange budget or the number of scenarios.
We will now look at the expected value of perfect information, and the value of the stochastic or robust
solution. Both measures are common measures within the stochastic programming literature (Birge and
Louveaux, 1997). The expected value of perfect information is the value of the SMLRP/RMLRP minus
Budget #sols Opened facilities Cost (millions)
0 2 ’s-Hertogenbosch and Hoofddorp (8/10) 7.0
0.25M4 ’s-Hertogenbosch and Hoofddorp (6/10) 6.3
0.50M4 Eindhoven and Hilversum (5/10) 6.0
0.75M3 ’s-Hertogenbosch and Hoofddorp (6/10) 5.9
M7 Eindhoven and Hilversum (4/10) 5.9
2M6 Eindhoven and Hilversum (4/10) 5.9
6 Eindhoven and Hilversum (4/10) 5.9
No gs4 ’s-Hertogenbosch and The Hague (8/10) 5.3
Table 8: Network interchange budget results for the RMLRP.
the wait and see solution. It is a measure that assesses how valuable perfect information about the
future is. The value of the stochastic and robust solution is the improvement of adding future scenarios
compared with only using the current situation. The expected value for the current case can be evaluated
by solving the second-stage problem for all scenarios while using the opened facilities of the current
situation. The value of the robust/stochastic solution is now the expected value of the current case
minus the SMLRP/RMLRP optimal objective value.
When we solve the current case scenario with a network interchange budget of 0.75M, the optimal
solution is to open only Almere Oostvaarders with an optimal objective of 3.3 million per year. However,
when we evaluate this solution by solving the MRP for an instance with 65536 scenarios, the objective
becomes 5.4 million euros per year for the average case objective and 7.7 million euros per year for the
absolute robust objective. As a consequence, the expected value of the stochastic/robust solution is
respectively 9.3% and 18.2%. This means that neglecting uncertainty about future line plans costs on
the order of 10%. The optimal objective for the wait and see solution (8192 scenarios), a solution for
which the best facilities are opened for each scenario separately, is 4.6 million for the SMLRP and 6.1
million for the RMLRP. This gives an expected value of perfect information of 2.1% for the SMLRP and
3.2% for the RMLRP.
We expect that the expected value of perfect information is low, because the combination of opening
a maintenance facility in the north and south Randstad (see Figure 4) is a good solution for all scenarios.
When the solution is not optimal, it is generally close to optimal. For the NS this implies three important
1. It is important to model uncertainty about the future to make good decisions. Ignoring this
uncertainly increases cost by around 10%.
2. The risk attitude of the decision maker hardly affects the best decisions, although it does affect
the objective value. In particular, any solution with a facility in circles 1 and 2 of Figure 4 is close
to optimal.
3. Investing in more accurate forecasts of future line plans (expected value of perfect information) has
very limited benefits.
8 Conclusion
We have formulated two novel models for the maintenance location routing problem and used different
algorithms to solve them. Our IMIP formulation and the SA method work quite well and can solve
industrial size instances. The SA method performs computationally better than the IMIP for the RMLRP
and requires less memory as it only has to consider a subset of the scenarios. We expect that the
success of our IMIP formulation can be explained by the fact that it is a very efficient formulation
that simplifies a multi-commodity problem to a single-commodity problem and reduces the memory
requirements by decreasing the number of variables and constraints by Θ(PdD|Ld|2|NC|+|Ld||Ad
and Θ(PdD|Ld|2+|NC||Ld|), respectively. Furthermore, the solution speed of MIP solvers have
increased enormously in recent years, while the current 64-bit computer architectures has increased the
maximum RAM usage from 232 different values (3-4 GB) to 264 values (18 EB, 1 EB = 109GB).
The case study at the NS indicates that a robust solution exists that gives good values for both the
SMLRP as RMLRP. This solution consists of opening two facilities one in the north and one in the
south of the larger randstad (circles 1 and 2 of Figure 4). Furthermore, cost savings up to 25.4% for the
SMLRP and up to 15.7% for the RMLRP can be made by improving the network interchange budget
and including scenarios saves 9.3% for the SMLRP and 18.2% for the RMLRP relative to only using the
current line plan scenario.
