# Roberto RossiThe University of Edinburgh | UoE · Business School

Roberto Rossi

PhD

## About

128

Publications

12,945

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869

Citations

Introduction

Reader at the University of Edinburgh Business School.
My research focuses on automated reasoning. I develop systems that aim to be robust and scalable in such a way as to enable computers to act intelligently in increasingly complex real world settings and in uncertain environments.
I conduct cross-disciplinary methodological as well as applied research in decision making under uncertainty, artificial intelligence, operational research and supply chain management.

Additional affiliations

January 2012 - present

Education

September 2005 - November 2008

## Publications

Publications (128)

This paper extends the single-item single-stocking location nonstationary stochastic inventory problem to relax the assumption of independent demand. We present a mathematical programming-based solution method built upon an existing piecewise linear approximation strategy under the receding horizon control framework. Our method can be implemented b...

The (R, s, S) is a stochastic inventory control policy widely used by practitioners. In an inventory system managed according to this policy, the inventory is reviewed at instant R; if the observed inventory position is lower than the reorder level s an order is placed. The order's quantity is set to raise the inventory position to the order-up-to-...

Electric road systems (ERS) are roads that allow compatible vehicles to be powered by grid electricity while in transit, reducing the need for stopping to recharge electric batteries. We investigate how this technology can affect routing and delivery decisions for hybrid heavy good vehicles (HGVs) travelling on a ERS network to support the demand o...

A well-known control policy in stochastic inventory control is the (R,s,S) policy, in which inventory is raised to an order-up-to-level S at a review instant R whenever it falls below reorder-level s. To date, little or no work has been devoted to developing approaches for computing (R,s,S) policy parameters. In this work, we introduce a hybrid app...

A well-know control policy in stochastic inventory control is the (R, s, S) policy, in which inventory is raised to an order-up-to-level S at a review instant R whenever it falls below reorder-level s. To date, little or no work has been devoted to developing approaches for computing (R, s, S) policy parameters. In this work, we introduce a hybrid...

In this work we compare several new computational approaches to an inventory routing problem, in which a single product is shipped from a warehouse to retailers via an uncapacitated vehicle. We survey exact algorithms for the Traveling Salesman Problem (TSP) and its relaxations in the literature for the routing component. For the inventory control...

Dynamic Programming (DP) can solve many complex problems in polynomial or pseudo-polynomial time, and it is widely used in Constraint Programming (CP) to implement powerful global constraints. Implementing such constraints is a nontrivial task beyond the capability of most CP users, who must rely on their CP solver to provide an appropriate global...

In this paper we address the single-item, single-stocking point, non-stationary stochastic lot-sizing problem under backorder costs. It is well known that the (s, S) policy provides the optimal control for such inventory systems. However the computational difficulties and the nervousness inherent in (s, S) paved the way for the development of vario...

This paper considers the periodic-review nonstationary stochastic joint replenishment problem (JRP) under Bookbinder and Tan's static-dynamic uncertainty control policy. According to a static-dynamic uncertainty control rule, the decision maker fixes timing of replenishments once and for all at the beginning of the planning horizon, inventory posit...

We present an extended mixed-integer programming formulation of the stochastic lot-sizing problem for the static-dynamic uncertainty strategy. The proposed formulation is significantly more time efficient as compared to existing formulations in the literature and it can handle variants of the stochastic lot-sizing problem characterized by penalty c...

This paper considers the single-item single-stocking non-stationary stochastic lot-sizing problem under correlated demand. By operating under a nonstationary (R, S) policy, in which R denote the reorder period and S the associated order-up-to-level, we introduce a mixed integer linear programming (MILP) model which can be easily implemented by usin...

In this work we introduce declarative statistics, a suite of declarative modelling tools for statistical analysis. Statistical constraints represent the key building block of declarative statistics. First, we introduce a range of relevant counting and matrix constraints and associated decompositions, some of which novel, that are instrumental in th...

In this paper, we study the single-item single-stocking location non-stationary stochastic lot sizing problem for a perishable product. We consider fixed and proportional ordering cost, holding cost and penalty cost. The item features a limited shelf life, therefore we also take into account a variable cost of disposal. We derive exact analytical e...

