Marco Laumanns

• Dr. habil.
• VP Research at BestMile

We study the coordination of two-echelon supply chains via service level-dependent bonus and penalty contracts and assume that the manufacturer maximizes its profit while enabling the supplier to achieve its performance target. Profit and return on investment (ROI) are considered as the supplier’s performance measures. We compute optimal contract p...

The train rescheduling problem is quite a popular topic in the railway research community. Many approaches are available to reschedule traffic in a network partition, but very few works address the coordination of these partitions. In railway systems with very dense traffic, e.g. the Swiss one, it is not always possible to partition the network suc...

Planners of maintenance intervals and operations have a strong need for rapid development and assessment of comprehensive and reliable timetable scenarios, which are able to satisfy the requirements of both, the train operating company and the infrastructure operating company. To address these requirements, in this paper we present a use case that...

A large-scale ensemble prediction model to predict train delays is presented. The ensemble model uses a disparate set of models, two statistical and one simulation-based to generate forecasts of train delays. The first statistical model is a context-aware random forest that accounts for network traffic states, such as likely stretch conflicts and c...

In the operational management of railway networks, the fast adaptation of timetable scenarios is an important requirement, in which operational disruptions or time windows with temporary unavailability of infrastructure, for instance during maintenance time windows, are taken into consideration. In those situations, easy and fast reconfiguration an...

We present a track-choice and vehicle scheduling extension of the commonly known method for the generation of periodic event schedules ‘PESP’. The extension makes use of the mesoscopic track infrastructure representation widely used by public transport planners and operators. Taking into consideration the technical and operational constraints given...

With continuously increasing capacity utilization of railway networks as well as growing requirements on service quality and reliability, railway timetabling is becoming increasingly difficult. Although most timetables are still constructed manually in practice, the demand for advanced automatic timetabling techniques is evident. Long computation t...

We present a track-choice and vehicle scheduling extension of the commonly known method for the generation of periodic event schedules ‘PESP’. The extension makes use of the mesoscopic track infrastructure repre-sentation widely used by public transport planners and operators. Taking into consideration the technical and operational constraints give...

We present a routing system that considers uncertainties, which are prevalent in any real transport system. Given desired departure or arrival times and a utility function representing the traveller’s preferences, our method computes not just a single path through the network, but a more sophisticated and adaptive journey plan called routing policy...

Recent studies have shown that cell cycle and cell volume are confounding factors when studying biological phenomena in single cells. Here we present a combined experimental and computational method, CellCycleTRACER, to account for these factors in mass cytometry data. CellCycleTRACER is applied to mass cytometry data collected on three different c...

The 27th European Conference on Operational Research, EURO XXVII, took place between 12–15 July 2015 at the University of Strathclyde in Glasgow, UK. In addition to three inspiring plenary sessions delivered by Tyrrell Rockafellar, Alan Wilson and Grazia Speranza, the conference also held eight keynote and three tutorial sessions by highly distingu...

We study the joint optimisation of capacity and safety stock allocation in assembly systems. Particularly, we consider capacitated systems with base-stock policies and periodic review. Capacity allocation is restricted by budget constraints, which can connect multiple systems. Our objective is to minimise overall inventory holding costs while satis...

Optimization and optimal control of multi-echelon supply chain operations is difficult due to the interdependencies across the stages and the various stock nodes of inventory networks. While network inventory control problems can be formulated as Markov decision processes, the resulting models can usually not be solved numerically due to the high d...

Modeling decision-dependent scenario probabilities in stochastic programs is difficult and typically leads to large and highly non-linear MINLPs that are very difficult to solve. In this paper, we develop a new approach to obtain a compact representation of the recourse function using a set of binary decision diagrams (BDDs) that encode a nested co...

Journey planning is a key process in public transport, where travelers get informed how to make the best use of a given public transport system for their individual travel needs. A common trait of most available journey planners is that they assume deterministic travel times, but vehicles in public transport often deviate from their schedule. The p...

Multimodal travel is a ubiquitous part of living in a city. The operation of modern urban transportation networks can negatively be impacted by multiple factors, including poor traffic conditions caused by congestion and events on the road. In effect, transportation networks feature many types of uncertainty, such as variations in the arrival times...