An interesting future direction for research is to make the price of the facilities dependent on the
provided capacity. In such a case, solutions with (approximately) the same facility costs, can consist of
many small facilities or a few larger facilities. As it is costly to have sufficient capacity for all possible
scenarios, even unlikely ones, we can also allow some recoverability. This recoverability can consist of
building additional facilities, selling facilities, or upgrading them to a higher capacity.
The authors thank the Associate Editor and the three anonymous referees for suggestions that improved
the paper. In addition the authors would like to thank the people from the Netherlands Railways for
the many discussions. We want to especially acknowledge Mei Li Kho, for gathering the current case
(2015) data for the case study, and Bob Huisman and Geert-Jan van Houtum for giving valuable input.
This study was funded by Nedtrain. Joachim Arts gratefully acknowledges funding through veni grant
number 451-16-025.
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... Timetable planners need accurate rolling stock and track data, since the feasibility and future performance of their plans depends significantly on the performance and precision of the models and information used for their design [3]. However, planners usually face a high level of uncertainty at this early stage, hindering their work and jeopardising the feasibility of the designed projects at later stages [4]. Moreover, feasible train paths have to be determined over lines, networks and stations. ...
Rail operators worldwide are seeing an increase in demand for services, driven by expanding global trade and the use of export commodities. Punctual service in a train stopped on the railroad is extremely important since the delay in service significantly impacts performance, asset utilization, and railroad productivity. The present work addresses the development and the solution of an optimization model for locating railway service teams that serve locomotives that need emergency maintenance on a Brazilian railway. Additionally, a hypercube model was solved to estimate several performance measures that assist in decision‐making. From the result analysis of different scenarios, it was found that the optimized maintenance teams' location provides superior results to the current ones considering the same resources. Also, it was possible to increase the coverage with double attendance in the studied system, monitoring different indicators such as attendance teams' average system travel time and workload.
Full-text available
Indian metro system is comprised of many functional units for enabling smooth run of trains namely tracks, rolling stock, signaling and communication, operations and control center, projects, and design. Among those functional units, Rolling Stock (RS) is an integral part of any rapid transit system and also the most critical space if proper maintenance measures are neglected. The rolling stock department comprises interdisciplinary teams working together to ensure efficient delivery of trains on a service run. The trainsets of Metro Rail Networks are often subjected to both periodic and corrective maintenance based on service requirements. The maintenance schedule of the trainsets is monitored through a wireless communication mode. The train operator is responsible for alerting the nodal person regarding subsequent correspondence in the event of any emergency maintenance necessity. Therefore, this paper concentrates on the development of an IoT-based automatized maintenance prioritizing platform based on the incorrect operational sequence number that pops up in the operator’s cabin. A mathematical model is synchronized with the alert triggering signal from the field to categorize hierarchical decision-making on preventative and corrective maintenance. Simultaneously, a Genetic Algorithm is implemented to analyze the adopted combinations of maintenance say M1, M2, and M3 to identify the model that produced precise results. The test results reveal that the M3 model, which includes both corrective and preventative maintenance exhibits higher efficiency with a probability of 0.92%–0.98%. In addition, the combined maintenance prioritization system M3 offers the quickest analyzing time in the cloud computing platform (0.18 s) and the highest transaction performance on real-time datasets.
At high-speed railways, trains cover services during the day and are required to undergo maintenance at depots each night. A low-quality train schedule in the depot may result in delays in the availability of trains during the day which influences the reliability of the train timetables. Accordingly, this study examines the problem of train shunting with service scheduling in a depot where daily maintenance, cleaning operation, and safety operational requirements are considered. To cope with this complex problem, we first construct a two-layer time–space network in which each layer can only be used by trains traveling in the same direction. We then formulate the considered problem as a minimum-cost multi-commodity network flow model with incompatible arc sets and operational constraints. To solve the network flow problem, we present a Lagrangian relaxation heuristic. Finally, several computational experiments with practical data based on the Hefei–Nan depot and randomly generated data on trains’ arrival and departure times at the depot are conducted to confirm the effectiveness of our model and the efficiency of the proposed heuristics.