In this work we investigate opportunities offered by telematics and analytics to enable better informed, and more integrated, collaborative management decisions on construction sites. We focus on efficient refuelling of assets across construction sites. More specifically, we develop decision support models that, by leveraging data supplied by diffe...

Stochastic Constraint Programming (SCP) is an extension of Constraint Programming (CP) used for modelling and solving problems involving constraints and uncertainty. SCP inherits excellent modelling abilities and filtering algorithms from CP, but so far it has not been applied to large problems. Reinforcement Learning (RL) extends Dynamic Programmi...

The accepted lore is that Operational Research traces its roots back to the First and Second World Wars, when scientific research was used to improve military operations. In this essay we provide a different perspective on the origins of Operational Research by arguing that these are deeply intertwined with the impressive technological advances in...

This paper addresses the single-item single-stocking location stochastic lot sizing problem under the $(s, S) $ policy. We first present a mixed integer non-linear programming (MINLP) formulation for determining near-optimal $(s, S)$ policy parameters. To tackle larger instances, we then combine the previously introduced MINLP model and a binary se...

In this work we develop advanced techniques for measuring bank insolvency risk. More specifically, we contribute to the existing body of research on the Z-Score. We develop bias reduction strategies for state-of-the-art Z-Score measures in the literature. We introduce novel estimators whose aim is to effectively capture nonstationary returns; for t...

We introduce the BIN_COUNTS constraint, which deals with the problem of counting the number of decision variables in a set which are assigned values that lie in given bins. We illustrate a decomposition and a filtering algorithm that achieves generalised arc consistency. We contrast the filtering power of these two approaches and we discuss a numbe...

We consider the non-stationary stochastic lot sizing problem with backorder costs and make a cost comparison among different lot-sizing strategies. We initially provide an overview of the strategies and some corresponding solution approaches in the literature. We then compare the cost performances of the lot-sizing strategies on a common test bed w...

Constraint Programming is a powerful and expressive framework for modelling and solving combinatorial problems. It is nevertheless not always easy to use, which has led to the development of high-level specification languages. We show that Constraint Logic Programming can be used as a meta-language to describe itself more compactly at a higher leve...

This paper studies the computation of so-called order-up-to levels for a stochastic programming inventory problem of a perishable product. Finding a solution is a challenge as the problem enhances a perishable product, fixed ordering cost and non-stationary stochastic demand with a service level constraint. An earlier study [7] derived order-up-to...

Some optimisation problems require a random-looking solution with no apparent patterns, for reasons of fairness, anonymity, undetectability or unpredictability. Randomised search is not a good general approach because problem constraints and objective functions may lead to solutions that are far from random.We propose a constraintbased approach to...

In this work we introduce a novel approach, based on sampling, for finding assignments that are likely to be solutions to stochastic constraint satisfaction problems and constraint optimisation problems. Our approach reduces the size of the original problem being analysed; by solving this reduced problem, with a given confidence probability, we obt...

In practical decision making, one often is interested in solutions that balance multiple objectives. In this study we focus on generating efficient solutions for optimization problems with two objectives and a large but finite number of feasible solutions. Two classical approaches exist, being the constraint method and the weighting method, for whi...

We introduce a novel strategy to address the issue of demand estimation in single-item single-period stochastic inventory optimisation problems. Our strategy analytically combines confidence interval analysis and inventory optimisation. We assume that the decision maker is given a set of past demand samples and we employ confidence interval analysi...

Only two Croston-style forecasting methods are currently known for handling
stochastic intermittent demand with possible demand obsolescence: TSB and HES,
both shown to be unbiased. When an item becomes obsolescent then TSB's
forecasts decay exponentially, while HES's decay hyperbolically. We describe a
third variant called Linear-Exponential Smoot...

Purpose
– The purpose of this paper is to assess whether an existing sourcing strategy can effectively supply products of appropriate quality with acceptable levels of product waste if applied to an international perishable product supply chain. The authors also analyse whether the effectiveness of this sourcing strategy can be improved by includin...

The first order loss function and its complementary function are extensively used in practical settings. When the random variable of interest is normally distributed, the first order loss function can be easily expressed in terms of the standard normal cumulative distribution and probability density function. However, the standard normal cumulative...