We study a class of capacitated assembly systems operated by base-stock policies and address the problem of finding base-stock levels that minimize holding costs under beta-service level (fill rate) constraints over an infinite horizon. To solve this nonconvex constrained optimization problem, we develop a new simulation-based optimization approach...

A central issue for operators of passenger railways is providing sufficient number of seats for passengers while at the same time minimising operating costs. This is the task of rolling stock planning. Due to the large number of practical, railway specific requirements that a rolling stock plan has to take into account, rolling stock plans are ofte...

Embodiments relate to generating a route plan. A method of generating a route plan is provided. The method receives a route planning request that includes a starting location, a destination location, a desired arrival time, and a set of user preferences. The method obtains transport service information that includes schedules of a plurality of tran...

This paper addresses Markov Decision Processes over compact state and action spaces. We investigate the special case of linear dynamics and piecewise-linear and convex immediate costs for the average cost criterion. This model is very general and covers many interesting examples, for instance in inventory management. Due to the curse of dimensional...

Stochastic programs are usually formulated with probability distributions that are exogenously given. Modeling and solving problems with endogenous uncertainty, where decisions can influence the probabilities, has remained a largely unresolved challenge. In this paper we develop a new approach to handle decision-dependent probabilities based on the...

Decision makers who use optimization technology to generate plans and schedules need to deal not only with high data volume, velocity, and variety, but also data veracity. This presents a significant challenge due to the uncertainty inherent in most data arising from, for example, approximations and aggregations, error in instrumentation, and predi...

The Shortest Path with Alternatives (SPA) policy differs from classical shortest path routing in the following way: instead of providing an exact list of means of transportation to follow, this policy gives such a list for each stop, and the traveler is supposed to pick the first option from this list when waiting at some stop. First, we show that...

One of the key unresolved challenges in Adjustable Robust Optimisation is how to deal with large discrete uncertainty sets. In this paper we present a technique for handling such sets based on symmetry breaking ideas from Constraint Programming. In earlier work we applied the technique to a pre-disaster planning problem modelled as a two-stage Stoc...

Optimal operation of rail transport systems has become an increasingly challenging task over the last decades. To allow for a better understanding of the system dynamics in different operational states (including disruptions) and in order to evaluate and to improve control strategies, a multi-component simulation framework, representing a closed-lo...

Today many European railway networks are operating near capacity. Developing timetables for these dense and often highly congested networks is becoming increasingly difficult. Several algorithmic approaches for solving timetabling problems have been developed in recent years, but the problem size, computational complexity, and lack of transparent i...

Certain regulated industries are monitored by inspections that ensure adherence (compliance) to regulations. These inspections can often be with very short notice and can focus on particular aspects of the business. Failing such inspections can bring great losses to a company; thus, evaluating the risks of failure against various inspection strateg...

Several types of symmetry have been identified and exploited in Constraint Programming, leading to large reductions in search time. We present a novel application of one such form of symmetry: detecting dynamic value interchangeability in the random variables of a 2-stage stochastic problem. We use a real-world problem from the literature: finding...

Railway networks are operated more and more at capacity margins, schedules are becoming more susceptible to disturbances, and delays propagate and hamper the service level experienced by the customers. As a consequence railway traffic management is becoming increasingly challenging, thus motivating the development of computer-aided systems. This pa...

We consider a third party logistics service provider (LSP), who faces the problem of distributing different products from suppliers to consumers having no control on supply and demand. In a third party set-up, the operations of transport and storage are run as a black box for a fixed price. Thus the incentive for an LSP is to reduce its operational...

Process models are used widely in business process management related initiatives, such as business process optimization, IT system implementation, etc. In large amount of practices, multiple process models are maintained simultaneously and are required to keep consistent with each other. Synchronization of multiple process modules has been studied...

We consider a production planning problem under uncertainty in which companies have to make product allocation decisions such that the risk of failing regulatory inspections of sites - and consequently losing revenue - is minimized. In the proposed decision model the regulatory authority is an adversary. The outcome of an inspection is a Bernoulli-...