This paper develops a multi-objective optimization methodology to identify potential location of a future intercity passenger rail maintenance and layover facility. Quantifiable factors which are critical in location decision making are considered in the proposed methodology. These factors consist of vulnerability to natural disasters, proximity to an existing rail line and station and to an already existing facility. These factors often drive the initial choice of a maintenance/layover facility. An application example is presented with five future potential site locations in California. These sites are tentative based on interviews conducted with the engineers and staff at two existing rail maintenance and layover facilities in California—the Oakland Operations and Maintenance Facility (OMF) and the Stockton Altamont Corridor Express (ACE) Maintenance and Layover Facility. Based on the methodology outlined in this research, it was found that one of the sites located close to the Union Pacific rail line could be the most preferred site for a future location of a maintenance and a layover facility. This site satisfied all factors considered in this research, namely minimum risk level associated with seismicity and other natural hazards, minimum distance from the existing facilities, minimum distance traveled to the nearest station, and minimum distance to the closest point on a rail line. The methodology proposed is particularly relevant for developing countries like India—which has a vast network of intercity passenger rail network with high fleet operational hours and frequencies between certain origins and destinations and the trains need regular maintenance.
The adoption of electric vehicles is seen as a potential panacea to many of the Indian cities suffering from vehicular pollution. However, if electric vehicles depend upon energy generated from non-renewable sources such as coal, there could be a scenario where emissions at thermal power plants may rise. The study assesses the impact on coal power plants due to shift from conventional to electric vehicles in India. After assessing the Indian power generation scenario, the forecasted load on coal power plant, due to adoption of electric vehicle, has been estimated to determine the future pollutant emissions, after considering the losses occurring during the electricity production process. The study develops emission indices that show electric vehicles would indirectly emit more CO2, NOX, and SO2 at the coal power plant; however, the CO emissions are likely to be lesser when compared to conventional vehicles.KeywordsICEVElectric vehiclesCoal power plant emissionsAir pollution
Product upgradations are common, as it is practical and economical in many cases, compared to complete replacement. Proper planning and scheduling using quantitative techniques can be helpful in the case of large-scale upgradations involving movable assets, like rolling stock. There is a possibility of doing the upgradation of the asset in multiple locations, and there are operational constraints involved in executing the process. In this paper, we propose integer programming models to optimize the execution of these types of upgrades. A case involving retrofitting of bio-toilets in Indian Railways is used as an example. Computations are done for the allocation of movable assets for retrofit in a division of Southern Railways. The central unit decides the number of items to be retrofitted in each facility and facilities to be upgraded based on the output of the optimization model. Subsequently, a model for scheduling retrofitting at a facility is also discussed. There are other problems in the industry involving other movable assets like automobile and naval vessels, where extension of these models can be useful.
Due to increasing railway use, the capacity at railway yards and maintenance locations is becoming limiting to accommodate existing rolling stock. To reduce capacity issues at maintenance locations during nighttime, railway undertakings consider performing more daytime maintenance, but the choice at which locations personnel needs to be stationed for daytime maintenance is not straightforward. Among other things, it depends on the planned rolling stock circulation and the maintenance activities that need to be performed. This paper presents the Maintenance Location Choice Problem (MLCP) and provides a Mixed Integer Linear Programming model for this problem. The model demonstrates that for a representative rolling stock circulation from The Netherlands Railways a substantial amount of maintenance activities can be performed during daytime. Also, it is shown that the location choice delivered by the model is robust under various time horizons and rolling stock circulations. Moreover, the running time for optimizing the model is considered acceptable for planning purposes.