We introduce statistical constraints, a declarative modelling tool that links
statistics and constraint programming. We discuss two novel statistical
constraints and some associated filtering algorithms. Finally, we illustrate
applications to standard problems encountered in statistics and to a novel
inspection scheduling problem in which the aim i...

Stochastic Constraint Programming (SCP) is an extension of Constraint Programming for modelling and solving combinatorial problems involving uncertainty. This paper proposes a metaheuristic approach to SCP that can scale up to large problems better than state-of-the-art complete methods, and exploits standard filtering algorithms to handle hard con...

We discuss the problem of computing optimal linearisation parameters for the first order loss function of a family of arbitrarily distributed random variable. We demonstrate that, in contrast to the problem in which parameters must be determined for the loss function of a single random variable, this problem is nonlinear and features several local...

To compare different forecasting methods on demand series we require an error
measure. Many error measures have been proposed, but when demand is
intermittent some become inapplicable, some give counter-intuitive results, and
there is no agreement on which is best. We argue that almost all known measures
rank forecasters incorrectly on intermittent...

To match products of different quality with end market preferences under supply uncertainty, it is crucial to integrate product quality information in logistics decision making. We present a case of this integration in a meat processing company that faces uncertainty in delivered livestock quality. We develop a stochastic programming model that exp...

The stochastic lot sizing problem is a practical pervasive problem in the
inventory control literature. A number of strategies have been proposed in the
literature to tackle the problem, among which we find the so-called
"static-dynamic uncertainty". This strategy benefits from a low degree of
system nervousness that comes with a fixed replenishmen...

Croston's method is generally viewed as superior to exponential smoothing
when demand is intermittent, but it has the drawbacks of bias and an inability
to deal with obsolescence, in which an item's demand ceases altogether. Several
variants have been reported, some of which are unbiased on certain types of
demand, but only one recent variant addre...

We consider the issue of modeling service level measures in stochastic decision making via chance constraints. More specifically we focus on service level measures in production/inventory control under stochastic demand and alpha service level constraints, which are constraints enforcing a prescribed non-stockout probability for the system. We intr...

We consider the periodic-review, single-location, single-product, production/inventory control problem under non stationary demand and service-level constraints. The product is perishable and has a fixed shelf life. Costs comprise fixed ordering costs and inventory holding costs. For this inventory system we discuss a number of control policies tha...

The basis of this study is a Stochastic Programming (SP) model derived for a practical case of a specifc inventory control problem for a perishable product. As it contains chance constraints describing the service level, deriving policies for this model is a challenge. In order to find parameter values in an order-up-to level policy, we derive a co...

The quality of most fresh products deteriorates as a function of environmental conditions and time, resulting in reduced market value and ultimately in product waste. Although product spoilage significantly impacts the performance of perishable supply chains often supply chain design strategies do not sufficiently account for the perishable product...

To fulfil segmented consumer demand and add value, meat processors seek to exploit quality differences in meat products. Availability of product quality information is of key importance for this. We present a case study where an innovative sensor technology that provides estimates of an important meat quality feature is considered. Process design s...

Stochastic Constraint Satisfaction Problems (SCSPs) are a powerful modeling framework for problems under uncertainty. To solve them is a PSPACE task. The only complete solution approach to date — scenario-based stochastic constraint programming — compiles SCSPs down into classical CSPs. This allows the reuse of classical constraint solvers to solve...

In this paper, we address the general multi-period production/inventory problem with non-stationary stochastic demand and supplier lead time under service-level constraints. A re-plenishment cycle policy is modeled. We propose two hybrid algorithms that blend Con-straint Programming and Local Search for computing near-optimal policy parameters. Bot...

This paper describes and analyses a Stochastic Programming (SP) model that is used for a specific inventory control problem for a per-ishable product. The decision maker is confronted with a non-stationary random demand for a fixed shelf life product and wants to make an or-dering plan for a finite horizon that satisfies a service level constraint....

One of the most important policies adopted in inventory control is the replenishment cycle policy. Such a policy provides
an effective means of damping planning instability and coping with demand uncertainty. In this paper we develop a constraint
programming approach able to compute optimal replenishment cycle policy parameters under non-stationary...

