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A multi-objective evolutionary algorithm based on decomposition and constraint programming for the multi-objective team orienteering problem with time windows

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APPLIED SOFT COMPUTING 2018年 73卷 383-393页

作者： Hu, Wanzhe Fathi, Mahdi Pardalos, Panos M. Chongqing Univ Coll Mat Sci & Engn Chongqing Peoples R China Univ Florida Dept Ind & Syst Engn Ctr Appl Optimizat Gainesville FL 32611 USA

The team orienteering problem with time windows (TOPTW) is a well-known variant of the orienteering problem (OP) originated from the sports game of orienteering. Since the TOPTW has many applications in the real world such as disaster relief routing and home fuel delivery, it has been studied extensively. In the classical TOPTW, only one profit is associated with each checkpoint while in many practical applications each checkpoint can be evaluated from different aspects, which results in multiple profits. In this study, the multi-objective team orienteering problem with time windows (MOTOPTW), where checkpoints with multiple profits are considered, is introduced to find the set of Pareto optimal solutions to support decision making. Moreover, a multi-objective evolutionary algorithm based on decomposition and constraint programming (CPMOEA/D) is developed to solve the MOTOPTW. The advantages of decomposition approaches to handle multi-objective optimization problems and those of the constraint programming to deal with combinatorial optimization problems have been integrated in CPMOEA/D. Finally, the proposed algorithm is applied to solve public benchmark instances. The results are compared with the best-known solutions from the literature and show more improvement. (C) 2018 Elsevier B.V. All rights reserved.

关键词： multi-objective combinatorial optimization Team orienteering problem multi-objective evolutionary algorithm Decomposition approach Constraint programming

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Solving a dial-a-ride problem with a hybrid evolutionary multi-objective approach: Application to demand responsive transport

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APPLIED SOFT COMPUTING 2012年第4期12卷 1247-1258页

作者： Chevrier, Remy Liefoogheb, Arnaud Jourdan, Laetitia Dhaenens, Clarisse Univ Lille Nord France IFSTTAR ESTAS F-59650 Villeneuve Dascq France Univ Lille 1 UMR CNRS 8022 Lab Informat Fondamentale Lille F-59655 Villeneuve Dascq France INRIA Lille Nord Europe F-59650 Villeneuve Dascq France

Demand responsive transport allows customers to be carried to their destination as with a taxi service, provided that the customers are grouped in the same vehicles in order to reduce operational costs. This kind of service is related to the dial-a-ride problem. However, in order to improve the quality of service, demand responsive transport needs more flexibility. This paper tries to address this issue by proposing an original evolutionary approach. In order to propose a set of compromise solutions to the decision-maker, this approach optimizes three objectives concurrently. Moreover, in order to intensify the search process, this multi-objective evolutionary approach is hybridized with a local search. Results obtained on random and realistic problems are detailed to compare three state-of-the-art algorithms and discussed from an operational point of view. (c) 2011 Elsevier B.V. All rights reserved.

关键词： Demand responsive transport Dial-a-ride problem Evolutionary algorithm multi-objective combinatorial optimization Local search

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An exact solution approach for multi-objective location-transportation problem for disaster response

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COMPUTERS & OPERATIONS RESEARCH 2014年第Jan.期41卷 83-93页

作者： Abounacer, Rachida Rekik, Monia Renaud, Jacques Univ Laval Quebec City PQ G1V 0A6 Canada Ibnou Zohr Univ Agadir 80000 Morocco

This paper considers a three-objective location-transportation problem for disaster response. The location problem aims at determining the number, the position and the mission of required humanitarian aid distribution centers (HADC) within the disaster region. The transportation problem deals with the distribution of aid from HADCs to demand points. Three conflicting objectives are considered. The first objective minimizes the total transportation duration of needed products from the distribution centers to the demand points. The second objective minimizes the number of agents (first-alders) needed to open and operate the selected distribution centers. The third objective minimizes the non-covered demand for all demand points within the affected area. We propose an epsilon-constraint method for this problem and prove that it generates the exact Pareto front. The proposed algorithm can be applied to any three-objective optimization problem provided that the problem involves at least two integer and conflicting objectives. The results obtained in our experimental study show that the computing time required by the proposed method may be large for some instances. A heuristic version of our algorithm yielded, however, good approximation of the Pareto front in relatively short computing times. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Emergency response Location-transportation problems multi-objective combinatorial optimization Exact method Epsilon-constraint method

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An optimization-based heuristic for the multi-objective Undirected Capacitated Arc Routing Problem

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COMPUTERS & OPERATIONS RESEARCH 2012年第10期39卷 2300-2309页

作者： Grandinetti, L. Guerriero, F. Lagana, D. Pisacane, O. Univ Calabria Dipartimento Elettron Informat & Sistemist Arcavacata Di Rende CS Italy