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Thousands of locomotives run in the North America railroad network and each of them must receive periodic maintenance service; meanwhile, the locomotives may break down during shipment operations and demand repair. Maintenance shops are characterized by the types of locomotives they can handle and the level of service they can provide. This article formulates a mixed-integer program model to optimize location and capacity of locomotive maintenance shops. The objective is to minimize the sum of the costs for building new (or shutting down existing) maintenance shops, the costs for capacity expansion (or reduction), and the costs for shipping broken locomotives to a suitable shop. A Lagrangian-relaxation-based heuristic algorithm is proposed to solve the shop location and capacity planning problem. The model and solution techniques are applied to a full-scale real-world case study, while the computational results show that the current maintenance shops are overly built and recommend that two maintenance shops should be closed and an amount of capacities should be reduced from the existing shops. A series of numerical sensitivity analyses are also conducted to draw managerial insights.
Sudden cardiac arrest is a significant public health concern. Successful treatment of cardiac arrest is extremely time sensitive, and use of an automated external defibrillator (AED) where possible significantly increases the probability of survival. Placement of AEDs in public locations can improve survival by enabling bystanders to treat victims of cardiac arrest prior to the arrival of emergency medical responders, thus shortening the time between collapse and treatment. However, since the exact locations of future cardiac arrests cannot be known a priori, AEDs must be placed strategically in public locations to ensure their accessibility in the event of an out-of-hospital cardiac arrest emergency. In this paper, we propose a data-driven optimization model for deploying AEDs in public spaces while accounting for uncertainty in future cardiac arrest locations. Our approach involves discretizing a continuous service area into a large set of scenarios, where the probability of cardiac arrest at each location is itself uncertain. We model uncertainty in the spatial risk of cardiac arrest using a polyhedral uncertainty set that we calibrate using historical cardiac arrest data. We propose a solution technique based on row-and-column generation that exploits the structure of the uncertainty set, allowing the algorithm to scale gracefully with the total number of scenarios. Using real cardiac arrest data from the City of Toronto, we conduct an extensive numerical study on AED deployment public locations. We find that hedging against cardiac arrest location uncertainty can produce AED deployments that outperform an intuitive sample average approximation by 9%-15% and cuts the performance gap with respect to an ex post model by half. Our findings suggest that accounting for cardiac arrest location uncertainty can lead to improved accessibility of AEDs during cardiac arrest emergencies and the potential for improved survival outcomes.
Uncertainty is a facet of many decision environments and might arise for various reasons, such as unpredictable information revealed in the future, or inherent fluctuations caused by noise. Stochastic optimization provides a means to handle uncertainty by modeling it by a probability distribution over possible realizations of the actual data, called scenarios. The field of stochastic optimization, or stochastic programming, has its roots in the work of Dantzig [4] and Beale [1] in the 1950s, and has since increasingly found application in a wide variety of areas, including transportation models, logistics, financial instruments, and network design. An important and widely-used model in stochastic programming is the 2-stage recourse model: first, given only distributional information about (some of) the data, one commits on initial (first-stage) actions. Then, once the actual data is realized according to the distribution, further recourse actions can be taken (in the second stage) to augment the earlier solution and satisfy the revealed requirements. The aim is to choose the initial actions so as to minimize the expected total cost incurred. The recourse actions typically entail making decisions in rapid reaction to the observed scenario, and are therefore more costly than decisions made ahead of time. Thus there is a trade-off between committing initially, having only imprecise information while incurring a lower cost, and deferring decisions to the second-stage, when we know the input precisely but the costs are higher. Many applications can be modeled this way, and much of the textbook of Birge and Louveaux [2] is devoted to models and algorithms for this class of problems. A commonly cited example involves a setting where a company has to decide where to set up facilities to serve client demands. Typically the demand pattern is not known precisely at the outset, but one might be able to obtain, through simulation models or surveys, statistical information about the demands. This motivates the following 2-step decision process: in the first-stage, given only distributional information about the demands (and deterministic data for the facility opening costs), one must decide which facilities to open initially; once the client demands are realized according to this distribution, we can extend the solution by opening more facilities, incurring a recourse cost, and we have to assign the realized demands to open facilities. This is the 2-stage stochastic uncapacitated facility location problem. The recourse costs are usually higher than the original ones (because opening a facility later would involve deploying resources with a small lead time); these costs could be different for the different facilities, and could even depend on the realized scenario.