The presence of symmetry in constraint satisfaction problems can cause a great deal of wasted search effort, and several methods for breaking symmetries have been reported. In this paper we describe a new method called Symmetry Breaking by Nonstationary Optimisation, which interleaves local search in the symmetry group with backtrack search on the...

Supply chain management for fresh produce differs significantly from that of other products. Similarly to other products, fresh produce quality plays a key role in consumer selection behavior. The key difference consists in the fact that, for fresh produce, quality varies over time and it is dramatically affected by storage conditions. Maintaining...

We provide an efficient computational approach to solve the mixed integer programming (MIP) model developed by Tarim and Kingsman [8] for solving a stochastic lot-sizing problem with service level constraints under the static–dynamic uncertainty strategy. The effectiveness of the proposed method hinges on three novelties: (i) the proposed relaxatio...

In this work we propose an efficient dynamic programming approach for computing replenishment cycle policy parameters under non-stationary stochastic demand and service level constraints. The replenishment cycle policy is a popular inventory control policy typically employed for dampening planning instability. The approach proposed in this work ach...

Tempelmeier (2007) considers the problem of computing replenishment cycle
policy parameters under non-stationary stochastic demand and service level
constraints. He analyses two possible service level measures: the minimum no
stock-out probability per period ({\alpha}-service level) and the so called
"fill rate", that is the fraction of demand sati...

We discuss a novel approach for dealing with single-stage stochastic constraint satisfaction problems (SCSPs) that include random variables over a continuous or large discrete support. Our approach is based on two novel tools: sampled SCSPs and (α, υ)-solutions. Instead of explicitly enumerating a very large or infinite set of future scenarios, we...

Stochastic inventory control in multi-echelon systems poses hard problems in optimisation under uncertainty. Stochastic programming can solve small instances optimally, and approximately solve larger instances via scenario reduction techniques, but it cannot handle arbitrary nonlinear constraints or other non-standard features. Simulation optimisat...

This paper presents a case study on use of advanced product quality information in meat processing. To serve segmented customer demand meat processors consider use of innovative sensor technology to sort meat products to customer orders. To assess the use of this sensor technology a discrete-event simulation model is built. Various scenarios were d...

We provide an efficient computational approach to solve the mixed integer programming (MIP) model developed by Tarim and Kingsman [8] for solving a stochastic lot-sizing problem with service level constraints under the static–dynamic uncertainty strategy. The effectiveness of the proposed method hinges on three novelties: (i) the proposed relaxatio...

In this survey, we focus on problems of decision making under uncertainty. First, we clarify the meaning of the word “uncertainty” and we describe the general structure of problems that fall into this class. Second, we provide a list of problems from the Constraint Programming, Artificial Intelligence, and Operations Research literatures in which u...

The presence of symmetry in constraint satisfaction problems can cause a great deal of wasted search effort, and several methods for breaking symmetries have been reported. We describe a new approach to partial symmetry breaking: using local search in the symmetry group to detect violated lex-leader constraints. The local search is interleaved with...

In this paper we address the general multi-period production/inventory problem with non-stationary stochastic demand and supplier lead-time under service level constraints. A replenishment cycle policy (Rn,Sn) is modeled, where Rn is the nth replenishment cycle length and Sn is the respective order-up-to-level. We propose a stochastic constraint pr...

Stochastic Constraint Programming is an extension of Constraint Programming for modelling and solving combinatorial problems
involving uncertainty. A solution to such a problem is a policy tree that specifies decision variable assignments in each
scenario. Several complete solution methods have been proposed, but the authors recently showed that an...

In this survey, we focus on problems of decision making under uncertainty. First, we clarify the meaning of the word “uncertainty”
and we describe the general structure of problems that fall into this class. Second, we provide a list of problems from the
Constraint Programming, Artificial Intelligence, and Operations Research literatures in which u...

The problem of finding the optimal timing of audit activities within an organisation has been addressed by many researchers.
We propose a stochastic programming formulation with Mixed Integer Linear Programming (MILP) and Constraint Programming (CP)
certainty-equivalent models. In experiments neither approach dominates the other. However, the CP ap...

In many industrial environments there is a significant class of problems for which the perishable nature of the inventory cannot be ignored in developing replenishment order plans. Food is the most salient example of a perishable inventory item. In this work, we consider the periodic-review, single-location, single-product production/inventory cont...