In this paper we investigate to what extent random search methods, equipped with an archive of bounded size to store a limited amount of solutions and other data, are able to obtain good Pareto front approximations. We propose and analyze two archiving schemes that allow for maintaining a sequence of solution sets of given cardinality that converge...

We consider a terminal operator who provides container handling services at multiple terminals within the same port. In this setting, the well-known berth allocation problem can no longer be considered for each terminal in isolation since vessel calls should be spread over the various terminals to avoid peaks and troughs in quay crane utilization,...

In the classical s-t network reliability problem, a network G is given with two designated vertices s and t. The arcs are subject to independent random failures, and the task is to compute the probability that s and t are connected in the resulting network. This probability is called the s-t reliability. We consider the problem of estimating the s-...

Many railway companies in Europe operate periodic timetables. Yet, most timetables are not entirely periodic but have a mixture of different periodicity and many exceptions to cope with changing demand. Current approaches for automatic timetable generation are not able to deal with such partially periodic structure, but consider only fully periodic...

This paper addresses the problem of generating conflict-free train schedules on a microscopic model of the railway infrastructure. Conflicts arise if two or more trains are scheduled to block the same track section at the same time. A standard model for this problem is the so-called conflict graph, where each considered train path corresponds to a...

This paper proposes a comprehensive multi-level framework for railway scheduling, which starts with a high-level commercial description of intended train services and aims to generates a conflict-free detailed schedule as the final outcome. The approach consists of three description levels and corresponding interfaces that enable a structured decom...

In this paper, we examine the problem of maintaining an approximation of the set of nondominated points visited during a multiobjective optimization, a problem commonly known as archiving. Most of the currently available archiving algorithms are reviewed, and what is known about their convergence and approximation properties is summarized. The main...

Convergence rate analyses of evolutionary multi-objective optimization algorithms in continuous search space are yet rare. First results have been obtained for simple algorithms. Here, we provide concrete results of convergence rates for a state-of-the-art algorithm, namely the S-metric selection evolutionary multi-objective optimization algorithm...

This article addresses the problem of generating conflict-free periodic train timetables for large railway networks. We follow a two-level approach, where a simplified track topology is used to obtain a macrolevel schedule, and the detailed topology is considered locally on the microlevel. To enlarge the solution space in the interface of the two l...

This paper addresses a production planning setting for pharmaceutical companies under the risk of failing quality inspections that are undertaken by the regulatory authorities to ensure good manufacturing practices. A staged decision model is proposed where the regulatory authority is considered an adversary with limited inspection budget, and the...

Convergence analyses of evolutionary multiobjective optimization algorithms typically deal with the convergence in limit (stochastic convergence) or the run time. Here, for the first time concrete results for convergence rates of several popular algorithms on certain classes of continuous functions are presented. We consider the algorithms in the v...

We consider a container terminal operator who faces the problem of constructing a cyclic berth plan. Such a plan defines the arrival and departure times of each cyclically calling vessel on a terminal, taking into account the expected number of containers to be handled and the necessary quay and crane capacity to do so. Conventional berth planning...

In inventory/distribution systems, lateral stock transshipments might lead to cost savings by effectively sharing inventory on the same echelon level. An approach is presented to quantify the value of this additional flexibility by determining the corresponding optimal control policies, and the resulting cost, in supply networks with transshipments...

Recently, a convergence proof of stochastic search algorithms toward finite size Pareto set approximations of continuous multi-objective optimization problems has been given. The focus was on obtaining a finite approximation that captures the entire solution set in some suitable sense, which was defined by the concept of epsilon-dominance. Though b...

Based on the regulation of grid fees and the regulatory requirements on the quality of supply, grid operators attempt to find an optimal balance between costs and quality of supply. One main aspect of the quality of supply is the non-availability of supply, which strongly depends on the duration of the restoration process after incidents and theref...

Spreading processes on networks are often analyzed to understand how the outcome of the process (e.g. the number of affected nodes) depends on structural properties of the underlying network. Most available results are ensemble averages over certain interesting graph classes such as random graphs or graphs with a particular degree distributions. In...

In order to reduce costs for grid operation and to fulfil regulatory requirements, grid operators need to analyse the relation between the organisation/employment of resources and the corresponding costs/quality of supply. This paper presents a complete grid operation model consisting of two modules for planned and unplanned work. The developed mod...

