ICORES 2021 Abstracts

Area 1 - Methodologies and Technologies
Full Papers Short Papers
Area 2 - Applications
Full Papers Short Papers

Area 1 - Methodologies and Technologies

Full Papers
Paper Nr: 2
Title:

Robust Emergency Medical System Design as a Multi-objective Goal Programming Problem
Authors:

Marek Kvet
Abstract: The main goal of this paper is to introduce and compare three different mathematical modelling approaches to robust emergency medical service system design. The idea of system robustness follows from the necessity of making the system resistant to various detrimental events, which may unexpectedly occur in the associated transportation network and thus negatively affect service. Such viewpoint is important mainly in different kinds of public service systems including rescue services, in which time necessary for service delivery plays a very important role. While the standard method of robust system designing takes into account only the worst possible situation considering the set of detrimental scenarios, suggested modeling approaches compute a separate objective function value for each scenario and then a special constraint is added to the original mathematical model. This way, an epsilon-constraint principle to the problem solution is applied. In this paper, numerical experiments to study the performance characteristics of suggested solving methods accompany the theoretical explanation of all presented models.
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Paper Nr: 12
Title:

An Algorithmic Approach to Online Multi-Facility Location Problems
Authors:

Christine Markarian, Abdul-Nasser Kassar and Manal Yunis
Abstract: Facility Location problems ask to optimally place facilities with respect to some objective so that all clients requesting a facility service are served. These are one of the most well-studied optimization problems spanning many research areas, such as operations research, computer science, and management science. Classical algorithmic study of Facility Location problems is based on the assumption that clients need to be served with one facility each. Nevertheless, in many real-world applications, facilities experience disruptions and to overcome their failures, a robust service is desired. To obtain this, clients are served with more than one facility, and this is commonly represented by an additional input parameter. The aim of the algorithm is then to provide a robust service to all clients while minimizing costs. This is known as the Multi-Facility Location problem (MFL), a well-known optimization problem, studied in the offline setting in which the entire input sequence is known to the algorithm in advance. In this paper, we address MFL in the online setting, in which client requests are not known in advance but revealed over time. We refer to it as the Online Multi-Facility Location problem (OMFL) and study its metric and non-metric variants. We propose the first online algorithms for these variants and measure their performance using the standard notion of competitive analysis. The latter is a worst-case analysis that compares the cost of the online algorithm to that of the optimal offline algorithm that is assumed to know all demands in advance.
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Paper Nr: 16
Title:

Optimizing Hospital Room Layout to Reduce the Risk of Patient Falls
Authors:

Sarvenaz Chaeibakhsh, Roya S. Novin, Tucker Hermans, Andrew Merryweather and Alan Kuntz
Abstract: Despite years of research into patient falls in hospital rooms, falls and related injuries remain a serious concern to patient safety. In this work, we formulate a gradient-free constrained optimization problem to generate and reconfigure the hospital room interior layout to minimize the risk of falls. We define a cost function built on a hospital room fall model that takes into account the supportive or hazardous effect of the patient’s surrounding objects, as well as simulated patient trajectories inside the room. We define a constraint set that ensures the functionality of the generated room layouts in addition to conforming to architectural guidelines. We solve this problem efficiently using a variant of simulated annealing. We present results for two real-world hospital room types and demonstrate a significant improvement of 18% on average in patient fall risk when compared with a traditional hospital room layout and 41% when compared with randomly generated layouts.
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Paper Nr: 18
Title:

Discounted Markov Decision Processes with Fuzzy Rewards Induced by Non-fuzzy Systems
Authors:

Karla Carrero-Vera, Hugo Cruz-Suárez and Raúl Montes-de-Oca
Abstract: This paper concerns discounted Markov decision processes with a fuzzy reward function triangular in shape. Starting with a usual and non-fuzzy Markov control model (Hernández-Lerma, 1989) with compact action ´ sets and reward R, a control model is induced only substituting R in the usual model for a suitable triangular fuzzy function R˜ which models, in a fuzzy sense, the fact that the reward R is “approximately” received. This way, for this induced model a discounted optimal control problem is considered, taking into account both a finite and an infinite horizons, and fuzzy objective functions. In order to obtain the optimal solution, the partial order on the α-cuts of fuzzy numbers is used, and the optimal solution for fuzzy Markov decision processes is found from the optimal solution of the corresponding usual Markov decision processes. In the end of the paper, several examples are given to illustrate the theory developed: a model of inventory system, and two others more in an economic and financial context.
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Paper Nr: 20
Title:

Pricing Competition in a Duopoly with Self-adapting Strategies
Authors:

Youri Kaminsky, Tobias Maltenberger, Mats Pörschke, Jan Westphal and Rainer Schlosser
Abstract: Online markets are characterized by highly dynamic price adjustments and steady competition. Many market participants adjust their prices in real-time to changing market situations caused by competitors’ pricing strategies. In this paper, we examine price optimization within a duopoly under an infinite time horizon with mutually unknown strategies. The challenge is to derive knowledge about the opponent’s pricing strategy automatically and to respond effectively. Strategy exploration procedures used to build a data foundation on a competitor’s strategy are crucial in unknown environments and therefore need to be selected and configured with caution. We show how our models explore and exploit a competitor’s price reaction probabilities. Moreover, we verify the quality of our learning approach against optimal strategies exploiting full information. In addition to that, we analyze the mutual interplay of two self-learning strategies. We observe a clear winning party over time as well as that they can also form a cartel when motivated accordingly.
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Paper Nr: 31
Title:

An Adjustable Robust Formulation and a Decomposition Approach for the Green Vehicle Routing Problem with Uncertain Waiting Time at Recharge Stations
Authors:

Luigi Di Puglia Pugliese, Francesca Guerriero and Giusy Macrina
Abstract: We investigate the problem of routing a fleet of electric vehicles in an urban area, which must serve a set of customers within predefined time windows. We allow partial battery recharge to any available recharge stations, located in the area. Since the recharge stations could be busy when a driver arrives for charging operations, the time spent can be seen as the sum of recharge times and waiting times. We model the waiting times as uncertain parameters and we assume to do not know their distribution. Hence, we address the problem under the robust optimization framework by modelling the realization of the uncertain parameter with the budget of uncertainty polytope. We propose an adjustable robust formulation, then we implement a row-and-column generation solution approach based on a two-stage reformulation of the problem, to provide a robust routing against infeasibility with respect to time collect requirements. We test the proposed approach on benchmark instances and we analyze the behavior of the considered transportation system with respect to the uncertain parameter. In addition, we investigate the price of robustness and the reliability of the robust solutions obtained.
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Paper Nr: 54
Title:

Properties and Bounds for the Single-vehicle Capacitated Routing Problem with Time-dependent Travel Times and Multiple Trips
Authors:

T. Adamo, G. Ghiani, P. Greco and E. Guerriero
Abstract: This paper deals with a problem where the same vehicle performs several routes to serve a set of customers and arc traversal times vary along the planning horizon. The relationship with its time-invariant counterpart is investigated and a procedure to compute lower and upper bounds on the optimal solution value is developed. Computational results on instances, based on the Paris (France) road graph, show the effectiveness of this approach.
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Paper Nr: 61
Title:

A LP Relaxation based Matheuristic for Multi-objective Integer Programming
Authors:

Duleabom An, Sophie N. Parragh, Markus Sinnl and Fabien Tricoire
Abstract: Motivated by the success of matheuristics in the single-objective domain, we propose a very simple linear programming-based matheuristic for three-objective binary integer programming. To tackle the problem, we obtain lower bound sets by means of the vector linear programming solver Bensolve. Then, simple heuristic approaches, such as rounding and path relinking, are applied to this lower bound set to obtain high-quality approximations of the optimal set of trade-off solutions. The proposed algorithm is compared to a recently suggested algorithm which is, to the best of our knowledge, the only existing matheuristic method for three-objective integer programming. Computational experiments show that our method produces a better approximation of the true Pareto front using significantly less time than the benchmark method on standard benchmark instances for the three-objective knapsack problem.
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Paper Nr: 62
Title:

Mixed-Integer Constrained Grey-Box Optimization based on Dynamic Surrogate Models and Approximated Interval Analysis
Authors:

Mohamad O. Nachawati and Alexander Brodsky
Abstract: In this paper an algorithmic framework, called GreyOpt, is proposed for the heuristic global optimization of simulations over general constrained mixed-integer sets, where simulations are expressed as a grey-box, i.e. computations using a mix of (1) closed-form analytical expressions, and (2) evaluations of numerical black- box functions that may be non-differentiable and computationally expensive. GreyOpt leverages the partially analytical structure of such problems to dynamically construct differentiable surrogate problems for multiple regions of the search space. These surrogate problems are then used in conjunction with a derivative-based method to locally improve sample points in each region. GreyOpt extends Moore interval arithmetic for approximating the intervals of grey-box objective and constraint functions by fitting quadric surfaces that attempt to roughly underestimate and overestimate embedded black-box functions. This serves as the foundation of a recursive partitioning technique that GreyOpt uses to refine the best points found in each region. An experimental study of GreyOpt’s performance is conducted on a set of grey-box optimization problems derived from MINLPLib, where the ratio of black-box function evaluations to analytical expressions is small. The results of the study show that GreyOpt significantly outperforms three derivative-free optimization algorithms on these problems.
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Short Papers
Paper Nr: 1
Title:

Attrition, Promotion, Transfer: Reporting Rates in Personnel Operations Research
Authors:

Etienne Vincent, Stephen Okazawa and Dragos Calitoiu
Abstract: Rates of personnel flow, such as attrition, promotion and transfer, are widely reported, compared and modelled in Personnel Operations Research (OR). However, different analysts commonly employ different formulas to define these rates. This paper solidifies the foundation of Personnel OR by presenting a theoretically sound formula for personnel flow rates that we will refer to as the general formula. The proposed formula is justified by its properties, but also by analogy with the field of Investment Performance Reporting, where it is known as the Time Weighted Rate of Return. The paper also derives approximation formulas for the rates of personnel flow, and empirically compares them.
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Paper Nr: 4
Title:

Multicriteria Decision Method for Project Ranking Considering Uncertainty
Authors:

Guilherme B. Marcondes
Abstract: Frequently, decision makers face the challenge of selecting projects to be executed. Resources are not enough for funding all of them. In this challenge, they need the support of a tool or method, due to several criteria to be considered simultaneously. Multicriteria decision methods can provide a good support. However, as inherent to all estimation in projects, uncertainty must be addressed. This works proposes a method for incorporating uncertainty in project selection using ELECTRE II method and Monte Carlo simulation.
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Paper Nr: 6
Title:

Multi-Mode RCPSP with Safety Margin Maximization: Models and Algorithms
Authors:

Christian Artigues, Emmanuel Hebrard, Alain Quilliot and Helene Toussaint
Abstract: We study here a variant of the multimode Resource Constrained Project Scheduling problem (RCPSP), which involves continuous modes, and a notion of Safety Margin maximization. Our interest was motivated by a work package inside the GEOSAFE H2020 project, devoted to the design of evacuation plans in face of natural disasters, and more specifically wildfire.
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Paper Nr: 11
Title:

The Impact of Information Geometry on the Analysis of the Stable M/G/1 Queue Manifold
Authors:

Ismail A. Mageed and Demetres D. Kouvatsos
Abstract: Information geometry (IG) seeks to characterize the structure of statistical geodesic models from a differential geometric point of view. By considering families of probability distributions as manifolds with coordinate charts determined by the parameters of each individual model, the tools of differential geometry, such as divergences and metric tensors, provide effective means of studying their characteristics. The research undertaken in this paper presents a novel approach to the modelling study of information geometrics of a queueing system. In this context, the manifold of stable M/G/1queue is characterised from the viewpoint of IG, the Kullback’s divergence (KD) and J-divergence (JD) are determined. Also, it is revealed that the stable M/G/1 queue manifold is developable (i.e., has a zero Gaussian curvature) and has a non-zero Ricci Curvature Tensor (RCT). Unifying IG with Queueing Theory enables the study of dynamics of queueing system from a novel Riemannian Geometry (RG) point of view, leading to the analysis of the stable M/G/1 queue, based on the Theory of Relativity (TR).
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Paper Nr: 14
Title:

Decision Support for Bundle Shipment Choices: Orthogonal Two-dimensional Bin Packing with Practical Constraints
Authors:

Yizi Zhou, Jiyin Liu, Rupal Mandania, Shuyang Li, Kai Chen and Rongjun Xu
Abstract: In this paper, we study a real life application of an orthogonal two dimensional bin packing problem (2D-BPP) with a small assortment of different shapes of bins and items. A telecommunication equipment company ships its products to customers several times daily by trucks and it currently uses the less-than-truckload (LTL) shipping option which is priced as unit price per volume times the total volume of products. However, depending on scale of products to be shipped, full-truckload (FTL) shipping which is priced as a single price per truck may be cheaper. In this paper we aim to help the company to decide on the delivery choice as well as on how to pack the truck optimally when FTL shipping is selected. We model the problem as a variant of an orthogonal 2D-BPP using mixed-integer linear programming (MIP) with the objective to minimise the total cost of delivery. Practical conditions influencing the feasibility of packing patterns are also considered. Practical guidance such as those on some modelling techniques and the application of the model, are applied to enhance the solving efficiency. The problems are solved by commercial solver CPLEX to optimality up to 34 pallets within an acceptable time. For lager problems, we adopt an approach to combine same items into larger rectangles and then pack them. This increases the solvability to 154 pallets. On average, our method help the company save 26% shipping cost.
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Paper Nr: 19
Title:

On the Optimal Allocation of Resources for a Marketing Campaign
Authors:

Patrick Hosein, Shiva Ramoudith and Inzamam Rahaman
Abstract: Many companies and institutions, such as banks, typically have a wide range of products that they make available to customers. However, such products must be marketed to their customers, especially when the product is new. Phone calls, emails, postal mail, and online advertisements are among the ways companies can market products to specific customers. However, the cost incurred during marketing increases with every contact made. Phone calls are the most personal means of targeted marketing but also the most costly. In telemarketing, a company can make multiple calls to a single customer with each call incurring a human resource cost. Such calls may or may not be able to persuade a customer to subscribe to the service or product. Some customers might subscribe after the first call. Some customers might require several calls to convince them. Other customers might never be persuaded. In light of limited resources, to maximize return, a company would need to determine which customers to contact and how many attempts to make for a customer. In this paper, we present a mathematical model for this problem in which, given a marketing budget of calls, one can determine a policy for selecting customers to target along with the optimal number of calls to use for each selected customer. We illustrate our model using a Portuguese banking dataset and show that our model can achieve significantly higher levels of success performance.
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Paper Nr: 22
Title:

Storage Fees in a Container Yard with Multiple Customer Types
Authors:

Yahel Giat
Abstract: We consider a small container storage yard that is located in the outskirts of a marine shipping port. Customers are classified according to the number of storage space they need. The yard has a limited number of storage spots, and serves customers only if there is sufficient space to fulfill their needs. We define the yard’s state space and derive its steady-state probability matrix for Markovian arrivals and service times. We consider the profit function of two services that the yard offers its customers. In the first, storage fees are independent of the storage duration and in the second they are proportional to it. We show when these pricing schemes are equivalent and demonstrate numerically how profits depend on customer arrivals rates and yard size.
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Paper Nr: 25
Title:

Machine Learning and Optimization for Predictive Maintenance based on Predicting Failure in the Next Five Days
Authors:

Eman Ouda, Maher Maalouf and Andrei Sleptchenko
Abstract: This study proposes a framework to predict machine failures using sensor data and optimize predictive/corrective maintenance schedule. Using historical data, machine learning (ML) models are trained to predict the failure probabilities for the next five days. Multiple algorithms, including feature extraction techniques, selections, and ML models (both regression and classification based) are compared. The machine learning models’ output is fed to an optimization model to propose an optimized maintenance policy, and we demonstrate how prediction models can help increase system reliability at lower costs.
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Paper Nr: 39
Title:

Analysing the Risk Propagation in the Project Portfolio Network using the SIRF Model
Authors:

Xingqi Zou, Qing Yang and Qinru Wang
Abstract: Due to the existence of dependencies among the projects, the risk in one project will cause risks in other projects, which will lead to the risk propagation in the portfolio network. To measure the criticality of projects in the portfolio considering risk propagation, the paper builds the risk analysis model using the complex network and SIRF model. Firstly, we build the network of the project portfolio based on the analysis of the independency among projects, then we propose the integrated project criticality measurement (IPCM) algorithm based on the complex network theory. The IPCM algorithm integrates the K-shell, eigenvector centrality and the neighbour nodes in the complex network to analyse the project criticality. Furthermore, the link entropy is used to calculate the influence of the project in the network. On this basis, combined with the practice of R&D project management, the SIRF (susceptible-infected-recovered-failed) model is proposed to analyse the dynamic propagation process of the risk in the project portfolio network. Then the priority ranking of the project portfolio is realized under the dynamic risk propagation. Finally, a representative example is provided to illustrate the validity of proposed models.
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Paper Nr: 41
Title:

Measuring Sustainability Performance in the Product Level
Authors:

Qinru Wang, Qing Yang and Mingxing Chang
Abstract: Sustainability is becoming increasingly important in new product development as modern society demands technicality, customer satisfaction, and economic efficiency during a product’s life cycle. Previous papers have commented more on the environmental aspects of sustainability in project management, whereas less attention has been paid to the measurable indicators of products. The knowledge about the product process structure is beginning to use sustainability indicators as part of the approach. Based on this idea, this paper suggests a combination of measurable indicators of sustainability that can be used in the product process structure of the construction industry. The aim is to identify a product process structure that is compatible with sustainable project management. The idea of product design structure matrix (DSM) will be introduced to identify the sustainability of products. By analysing the different dimensions of the measurable indicators, the sustainable products can be compared. This provides an integrated view of the product process structure when developing new products. This approach will then be applied to the smartphone industry as an illustrate example, which will provide ideas to improve the sustainability of new smartphone development.
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Paper Nr: 43
Title:

Towards Distributed Local Search Through Neighborhood Combinators
Authors:

Gustavo Ospina and Renaud De Landtsheer
Abstract: This paper presents an approach for making local search algorithms distributed, to get speed improvements thanks to the growth both in multi-core hardware and the massive availability of distributed computing power, notably in the cloud. Local search algorithms rely on the exploration of neighborhoods on a given solution space model. Our distribution approach relies on the exploration of multiple neighborhoods in parallel, performed by different workers located on different CPU cores (locally or distributed). This approach relies on neighborhood combinators, which are composite neighborhoods built out of basic neighborhoods. Combinators allows us to introduce metaheuristics (restart, tabu search, simulated annealing), neighborhood selection (hill-climbing, round-robin) and handle search strategies. We propose some parallel search combinators that can be instantiated to build search strategies encompassing parallel neighborhood exploration. An implementation is proposed in the OscaR.cbls framework, using the Actor model of computation.
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Paper Nr: 46
Title:

A Cooperative Market-based Decision Guidance Approach for Resilient Power Systems
Authors:

Alexander Brodsky, Eric Osterweil and Roberto Levy
Abstract: National and local economies are strongly dependent on stable power systems. While the problem of power system resilience in the face of natural disasters and terrorist attacks has been extensively studied from the systems engineering perspective, a major unsolved problem remains in the need for preventive solutions against the collapse of power systems. These solutions must ensure the most economically efficient operation of power systems, within the bounds of any remaining power capacity. Transferring power usage rights from the lowest-loss to the highest-loss entities would result in significant reduction of the combined loss. The existing power systems do not take this fact into account. To address this need, we envision a paradigm shift toward three-step system for (1) a cooperation power market where power usage rights can be transferred among participating entities, (2) decision guidance to recommend market asks and bids to each entity, and (3) optimization that, given the market clearance, will recommend precise operational controls for each entity’s microgrid. The key challenge to address is the design of this three-step market system that will guarantee important properties including Pareto-optimality, individual rationality, and fairness, as well as privacy, security, pseudo-anonymity and non-repudiation.
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Paper Nr: 48
Title:

Catalyzing the Agility, Accessibility, and Predictability of the Manufacturing-Entrepreneurship Ecosystem through Design Environments and Markets for Virtual Things
Authors:

Alexander Brodsky, Yotam Gingold, Thomas D. LaToza, Lap-Fai Yu and Xu Han
Abstract: Proposed is a fundamentally new approach to manufacturing as a service based on a market of virtual things: parameterized products and services that can be searched, composed and optimized, while hiding the underlying complexity of product designs and manufacturing service networks. The approach includes (1) a mathematical framework, composition and decision guidance for virtual things; (2) bootstrapping the market with novel computational techniques and tools to reuse the distributed wealth of existing product and process designs by generalizing them into models of virtual things; and, (3) intelligent computational design tools for entrepreneurs. The goal is to catalyze the agility, accessibility and predictability of the manufacturing-entrepreneurship ecosystem, transforming the Future of Manufacturing.
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Paper Nr: 51
Title:

A Very Large Scale Neighborhood Approach to Pickup and Delivery Problems with Time Windows
Authors:

Renaud De Landtsheer, Thomas Fayolle, Fabian Germeau and Gustavo Ospina
Abstract: When solving optimisation problem in the presence of strong constraints, local search algorithms often fall into local minima. One approach that helps escaping local minima is using Very Large Scale Neighborhoods (VLSN). We applied VLSN to the pick-up and delivery problem with time windows (PDPTW). Unfortunately, VLSN can be rather slow: it requires to build a large graph of moves (the VLSN graph) and explore the graph to identify cycles in it. Most of the execution time is spent building the graph. Once a cycle is found, large parts of the graph are invalidated, hence the time spent building these portions of the graph is wasted. We introduce a generic speed improvement of the VLSN algorithm. The idea is to build the VLSN graph gradually and to perform the cycle search regularly throughout the construction of the graph, so that if the searched cycles are discovered, large portions of the graph under construction are not explored at all. Roughly speaking, on a specific standard PDPTW instance (LC1 2 1), only 43% of the graph needs to be built, and the sped up VLSN procedure only takes 53% of time taken by the original one.
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Paper Nr: 53
Title:

A Dynamic Dial-a-ride Model for Optimal Vehicle Routing in a Wafer Fab
Authors:

Claudio Arbib, Fatemeh Kafash Ranjbar and Stefano Smriglio
Abstract: We consider the problem of moving production lots within the clean room of LFoundry, an important Italian manufacturer of micro-electronic devices. The problem is modeled as dial-a-ride in a dynamic environment with the objective of makespan minimization. This general objective is achieved by minimizing, at each optimization cycle, the largest completion time among the vehicles, so as to locally balance their workloads. A cluster-first route-second heuristic is devised for on-line use and compared to the actual practice through a computational experience based on real plant data.
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Paper Nr: 58
Title:

Stochastic Optimization Algorithm based on Deterministic Approximations
Authors:

Mohan Krishnamoorthy, Alexander Brodsky and Daniel A. Menascé
Abstract: We consider steady-state production processes that have feasibility constraints and metrics of cost and throughput that are stochastic functions of process controls. We propose an efficient stochastic optimization algorithm for the problem of finding process controls that minimize the expectation of cost while satisfying deterministic feasibility constraints and stochastic steady state demand for the output product with a given high probability. The proposed algorithm is based on (1) a series of deterministic approximations to produce a candidate set of near-optimal control settings for the production process, and (2) stochastic simulations on the candidate set using optimal simulation budget allocation methods. We demonstrate the proposed algorithm on a use case of a real-world heat-sink production process that involves contract suppliers and manufacturers as well as unit manufacturing processes of shearing, milling, drilling, and machining, and conduct an experimental study that shows that the proposed algorithm significantly outperforms four popular simulation-based stochastic optimization algorithms.
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Paper Nr: 7
Title:

Enterprise Architecture Model Transformation Engine
Authors:

Erik Heiland, Peter Hillmann and Andreas Karcher
Abstract: With increasing linkage within value chains, the IT systems of different companies are also being connected with each other. This enables the integration of services within the movement of Industry 4.0 in order to improve the quality and performance of the processes. Enterprise architecture models form the basis for this with a better buisness IT-alignment. However, the heterogeneity of the modeling frameworks and description languages makes a concatenation considerably difficult, especially differences in syntax, semantic and relations. Therefore, this paper presents a transformation engine to convert enterprise architecture models between several languages. We developed the first generic translation approach that is free of specific meta-modeling, which is flexible adaptable to arbitrary modeling languages. The transformation process is defined by various pattern matching techniques using a rule-based description language. It uses set theory and first-order logic for an intuitive description as a basis. The concept is practical evaluated using an example in the area of a large German IT-service provider. Anyhow, the approach is applicable between a wide range of enterprise architecture frameworks.
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Paper Nr: 10
Title:

A Methodology for Deriving Evaluation Criteria for Software Solutions
Authors:

Harald Papp and Marc Hanussek
Abstract: Finding a suited software solution for a company poses a resource-intensive task in an ever-widening market. Software should solve the technical task at hand as perfectly as possible and, at the same time, match the company strategy. Based on these two dimensions, domain knowledge and industry context, we propose a methodology for deriving individually tailored evaluation criteria for software solutions to make them assessable. The approach is formalized as a three-layer model, that ensures the encoding of said dimensions, where each layer holds a more refined and individualized criteria list, starting from a general software-agnostic catalogue we composed. Finally, we exemplarily demonstrate our method for Machine-Learning-as-a-Service platforms (MaaS) for small and medium-sized enterprises (SME).
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Paper Nr: 23
Title:

Integration of Life Cycle Assessment and Data Envelopment Analysis using a Free Disposable Hull Approach to Evaluate Farms’ Eco-efficiency
Authors:

Leonardo Vásquez-Ibarra, Alfredo Iriarte, Ricardo Rebolledo-Leiva, Marcela Gonzalez-Araya and Lidia Angulo-Meza
Abstract: The joint use of Life Cycle Assessment and Data Envelopment Analysis, also known as LCA+DEA, appears as a suitable methodology to evaluate eco-efficiency of multiple units. This methodology has been developed mainly during the last decade, and different methodological aspects has been proposed. However, there are other such as the use of advanced DEA models that have been poorly explored. In this sense, this study seeks to integrate the Free Disposable Hull (FDH) approach into LCA+DEA methodology, applied an agricultural case study. The five-step method is employed to a sample of 37 raspberry producers considering carbon footprint as environmental category. The results indicated that 11 farmers are identified as inefficient for which operational and environmental targets are proposed. The use of FDH model is suitable for the use into the LCA+DEA methodology since it allows to determine one benchmark for inefficient farmers, unlike others models widely used in this methodology, such as BCC, SBM or CCR.
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Paper Nr: 29
Title:

Multi-trip Pickup and Delivery Problem, with Split Loads, Profits and Multiple Time Windows to Model a Real Case Problem in the Construction Industry
Authors:

Atef Jaballah and Wahiba R. Cherif-Khettaf
Abstract: This paper presents the first optimization study of multi-site transportation in the construction industry, which allows mutualizing building material delivery and construction waste removal. This study is inspired by a real-world problem encountered in the framework of the French R&D project DILC, in which a pooling platform must centralize the delivery of building materials to the construction sites and the pickup of their waste, using a limited and heterogeneous fleet that is allowed to perform multiple trips, under time and capacity limitation constraints. The problem under study, called the Multi-Trip Pickup and Delivery Problem, with Split loads, Profits and Multiple Time Windows is a new extension of the vehicle routing problem with pickup and delivery, that considers new realistic constraints specific to the construction industry such as each construction site may have a priority on its delivery request or its pickup request or both, with a higher priority level for delivery request, and each construction site may have several time windows. To solve this problem, we propose new insertion criteria that takes into consideration several aspects of our problem, which we have embedded in a construction heuristic. Experiments performed on new instances have shown the efficiency of our method.
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Paper Nr: 30
Title:

Business Intelligence and Innovation: An European Digital Innovation Hub to Increase System Interaction and Value Co-creation within and among Service Systems
Authors:

Florian Maurer
Abstract: Due to limited resources (e.g. human, financial, knowledge, etc.), Small- and Medium sized Enterprises run the risk to miss the Digital Transformation of its systems. Especially the manufacturing industry – a strategic industry within the European Union that employs millions of workers and provide tremendous Gross Domestic Product to the partner countries – faces technology changes and increased challenges for implementation. As reaction to the challenges of the manufacturing industry, the European Union launched the Factory of the Future programme and the European Digital Innovation Hub programme. Within this article at hand, the literately and empirically endeavours towards the design and development of the Digital Innovation Hub: Business Intelligence & Innovation within the region of Vorarlberg are presented. In doing so, a narrative literature review about the academic discipline of Service Science – the guiding theory to innovate service systems – and an empirical research about the motivation, status quo, vision and strategy towards the Digital Transformation and Industry 4.0 paradigm of the manufacturing industry within the region of the Federal State of Vorarlberg are presented.
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Paper Nr: 38
Title:

Soft Directional Substitutable based Decompositions for MOVCSP
Authors:

Maher Helaoui and Wady Naanaa
Abstract: To better model several artificial intelligence and combinatorial problems, classical Constraint Satisfaction Problems (CSP) have been extended by considering soft constraints in addition to crisp ones. This gave rise to a Valued Constraint Satisfaction Problems (VCSP). Several real-world artificial intelligence and combinatorial problems require more than one single objective function. In order to present a more appropriate formulation for these real-world problems, a generalization of the VCSP framework called Multi-Objective Valued Constraint Satisfaction Problems (MOVCSP) has been proposed. This paper addresses combinatorial optimization problems that can be expressed as MOVCSP. Despite the NP-hardness of general MOVCSP, we can present tractable versions by forcing the allowable valuation functions to have specific mathematical properties. This is the case for MOVCSP whose dual is a binary MOVCSP with crisp binary valuation functions only and with a weak form of Neighbourhood Substitutable Valuation Functions called Directional Substitutable Valuation Functions.
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Paper Nr: 40
Title:

Modeling the Cabin Capacity Allocation Problem in the Cruise Industry: An Italian Case Study
Authors:

Giusy Macrina, Francesca Guerriero and Luigi P. Pugliese
Abstract: In this paper we present several optimization models for cruise cabin capacity allocation. In particular, we address the problem of managing the booking requests for a set of cabins with different type and price in a cruise ship. We formulate three models, considering several features of the problem such as: the limited number of bookable places on the ship, different planning and operation horizon, the possibility to postpone a departure or to apply special offers. Then, we present an Italian case study and we analyze the impact of different strategies on the revenues achievable by the company.
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Paper Nr: 65
Title:

Trends Identification in Medical Care
Authors:

Inês Sena and Ana I. Pereira
Abstract: Daily, health professionals are sought out by patients, motivated by the will to stay healthy, making numerous diagnoses that can be wrong for several reasons. In order to reduce diagnostic errors, an application was developed to support health professionals, assisting them in the diagnosis, assigning a second diagnostic opinion. The application, called ProSmartHealth, is based on intelligent algorithms to identify clusters and patterns in human symptoms. ProSmartHealth uses the Support Vector Machine ranking algorithm to train and test diagnostic suggestions. This work aims to study the application’s reliability, using two strategies. First, study the influence of pre-processing data analysing the impact in the accuracy method when data is previously processed. The second strategy aims to study the influence of the number of training data on the method precision. This study concludes the use of pre-processing data and the number of training data influence the precision of the model, improving the precision on 8%.
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Area 2 - Applications

Full Papers
Paper Nr: 5
Title:

Identifying Geographical Areas using Machine Learning for Enrolling Women in the Canadian Armed Forces
Authors:

Ryuichi Ueno, Peter Boyd and Dragos Calitoiu
Abstract: To improve the visibility of military service as a career option for women, the Canadian Armed Forces (CAF) can tailor marketing campaigns to geographical areas and demographics within Canada that have historically high enrollment of women. To aid in this recruitment strategy, a logistic regression model was developed using historical recruiting data. The score obtained was used to rank Canadian postal codes and to identify the ones with the highest potential for recruiting of women. Additional demographic filtering was applied using marketing segments provided by a vendor. The final top 10% postal codes with the highest probability of women enrollment were clustered based on the collective social media behaviour of each postal code and was binned using the distance to the nearest recruiting centre. Several social media outlets were observed to be of interest, among them YouTube and Snapchat appear as viable options to reach women with a high probability of CAF enrollment.
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Paper Nr: 13
Title:

A Hierarchical Decomposition Approach for the Optimal Design of a District Cooling System
Authors:

Bingqian Liu, Côme Bissuel, François Courtot, Céline Gicquel and Dominique Quadri
Abstract: A district cooling system is a centralized cooling supply system providing air conditioning to a set of buildings located in the same district. Designing and sizing such a system is very complex, as both the initial construction cost and the operation cost of the cooling system during its entire life must be considered. We first propose a modeling approach aiming at formulating this combinatorial optimization problem as a mixed-integer linear program of tractable size. We then extend a previously published hierarchical decomposition technique in order to find the optimal solution in an efficient way. Finally, we provide preliminary computational results based on a real-life case study located in China.
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Paper Nr: 15
Title:

Development of a Purchasing Portfolio Model for the Health Sector: A Case Study of a Central Hospital
Authors:

Amílcar Arantes and Andreia F. Alhais
Abstract: Over the years, the purchasing area has taken on an essential role in the management of companies in all activity sectors. In the health sector, purchasing medicines is highly important considering the amounts involved, the impact on the service quality, and the wide variety of purchased products. This research work combined action research with a case study and aims to apply to a Central Hospital a Purchasing Portfolio Model based on the Kraljic Purchasing Matrix (KPM). The KPM allows for the classification of different classes of medicines in accordance with their impact on profits and supply risk dimensions, making it possible to define differentiated purchasing strategies. This application used the Analytical Hierarchical Process (AHP) tool for criteria prioritization and used direct measurement for criterion rating. By applying the model to a Central Hospital, this study seeks to increase the areas of applicability of purchasing portfolio models. Moreover, the results confirmed the model's value in defining medicine purchasing strategy at the Central Hospital and also gave rise to guidelines for applying the model.
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Paper Nr: 17
Title:

Decision Guidance Framework for a Hybrid Renewable Energy System Investment Model
Authors:

Roberto Levy and Alexander Brodsky
Abstract: This paper focuses on making optimal investment and operational recommendations for a Hybrid Renewable Energy System (HRES). For this purpose we develop a modular composite analytic performance model for HRES investment, which is based on an extensible library of atomic component models, including renewable sources such as solar and wind, power storage, power contracts, and programmable customer loads’ switches. The performance model formally expresses feasibility constraints and key performance indicators, including total tost of ownership, environment impact, and infrastructure resilience, as a function of investment and operational decision variables. Based on the performance model, we design and develop a decision guidance system to enable actionable investment recommendations that optimize key performance indicators subject to the operational constraints associated with the network. Finally, we demonstrate the model in a case study based on a real world example for a municipal electric utility.
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Paper Nr: 21
Title:

Using Goal Directed Techniques for Journey Planning with Multi-criteria Range Queries in Public Transit
Authors:

Arthur Finkelstein and Jean-Charles Régin
Abstract: One of the main problems for a realistic journey planning in public transit is the need to give the user multiple qualitative choices. Usually, public transit journeys involve 4 main criteria: the departure time, the arrival time, the number of transfers and the walking distance. The problem of computing Pareto sets with these criteria is called the Pareto range query problem. This problem is complex and difficult to solve within the constraints of the industrial world of smartphone applications, like a response time of the order of a second. In this paper, we present the Goal Directed Connection Scan Algorithm (GDCSA), an algorithm that allows, for the first time, to solve this problem with run times of less than 0.5 seconds on most European city or country-wide networks, like Berlin or Switzerland. In addition, GDCSA satisfies other industrial needs: it is conceptually simple and easy to implement. It partitions the graph in geographically small areas and precomputes some lower bounds on the duration of a trip in order to select for each itinerary a sub-set of these areas to decrease the number of scanned connections. Combining this sub-set and a journey planning using 4 criteria, the number of scanned connections is lowered by a factor of up to 17 times compared to the best algorithms (CSA and RAPTOR), the number of nodes opened during the search is lowered by a factor of up to 2.9 and the query times are lowered by a factor of up to 9 on metropolitan networks. The integration of GDCSA in a smartphone app backend server led to an improvement in results by a factor of 5.
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Paper Nr: 27
Title:

Determining the Required Size of a Military Training Pipeline
Authors:

Etienne Vincent and Michelle Straver
Abstract: This paper addresses the problem of deciding how many positions to set aside, in a military establishment, for recruits undergoing training. We assume a cap on total strength, and thus must select a ratio between positions in the force’s training pipeline versus its trained establishment. We develop a Markovian model of the training pipeline, with parameters derived from historical Human Resources data. Through Monte Carlo simulation we may then predict how often a given ratio will be sufficient to generate the required trained force, as well as how much surplus trained personnel it is expected to generate. Our modelling results have informed ongoing initiatives to optimize the force mix and structure of the Canadian Armed Forces.
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Paper Nr: 50
Title:

Interpretive Structural Model-based for Analysis of Causes of Delays in Construction Projects: The Portuguese Case
Authors:

Amílcar Arantes and Luis F. Ferreira
Abstract: Delays are a common issue in construction projects worldwide, and they can frequently have an influence on time and cost overruns, among other problems. This study aims to add to the knowledge on construction project management theory and practice by identifying the leading causes of Delays in Construction Projects (DCP) in Portugal, modeling their interrelationships, and determining their main causes. The study presented herein adopted a two-phase methodology. First, based on the literature, the causes of DCP in Portugal are identified. Then the hierarchical structure of the causes of DCP is determined, using integrated Interpretive Structural Modelling (ISM), and an ISM-based Model is developed. The results show that the 16 causes of DCP taken into consideration are hierarchized in six different influence levels. The causes Bidding and contract award process and Lack of communication between parties are the most influential causes, and are thus considered to be the root causes of DCP in Portugal. Additionally, the results show that the causes of DCP can be divided into four different categories relating to Relationships and contract, Material, the Developer, and the Contractor. Finally, these results provide fundamental insights for practitioners and researchers to develop effective measures to mitigate the causes of DCP.
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Paper Nr: 63
Title:

An Optimization Model to Help Cruise Companies to Evaluate their Offer in a Basin
Authors:

Daniela Ambrosino and Veronica Asta
Abstract: This work focuses on the problem of evaluating and improving a current offer, when the market changes due to either new customers demands or new actions of competitors. A model for evaluating the current offer of cruise itineraries is proposed. A case study related to the Mediterranean sea cruise offer is presented. Offers have been compared in terms of demand satisfaction, revenue, costs, accessibility and appealing values that are important information that can help the decision maker to choose the best offer in accordance with the trend of the demand, the future business development and the future companys strategies. The current situation due to COVID-19 pandemic represents an extreme case that poses new decision problems and requires different analysis due to very high level of uncertainty. The present paper can furnish useful insights for facing some of the emerging decision problems but it is dedicated to help decision makers in evaluating the cruise offer when changes occur in a single market or in a limited area.
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Short Papers
Paper Nr: 3
Title:

Bi-objective Optimization Model for Determining Shelter Location-allocation in Humanitarian Relief Logistics
Authors:

Panchalee Praneetpholkrang, Van N. Huynh and Sarunya Kanjanawattana
Abstract: Shelter location-allocation is very important since it affects victims’ security and the performance of the relief supply chain. This paper proposes a bi-objective optimization model for justifying locations of shelters and allocating appropriate shelters to all affected areas efficiently and effectively. The first objective seeks to minimize the total cost, and the second objective attempts to minimize the average victim evacuation time. The Epsilon Constraint method is employed to deal with the bi-objective function. To avoid the inefficient solutions problem, which is likely generated by the Epsilon Constraint approach, the transformed constraint is augmented by the positive constant value of allowance time. A case study of the flood in Phun Phin, Surat Thani, Thailand is used to demonstrate the application of the proposed model. The results obtained from this study could help decision-makers to determine an effective shelter location-allocation plan as well as, more broadly, to develop a disaster management strategy.
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Paper Nr: 9
Title:

Effectiveness of Comments on Self-reflection Sheet in Predicting Student Performance
Authors:

Rumiko Azuma
Abstract: In recent years, schools and universities have become more focused on how to allow learners to learn successfully, and it has become an expectation to design instruction in a way that takes into account the individual differences of learners. Accordingly, the purpose of this study is to predict, at an earlier stage in a course, which students are likely to fail, so that adequate support can be provided for them. We proposed a new approach to identify such students using free-response self-reflection sheets. This method uses the unrestricted comments from the students to create a comment vector that can be used to predict who are likely to fail the course. Subsequently, we conducted experiments to verify the effectiveness of this prediction. In comparison to methods used in existing research which predict potential failures using quiz scores and the students’ subjective level of understanding, our proposed method was able to improve the prediction performance. In addition, when cumulative data after several sessions were used to predict which students were likely to fail, the predictions made by the support vector machine (SVM) algorithm showed a consistent prediction performance, and the prediction accuracy was higher than that of other algorithms.
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Paper Nr: 34
Title:

Mathematical Model for Estimating Nutritional Status of the Population with Poor Data Quality in Developing Countries: The Case of Chile
Authors:

Denisse Ávalos, Cristóbal Cuadrado, Jocelyn Dunstan, Javier Moraga-Correa, Luis Rojo-González, Nelson Troncoso and Óscar C. Vásquez
Abstract: Obesity is one of the most important risk factors for non-communicable diseases. Nutritional status is generally measured by the body mass index (BMI) and its estimation is especially relevant to analyse long-term trends of overweight and obesity at the population level. Nevertheless, in most context nationally representative data on BMI is scarce and the probability of individuals to progress to obese status is not observed longitudinally. In the literature, several authors have addressed the problem to obtain this estimation using mathematical/computational models under a scenario where the parameters and transition probabilities between nutritional states are possible to compute from regular official data. In contrast, the developing countries exhibit poor data quality and then, the approaches provided from the literature could not be extended to them. In this paper, we deal with the problem of estimating nutritional status transition probabilities in settings with scarce data such as most developing countries, formulating a non-linear programming (NLP) model for a disaggregated characterization of population assuming the transition probabilities depend on sex and age. In particular, we study the case of Chile, one of the countries with the highest prevalence of malnutrition in Latin America, using three available National Health Surveys between the years 2003 and 2017. The obtained results show a total absolute error equal to 5.11% and 10.27% for sex male and female, respectively. Finally, other model applications and extensions are discussed and future works are proposed.
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Paper Nr: 37
Title:

Analysis of the Hiring Cost Impact with a Bi-objective Model for the Multi-depot Open Location Routing Problem
Authors:

Joel-Novi Rodríguez-Escoto, Samuel Nucamendi-Guillén and Elias Olivares-Benitez
Abstract: This paper investigates the effect of the hiring cost over transportation cost and the capacity utilization for the vehicles used. This analysis is conducted on a multi-depot open location-routing problem. The problem consists of determining the optimal number of depots to open, as well as the design of the open routes in order to satisfy the demand for all of the customers while seeking the best trade-off between the total traveling and opening cost. To solve the problem, we propose a bi-objective mixed-integer linear model, which is solved using two different approaches: the augmented epsilon constraint 2 (AUGMECON2) method and the weighting revised multi-choice goal programming (WRMCGP) method. Both approaches are implemented, solving benchmark instances and comparing the quality of the Pareto fronts in terms of multi-objective metrics. Accordingly, the results indicate that AUGMECON2 performs better than WRMCGP concerning the quality of the Pareto Front and the elapsed CPU time, for instances with a homogeneous fleet. However, the WRMCGP reported the best solution time in the heterogeneous instances. In summary, considering heterogeneous fleets, the results demonstrate that the hiring cost can be reduced up to 85%, with 73% more vehicle utilization on average.
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Paper Nr: 42
Title:

A Data-thrifty Approach to Routing Optimization
Authors:

Thomas Fayolle, Renaud De Landtsheer, Gustavo Ospina and Fabian Germeau
Abstract: Solving a routing optimisation problem often requires to know the travel times between each pair of points of the problem. Usually, when solving a routing optimisation problem, the travel time is assumed to be constant. However, in real life problems, it can vary a lot due to traffic jams, especially near big cities. Some map data providers can provide accurate travel time estimations, but the data are expensive and querying such a data provider is time consuming. In this paper, we present a method to solve the Pickup and Delivery Problem with Time Windows (PDPTW) that reduces the number of calls to paid data providers while preserving the quality of the solution.
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Paper Nr: 55
Title:

Towards Blockchain-based Ride-sharing Systems
Authors:

Edgar Vazquez and Dario Landa-Silva
Abstract: Blockchain technology has been used in finance, health care, supply chain, and transport, with the main goals of improving security and eliminating the need for a third party to manage transactions in the system. In blockchain, smart contracts are used to facilitate negotiation between stakeholders. The sharing economy movement has gained popularity in recent years in various sectors including transport. Ride-sharing has become an important component of sustainable transportation by increasing vehicle utilisation and reducing the number of vehicles on the road. Current ride-sharing systems are centralised with an intermediary maintaining users’ data and managing transactions between drivers and passengers. This paper proposes the use of blockchain and in particular smart contracts, to develop decentralised ride-sharing systems. The benefits of having a distributed approach to maintaining users’ data and managing transactions between users include more automation, more transparency, better data privacy, and possibly more trust between users.
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Paper Nr: 56
Title:

Flexibility in Home Delivery by Enabling Time Window Changes
Authors:

F. Phillipson and E. A. van Kempen
Abstract: To enhance the perceived quality of home delivery services more and more flexibility is offered to the receivers. Enabling same-day delivery, communicating predefined narrow time windows, choosing the time windows and alternative address delivery add to the receiver’s experience, making the delivery company an interesting partner for parcel-shipping companies. A new flexibility is the possibility to change the chosen or communicated time window by the receiver during the day of delivery. In this paper we investigate the effect on delivery costs of this flexibility. Receivers are allowed to change their time window until the start of the old or new (the earliest) time window. In this paper two situations are investigated: one situation where communicated time window is a result of the planning process (Time Indication) and one situation where the original time window was already chosen by the receiver (Time choice). We show that the costs rises quickly in the Time Indication case when the percentage of time window changes grow. The Time Choice case is more costly at the start, but time window changes can be handled without (too much) extra costs. However, here a higher percentage of parcels is delivered outside the time windows.
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Paper Nr: 59
Title:

Location Techniques for the Design of a Walking Aid Network for Visual Impaired Students
Authors:

Chefi Triki
Abstract: Universities may have students with visual impairments that require support and facilities in order to ensure for help them adequate educational experience. Fortunately, many universities have already adopted particular solutions for those students by providing supportive services and advanced technologies. However, other campuses are still left behind and, thus, special solutions are still to be implemented. This position paper represents a preliminary study in this direction for a case-study university campus in Oman. It attempts to employ the location optimization techniques to design a walking aid network with rest lounges for students who are blind or suffer from a visual impairment. The walking network will connect the several classrooms, the administrative offices and the service structures that these students frequently visit during their academic day. The walking network will be equipped by tactile tiles and has, thus, budget and execution time restriction for its implementation. For this purpose, our approach will consist in selecting a subset of the available routes to be used for the tactile tiles installation purpose and also in developing the project management technique that allows to complete the project in the minimum span time. Preliminary results on the implementation of such techniques in the Omani University campus will be discussed.
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Paper Nr: 66
Title:

A Regeneration Placement, Routing and Spectrum Assignment Solution for Translucent Elastic Optical Networks: A Joint Optimization Approach
Authors:

Claudio González, Nicolás Jara and Víctor M. Albornoz
Abstract: In this paper, we propose a novel joint approach to solve the regeneration placement, routing, modulation level, and spectrum assignment (RP-RMLSA) problem using a binary integer program (BIP) model. Using a mock and real-world network topology, we conduct extensive numerical experiments testing the proposed optimization model’s performance and analyzing the characteristics of the solutions found. Our results show that considering only an optimal solution occurs when signals in need of regeneration are concentrated in one regeneration node when considering the regeneration devices’ capital and operational expenditure.
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Paper Nr: 26
Title:

Logical Workflow Analysis based on Multiple Criteria Decision Analysis: Industrial Application for a Make to Order Environment

Authors:

Soukaina Oujana, Lionel Amodeo and Farouk Yalaoui
Abstract: The aim of this study is to analyze the possible application of multiple criteria decision approach for a relevant workflow analysis in a French printing company. An important result and contribution of this paper will be precisely to show that MCDA could be an important decision tool to analyze and identify the main stream for make to order environment. An overview of the logical structure of the process on how things actually operate on the production, is presented. To summarize, this paper demonstrates and presents a new approach for using MCDA for a workflow analysis and discusses its application in a real case study where production faces various variations.
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Paper Nr: 36
Title:

A Decision Guidance System for Optimal Operation of Hybrid Power Desalination Service Network
Authors:

Bedor Alyahya and Alexander Brodsky
Abstract: While modeling and optimization of desalination systems’ operation have been extensively studied, current approaches are hard-wired to specific designs and performance metrics, without the flexibility to reuse or extend these models. Bridging this gap, reported in this paper is the development of a formal analytic model and a decision guidance system for desalination service networks that can be applied to a broad range of desalination designs and architectures. The model and the system are based on an extensible repository of atomic component models, initially including models for pumps, renewable energy sources, water and power storage, and reverse osmosis units. An experimental study is conducted to demonstrate the flexibility of the model and system, and its scalability to support realistic size problems.
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Paper Nr: 52
Title:

Multi-product, Multi-supplier Order Assignment and Routing for an e-Commerce Application in the Retail Sector
Authors:

Louis Rivière, Christian Artigues, Azeddine Cheref, Nicolas Jozefowiez, Marie-José Huguet, Sandra U. Ngueveu and Vincent Charvillat
Abstract: With the rise of virtualization, the share of e-commerce in the retail market continues to grow in an omnichannel context. We consider an existing software tool, developed by the Devatics company, for pooling inventories in stores to meet online orders. The problem which arises therefore consists in seeking the optimal allocation of a set of customers to stores. In this paper we consider a variant of the offline problem corresponding to an evolution of the existing software, consisting of assigning a set of predefined orders when the transportation cost depends on a delivery tour to the customer locations. We show that the problem corresponds to a vehicle routing problem with additional but standard attributes. A mixed-integer linear programming formulation is given and several heuristics are proposed : a giant tour-based genetic algorithm, a simple cluster-first, route second heuristic and an assignment-based genetic algorithm. Preliminary computational results on a set of realistic problem instances suggest that the assignment-based genetic algorithm better scales as the problem size increases.
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