Interuniversity Research Center on Enterprise Networks

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Interuniversity Research Center on Enterprise Networks

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Charles V.,The University of Buckingham | Cornillier F.,Interuniversity Research Center on Enterprise Networks
Expert Systems with Applications | Year: 2017

This article examines the potential benefits of solving a stochastic DEA model over solving a deterministic DEA model. It demonstrates that wrong decisions could be made whenever a possible stochastic DEA problem is solved when the stochastic information is either unobserved or limited to a measure of central tendency. We propose two linear models: a semi-stochastic model where the inputs of the DMU of interest are treated as random while the inputs of the other DMUs are frozen at their expected values, and a stochastic model where the inputs of all of the DMUs are treated as random. These two models can be used with any empirical distribution in a Monte Carlo sampling approach. We also define the value of the stochastic efficiency (or semi-stochastic efficiency) and the expected value of the efficiency. © 2017 Elsevier Ltd


Jin J.,Molde University College | Crainic T.G.,Interuniversity Research Center on Enterprise Networks | Lokketangen A.,Molde University College
Computers and Operations Research | Year: 2014

This paper introduces a cooperative parallel metaheuristic for the capacitated vehicle routing problem. The proposed metaheuristic consists of multiple parallel tabu search threads that cooperate by asynchronously exchanging best-found solutions through a common solution pool. The solutions sent to the pool are clustered according to their similarities. The search history information identified from the solution clusters is applied to guide the intensification or diversification of the tabu search threads. Computational experiments on two sets of large-scale benchmark instance sets from the literature demonstrate that the suggested metaheuristic is highly competitive, providing new best solutions to ten of those well-studied instances. © 2013 Elsevier Ltd. All rights reserved.


Coelho L.C.,Interuniversity Research Center on Enterprise Networks | Coelho L.C.,Laval University | Laporte G.,Interuniversity Research Center on Enterprise Networks | Laporte G.,HEC Montréal
Computers and Operations Research | Year: 2014

In this paper we analyze the optimal joint decisions of when, how and how much to replenish customers with products of varying ages. We discuss the main features of the problem arising in the joint replenishment and delivery of perishable products, and we model them under general assumptions. We then solve the problem by means of an exact branch-and-cut algorithm, and we test its performance on a set of randomly generated instances. Our algorithm is capable of computing optimal solutions for instances with up to 30 customers, three periods, and a maximum age of two periods for the perishable product. For the unsolved instances the optimality gap is always small, less than 1.5% on average for instances with up to 50 customers. We also implement and compare two suboptimal selling priority policies with an optimized policy: always sell the oldest available items first to avoid spoilage, and always sell the fresher items first to increase revenue. © 2014 Elsevier Ltd.


Remli N.,Interuniversity Research Center on Enterprise Networks | Remli N.,Laval University | Rekik M.,Interuniversity Research Center on Enterprise Networks | Rekik M.,Laval University
Transportation Research Part C: Emerging Technologies | Year: 2013

Combinatorial auctions are widely used for the procurement of transportation services. In these auctions, shippers act as auctioneers who need to outsource a number of transportation services to external carriers. Carriers compete by submitting bids on packages of shippers' requests. After receiving all carriers' bids, the shipper solves the well-known winner determination problem (WDP) in order to determine winning bids. This paper considers the WDP in a context where shipment volumes are not known with certainty. Based on the bi-level characteristic of the problem, a 2-stage robust formulation is proposed and solved using a constraint generation algorithm. Experimental results show a good performance of the proposed approach. We also evaluate, through an experimental analysis, the benefits of considering a robust rather than a deterministic WDP. © 2013 Elsevier Ltd.


Coelho L.C.,Interuniversity Research Center on Enterprise Networks | Coelho L.C.,Laval University | Laporte G.,Interuniversity Research Center on Enterprise Networks | Laporte G.,HEC Montréal
International Journal of Production Economics | Year: 2014

Inventory-routing problems (IRP) combine inventory control and vehicle routing, effectively optimizing inventory and replenishment decisions over several periods at a centralized level. In this paper we provide an exact formulation which includes several well-known valid inequalities for some classes of IRPs. We then propose three new valid inequalities based on the relation between demand and available capacities. Then, following an idea proposed for the binary clustering and for the job scheduling problems, we also show how the order of the input data can have a major effect on the linear relaxation of the proposed model for the IRP. Extensive computational experiments confirm the success of our algorithm. We have used two available datasets with new solutions identified as recently as 2013. On one set of benchmark instances with 249 open instances, we have improved 98 lower bounds, we have computed 96 new best known solutions, and we have proved optimality for 11 instances. On the other dataset composed of larger instances, of which were 63 open, we have improved 32 lower bounds, we have obtained 20 new best known solutions, and we proved optimality for three instances. © 2014 Elsevier B.V. All rights reserved.