Sustainable management of groundwater resources is of crucial importance for regions where freshwater supply is naturally limited. Long-term planning of groundwater usage requires computer-based decision support tools: on the one hand, they must be able to predict the complex system dynamics with sufficient accuracy, on the other, they must allow e...

We address the problem of generating detailed conflict-free railway schedules for given sets of train lines and frequencies. To solve this problem for large railway networks, we propose a network decomposition into condensation and compensation zones. Condensation zones contain main station areas, where capacity is limited and trains are required t...

In this work, we develop repair strategies for simplified models of electric power grids. The goal is to gain insight into the combinatorial properties of repair problems in order to improve maintenance and restoration planning. Firstly, we consider a seriously damaged grid and try to find the set of lines that should be repaired first in order to...

Spreading processes on networks can often be mapped onto network reliability problems. The computational complexity of computing the probability that the spreading process reaches some given subset K of the nodes is well studied as it reduces to the classical K-terminal reliability problem. Often one is not interested in a particular set K, but mor...

In this paper we address the problem of approximating the ’knee’ of a bi-objective optimization problem with stochastic search algorithms. Knees or entire knee-regions are of particular interest since such solutions are often preferred by the decision makers in many applications. Here we propose and investigate two update strategies which can be u...

In this work we investigate the convergence of stochastic search algorithms toward the Pareto set of continuous multi-objective optimization problems. The focus is on obtaining a finite approximation that should capture the entire solution set in a suitable sense, which will be defined using the concept of ε-dominance. Under mild assumptions about...

This paper shows how to compute optimal control policies for a certain class of supply networks via a combination of stochastic dynamic programming and parametric programming.We consider supply networks where the dynamics of the material and information flows within the entire network can be expressed by a system of first-order difference equations...

Based on the regulation of grid fees and the regulatory requirements on the quality of supply, grid oper-ators attempt to find an optimal balance between costs and quality of supply. One main aspect of the quality of supply is the non-availability of supply, which strongly depends on the duration of the re-supply process and therefore on the availa...

We consider a container operator, who serves a number of shipping lines by discharging and loading their periodically arriving container vessels. Disruptions on vessels' travel times lead to stochastic arrivals in the port. To cope with these disturbances, the oper-ator and each vessel line agree on two types of arrivals: arrivals i) within, and ii...

This paper presents the first convergence result for random search algorithms to a subset of the Pareto set of given maximum size k with bounds on the approximation quality. The core of the algorithm is a new selection criterion based on a hypothetical multilevel grid on the objective space. It is shown that, when using this criterion for accepting...

In the classical s-t network reliability problem a fixed network G is given including two designated vertices s and t (called terminals). The edges are subject to independent random failure, and the task is to compute the probability that s and t are connected in the resulting network, which is known to be #P-complete. In this paper we are interest...

Recently, a convergence proof of stochastic search algorithms toward finite size Pareto set approximations of continuous multi-objective optimization problems has been given. The focus was on obtaining a finite approximation that captures the entire solution set in some suitable sense, which was defined by the concept of epsilon-dominance. Though b...

Abstract We consider a port consisting of a cluster of inter-related terminals, where container ves- sels arrive cyclically. The problem,is to strategically assign a terminal and a time interval of berthing to each of the vessels in the cycle. Restricting properties are terminal quay lengths and quay crane capacity. Conflicting objectives are i) mi...

The problem of connecting a set of client nodes with known demands to a root node through a minimum cost tree network, subject to capacity constraints on all links is known as the capacitated minimum spanning tree (CMST) problem. As the problem is NP-hard, we propose a hybrid ant colony optimization (ACO) algorithm to tackle it heuristically. The a...

We develop a model based on stochastic discrete-time controlled dynamical systems in order to derive optimal policies for controlling the material flow in supply networks. Each node in the network is described as a transducer such that the dynamics of the material and information flows within the entire network can be expressed by a system of first...

After adequately demonstrating the ability to solve different two-objective optimization problems, multiobjective evolutionary algorithms (MOEAs) must demonstrate their efficacy in handling problems having more than two objectives. In this study, we have suggested three different approaches for systematically designing test problems for this purpos...