This work deals with a facility location problem in which location and allocation (transportation) policy is defined in two stages such that a first-stage solution should be robust against the possible realizations (scenarios) of the input data that can only be revealed in a second stage. This solution should be robust enough so that it can be recovered promptly and at low cost in the second stage. In contrast to some related modeling approaches from the literature, this new recoverable robust model is more general in terms of the considered data uncertainty; it can address situations in which uncertainty may be present in any of the following four categories: provider-side uncertainty, receiver-side uncertainty, uncertainty in-between, and uncertainty with respect to the cost parameters. For this novel problem, a sophisticated branch-and-cut framework based on Benders decomposition is designed and complemented by several non-trivial enhancements, including scenario sorting, dual lifting, branching priorities, matheuristics and zero-half cuts. Two large sets of instances that incorporate spatial and demographic information of countries such as Germany and US (transportation) and Bangladesh and the Philippines (disaster management) are introduced. They are used to analyze in detail the characteristics of the proposed model and the obtained solutions as well as the effectiveness, behavior and limitations of the designed algorithm.
To handle significant variability in loads, renewable energy generation, as well as various contingencies, (two-stage) robust optimization method has been adopted to construct unit commitment models and to ensure reliable solutions. In this paper, we further explore and extend the modeling capacity of two-stage robust optimization and present two new robust unit commitment variants: the expanded robust unit commitment and the risk constrained robust unit commitment model. We derive structural properties, demonstrate the connection to the popular scenario based stochastic unit commitment models, and present a customized column-and-constraint generation method. Numerical experiments on those models are performed using practical data sets, which illustrate their modeling strength, economic outcomes, and the algorithm performance in solving those models.
This paper attempts to design a reverse supply chain network (SCN), add it to an existing multi-product forward SCN and simultaneously redesign the existing forward supply chain (SC). The problem considers uncertainty on products demand and and also returned products in multi-period context. Benders’ decomposition is applied to solve the stochastic mixed-integer model to optimality. The scenarios are generated based on the demand distribution function using Cholesky’s factorization method to consider correlation among different products’ demands. To decrease the computational effort, the number of scenarios is reduced using k-means clustering algorithm. The method is tested on a cell phone SC.
We propose a two-stage robust optimization model for the distribution network reconfiguration problem with load uncertainty. The first-stage decision is to configure the radial distribution network and the second-stage decision is to find the optimal a/c power flow of the reconfigured network for given demand realization. We solve the two-stage robust model by using a column-and-constraint generation algorithm, where the master problem and subproblem are formulated as mixed-integer second-order cone programs. Computational results for 16, 33, 70, and 94-bus test cases are reported. We find that the configuration from the robust model does not compromise much the power loss under the nominal load scenario compared to the configuration from the deterministic model, yet it provides the reliability of the distribution system for all scenarios in the uncertainty set. Index Terms—Distribution network, minimum loss, mixed-integer second-order cone program (MISOCP), reconfiguration, robust optimization.
We study a reliable facility location problem wherein some facilities are subject to failure from time to time. If a facility fails, customers originally assigned to it have to be reassigned to other (operational) facilities. We formulate this problem as a two-stage stochastic program and then as a nonlinear integer program. Several heuristics that can produce near-optimal solutions are proposed for this NP-hard problem. For the special case where the probability that a facility fails is a constant (independent of the facility), we provide an approximation algorithm with a worst-case bound of 4. The effectiveness of our heuristics is tested by extensive computational studies, which also lead to some managerial insights.