Klibi W.,Interuniversity Research Center on Enterprise Networks | Martel A.,Interuniversity Research Center on Enterprise Networks | Martel A.,Laval University
International Journal of Production Economics | Year: 2012

This paper studies various modeling approaches to design resilient supply networks (SN) for the locationtransportation problem under uncertainty. The future environment of the SN is shaped by random demands, and by disruptions perturbing depots capacity and ship-to-point demand processes. The paper proposes several stochastic programming models incorporating alternative resilience seeking formulations. A generic approach to model SN disruptions, and to elaborate and evaluate SN designs is also proposed. Experiments are made to compare the SN design models formulated, and recommendations are drawn on the approach to use to design effective and robust supply networks. © 2011 Elsevier B.V. All Rights Reserved.


Soro I.W.,Interuniversity Research Center on Enterprise Networks | Nourelfath M.,Interuniversity Research Center on Enterprise Networks | Ait-Kadi D.,Interuniversity Research Center on Enterprise Networks
Reliability Engineering and System Safety | Year: 2010

In this paper, we develop a model for evaluating the availability, the production rate and the reliability function of multi-state degraded systems subjected to minimal repairs and imperfect preventive maintenance. The status of the system is considered to degrade with use. These degradations may lead to decrease in the system efficiency. It is assumed that the system can consecutively degrade into several discrete states, which are characterized by different performance rates, ranging from perfect functioning to complete failure. The latter is observed when the degradation level reaches a certain critical threshold such as the system efficiency may decrease to an unacceptable limit. In addition, the system can fail randomly from any operational or acceptable state and can be repaired. This repair action brings the system to its previous operational state without affecting its failure rate (i.e., minimal repair). The used preventive maintenance policy suggests that if the system reaches the last acceptable degraded state, it is brought back to one of the states with higher efficiency. Considering customer demand as constant, the system is modeled as a continuous-time Markov process to assess its instantaneous and stationary performance measures. A numerical example is given to illustrate the proposed model. © 2009 Elsevier Ltd. All rights reserved.


Laporte G.,Interuniversity Research Center on Enterprise Networks | Pascoal M.M.B.,University of Coimbra | Pascoal M.M.B.,Polytechnic Institute of Coimbra
Computers and Operations Research | Year: 2010

The minimum cost path problem with relays (MCPPR) consists of finding a minimum cost path from a source to a destination, along which relay nodes are located at a certain cost, subject to a weight constraint. This paper first models the MCPPR as a particular bicriteria path problem involving an aggregated function of the path and relay costs, as well as a weight function. A variant of this problem which takes into account all three functions separately is then considered. Formulating the MCPPR as a part of a bicriteria path problem allows the development of labeling algorithms in which the bound on the weight of paths controls the number of node labels. The algorithm for this constrained single objective function version of the problem has a time complexity of O(WmWnlog(maxW,n)), where n is the number of nodes, m is the number of arcs and W is the weight upper bound. Computational results on random instances with up to 10 000 nodes and 100 000 arcs, are reported. © 2010 Elsevier Ltd. All rights reserved.


Lei H.,National University of Defense Technology | Laporte G.,HEC Montréal | Laporte G.,Interuniversity Research Center on Enterprise Networks | Guo B.,National University of Defense Technology
Computers and Operations Research | Year: 2011

The capacitated vehicle routing problem with stochastic demands and time windows is an extension of the capacitated vehicle routing problem with stochastic demands, in which demands are stochastic and a time window is imposed on each vertex. A vertex failure occurring when the realized demand exceeds the vehicle capacity may trigger a chain reaction of failures on the remaining vertices in the same route, as a result of time windows. This paper models this problem as a stochastic program with recourse, and proposes an adaptive large neighborhood search heuristic for its solution. Modified Solomon benchmark instances are used in the experiments. Computational results clearly show the superiority of the proposed heuristic over an alternative solution approach. © 2011 Elsevier Ltd. All rights reserved.


El Hachemi N.,Interuniversity Research Center on Enterprise Networks | Gendreau M.,Interuniversity Research Center on Enterprise Networks | Rousseau L.-M.,Interuniversity Research Center on Enterprise Networks
Computers and Operations Research | Year: 2013

We present a synchronized routing and scheduling problem that arises in the forest industry, as a variation of the log-truck scheduling problem. It combines routing and scheduling of trucks with specific constraints related to the Canadian forestry context. This problem includes aspects such as pick-up and delivery, multiple products, inventory stock, multiple supply points and multiple demand points. We developed a decomposition approach to solve the weekly problem in two phases. In the first phase we use a MIP solver to solve a tactical model that determines the destinations of full truckloads from forest areas to woodmills. In the second phase, we make use of two different methods to route and schedule the daily transportation of logs: the first one consists in using a constraint-based local search approach while the second one is a hybrid approach involving a constraint programming based model and a constraint-based local search model. These approaches have been implemented using COMET2.0. The method, was tested on two industrial cases from forest companies in Canada. © 2011 Elsevier Ltd.

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