This paper discusses methods for generating or approximating the Pareto set of multiobjective optimization problems by solving a sequence of constrained single-objective problems. The necessity of determining the constraint value a priori is shown to be a serious drawback of the original epsilon-constraint method. We therefore propose a new, adapti...

We give faster approximation algorithms for the generalization of two NP-hard spanning tree problems. First, we investigate the problem of minimizing the degree of minimum spanning forests. Fischer [3] has shown how to compute a minimum spanning tree of degree at most b 4 + 1/ ln b^{\rm 4 + 1/ ln {\it b}}) for any b>1, where Δ* is the value of an o...

This paper describes the use of evolutionary algorithms to solve multiobjective optimization problems arising at different stages in the automotive design process. The problems considered are black box optimization scenarios: definitions of the decision space and the design objectives are given, together with a procedure to evaluate any decision al...

This chapter investigates the convergence behavior of simple evolutionary algorithms with different selection strategies on a continuous multiobjective model problem. Special focus is given to the problem of controlling the mutation strength, since an adaptation of the mutation strength is necessary to converge to the optimum with arbitrary precisi...

We discuss methods for generating or approximating the Pareto set of multiobjective optimization problems by solving a sequence of constrained single-objective problems. The necessity of determining the constraint value a priori is shown to be a serious drawback of the original epsilon-constraint method. We therefore propose a new, adaptive scheme...

This thesis deals with the analysis and application of evolutionary algorithms for optimization problems with multiple objectives. Many application problems involve (i) a system model that is not given in closed analytical form and (ii) multiple, often conflicting optimization criteria. Both traits hamper the application of classical optimization t...

This paper presents a rigorous running time analysis of evolutionary algorithms on pseudo-Boolean multiobjective optimization problems. We propose and analyze different population-based algorithms, the simple evolutionary multiobjective optimizer (SEMO), and two improved versions, fair evolutionary multiobjective optimizer (FEMO) and greedy evoluti...

This paper presents a rigorous running time analysis of evolutionary algorithms on pseudo-Boolean multiobjective optimization problems. We propose and analyze dierent population-based algorithms, the simple evolutionary multiobjective optimizer SEMO and two improved versions, FEMO and GEMO. The analysis is carried out on two bi-objective model prob...

The problem of flnding a Capacitated Minimum Spanning Tree asks for connecting a set of client nodes to a root node through a minimum cost tree network, subject to capacity constraints on all links. This paper reports on our design, implementation and performance evaluationofahybridAntColonyOptimizationalgorithmforflndingCapacitatedMinimum Spanning...

This paper introduces a text based interface (PISA) that allows to separate the algorithm-specific part of an optimizer from the applicationspecific part. These parts are implemented as independent programs forming freely combinable modules. It is therefore possible to provide these modules as ready-to-use packages. As a result, an application engi...

An important issue in multiobjective optimization is the quantitative comparison of the performance of different algorithms. In the case of multiobjective evolutionary algorithms, the outcome is usually an approximation of the Pareto-optimal set, which is denoted as an approximation set, and therefore the question arises of how to evaluate the qual...

Mu l ip often conflicting objectives arise naturalj in most real worl optimization scenarios. As evol tionaryalAxjO hms possess several characteristics that are desirabl e for this type of probl em, this clOv of search strategies has been used for mul tiobjective optimization for more than a decade. Meanwhil e evol utionary mul tiobjective optimiza...

Multiple, often conflicting objectives arise naturally in most real-world optimization scenarios. As evolutionary algorithms possess several characteristics that are desirable for this type of problem, this class of search strategies has been used for multiobjective optimization for more than a decade. Meanwhile evolutionary multiobjective optimiza...

This paper presents a rigorous running time analysis of evolutionary algorithmson pseudo-Boolean multiobjective optimization problems. We proposeand analyze dierent population-based algorithms, the simple evolutionarymultiobjective optimizer SEMO and two improved versions, FEMO andGEMO. The analysis is carried out on two bi-objective model problems...

In recent years, several researchers have concentrated on using probabilistic models in evolutionary algorithms. These Estimation Distribution Algorithms (EDA) incorporate methods for automated learning of correlations between variables of the encoded solutions. The process of sampling new individuals from a probabilistic model respects these mutua...

For the first time, a running time analysis of populationbased multi-objective evolutionary algorithms for a discrete optimization problem is given. To this end, we define a simple pseudo-Boolean bi-objective problem (Lotz: leading ones– trailing zeroes) and investigate time required to find the entire set of Pareto-optimal solutions. It is shown t...

Quantitative quality assessment of approximations of the Pareto-optimal set is an important issue in comparing the performance of multiobjective evolutionary algorithms. Most popular are methods that assign each approximation set a vector of real numbers that reflect different aspects of the quality. In this study, we investigate this type of quali...

Over the past few years, the research on evolutionary algorithms has demonstrated their niche in solving multi-objective optimization problems, where the goal is to find a number of Pareto-optimal solutions in a single simulation run. However, none of the multi-objective evolutionary algorithms (MOEAs) has a proof of convergence to the true Pareto-...

After adequately demonstrating the ability to solve different two-objective optimization problems, multi-objective evolutionary algorithms (MOEAs) must now show their efficacy in handling problems having more than two objectives. In this paper, we suggest three different approaches for systematically designing test problems for this purpose. The si...

Adaptive Cruise Control (ACC) systems represent an active research area in the automobile industry. The design of such systems typically involves several, possibly conflicting criteria such as driving safety, comfort and fuel consumption. When the different design objectives cannot be met simultaneously, a number of non-dominated solutions exists,...

After adequately demonstrating the ability to solve different two-objective optimization problems, multi-objective evolutionary algorithms (MOEAs) must show their efficacy in handling problems having more than two objectives. In this paper, we suggest three different approaches for systematically designing test problems for this purpose. The simpli...

Over the past few years, the research on evolutionary algorithms has demonstrated their niche in solving multiobjective optimization problems, where the goal is to find a number of Pareto-optimal solutions in a single simulation run. Many studies have depicted different ways evolutionary algorithms can progress towards the Pareto-optimal set with a...

The Strength Pareto Evolutionary Algorithm (SPEA) is a relatively recent technique for finding or approximating the Pareto-optimal set for multiob- jective optimization problems. In different studies ,2 SPEA has shown very good performance in comparison to other multiobjective evolutionary algorithms, and therefore it has been a point of reference...

This paper describes the use of Evolutionary Algorithms (EAs) as a decision support tool in environmentally relevant decision problems. Though various methods from Artificial Intelligence as well as from Computational Intelligence have been successfully integrated into Environmental Decision Support Systems (EDSS), the use of Evolutionary Computati...

This paper addresses the problem of controlling mutation strength in multi-objective evolutionary algorithms and its implications for the convergence to the Pareto set. Adaptive parameter control is one major issue in the field of evolutionary computation, and several methods have been proposed and applied successfully for single objective optimiza...

This paper adresses the problem of diversity in multiobjective evolutionary algorithms and its implications for the quality of the approximated set of efficient solutions (Pareto set). Current approaches for maintaining diversity are classified and related to the overall fitness assignment strategy. The resulting groups of complex selection operato...

Over the past few years, the research on evolutionary algorithms has demonstrated their niche in solving multi-objective optimization problems, where the goal is to find a number of Pareto-optimal solutions in a single simulation run. Many studies have depicted di#erent ways evolutionary algorithms can progress towards the true Paretooptimal soluti...

. This paper examines the road train concept as a new alternative in long-distance freight traffic. The design of such a system is a difficult task since many different and conflicting criteria arise depending on the application spectrum, the legal conditions and the preferences of the carrier. Furthermore the evaluation of each decision alternativ...

The Strength Pareto Evolutionary Algorithm (SPEA) (Zitzler and Thiele 1999) is a relatively recent technique for finding or approximating the Pareto-optimal set for multiobjective optimization problems. In different studies (Zitzler and Thiele 1999; Zitzler, Deb, and Thiele 2000) SPEA has shown very good performance in comparison to other multiobje...

This paper studies the influence of what are recognized as key issues in evolutionary multi-objective optimization: archiving (to keep track of the current non-dominated solutions), elitism (to let the archived solutions take part in the search process), and diversity maintenance (through density dependent selection). Many proposed algorithms use t...