The multi-objective Undirected Capacitated Arc Routing Problem (MUCARP) is the optimization problem aimed at finding the best strategy for servicing a subset of clients localized along the links of a logistic network, by using a fleet of vehicles and optimizing more than one objective. In general, the first goal consists in minimizing the total transportation cost, and in this case the problem brings back to the well-known Undirected Capacitated Arc Routing Problem (UCARP). The motivation behind the study of the MUCARP lies in the study of real situations where companies working in the logistic distribution field deal with complex operational strategies, in which different actors (trucks, drivers, customers) have to be allocated within an unified framework by taking into account opposite needs and different employment contracts. All the previous considerations lead to the MUCARP as a benchmark optimization problem for modeling practical situations. In this paper, the MUCARP is heuristically tackled. In particular, three competitive objectives are minimized at the same time: the total transportation cost, the longest route cost (makespan) and the number of vehicles (i.e., the total number of routes). An approximation of the optimal Pareto front is determined through an optimization-based heuristic procedure. whose performances are tested and analyzed on classical benchmark instances. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： multi-objective combinatorial optimization Arc routing problems Hybrid heuristic algorithm

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An application of a multi-objective tabu search algorithm to a bicriteria flowshop problem

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JOURNAL OF HEURISTICS 2004年第5期10卷 463-481页

作者： Armentano, VA Arroyo, JEC Univ Estadual Campinas Dept Engn Sistemas Fac Elect & Comp Engn BR-13083970 Campinas SP Brazil Univ Colorado Coll Business & Adm Boulder CO 80309 USA

This paper proposes a new tabu search algorithm for multi-objective combinatorial problems with the goal of obtaining a good approximation of the Pareto-optimal or efficient solutions. The algorithm works with several paths of solutions in parallel, each with its own tabu list, and the Pareto dominance concept is used to select solutions from the neighborhoods. In this way we obtain at each step a set of local nondominated points. The dispersion of points is achieved by a clustering procedure that groups together close points of this set and then selects the centroids of the clusters as search directions. A nice feature of this multi-objective algorithm is that it introduces only one additional parameter, namely, the number of paths. The algorithm is applied to the permutation flowshop scheduling problem in order to minimize the criteria of makespan and maximum tardiness. For instances involving two machines, the performance of the algorithm is tested against a Branch-and-Bound algorithm proposed in the literature, and for more than two machines it is compared with that of a tabu search algorithm and a genetic local search algorithm, both from the literature. Computational results show that the heuristic yields a better approximation than these algorithms.

关键词： multi-objective combinatorial optimization metaheuristics tabu search flowshop scheduling

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On Local Search for Bi-objective Knapsack Problems

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EVOLUTIONARY COMPUTATION 2013年第1期21卷 179-196页

作者： Liefooghe, Arnaud Paquete, Luis Figueira, Jose Rui Univ Lille 1 LIFL UMR CNRS 8022 F-59655 Villeneuve Dascq France Univ Coimbra CISUC Dept Informat Engn Fac Sci & Technol P-3030290 Coimbra Portugal Inst Super Tecn CEG IST P-1040001 Lisbon Portugal LORIA Lab Nancy France

In this article, a local search approach is proposed for three variants of the bi-objective binary knapsack problem, with the aim of maximizing the total profit and minimizing the total weight. First, an experimental study on a given structural property of connectedness of the efficient set is conducted. Based on this property, a local search algorithm is proposed and its performance is compared to exact algorithms in terms of runtime and quality metrics. The experimental results indicate that this simple local search algorithm is able to find a representative set of optimal solutions in most of the cases, and in much less time than exact algorithms.

关键词： multi-objective combinatorial optimization population-based local search structural analysis connectedness knapsack problems

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On optimizing a bi-objective flowshop scheduling problem in an uncertain environment

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COMPUTERS & MATHEMATICS WITH APPLICATIONS 2012年第12期64卷 3747-3762页

作者： Liefooghe, Arnaud Basseur, Matthieu Humeau, Jeremie Jourdan, Laetitia Talbi, El-Ghazali Univ Lille 1 CNRS UMR 8022 LIFL F-59655 Villeneuve Dascq France INRIA Lille Nord Europe F-59655 Villeneuve Dascq France Univ Angers LERIA F-49045 Angers France Ecole Mines Douai IA Dept BP 10838 Douai France

Existing models from scheduling often over-simplify the problems appearing in real-world industrial situations. The original application is often reduced to a single-objective one, where the presence of uncertainty is neglected. In this paper, we focus on multi-objective optimization in uncertain environments. A bi-objective flowshop scheduling problem with uncertain processing times is considered. An indicator-based evolutionary algorithm is proposed to handle these two difficulties (multiple objectives and uncertain environment) at the same time. Four different strategies, based on uncertainty-handling quality indicators, are proposed in the paper. Computational experiments are performed on a large set of instances by considering different scenarios with respect to uncertainty. We show that an uncertainty-handling strategy is a key issue to obtain good-quality solutions, and that the algorithm performance is strongly related to the level of uncertainty over the environmental parameters. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Permutation flowshop scheduling Evolutionary algorithms multi-objective combinatorial optimization Uncertain processing times

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multi-objective evolutionary approaches for intelligent design of sensor networks in the petrochemical industry

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EXPERT SYSTEMS WITH APPLICATIONS 2012年第3期39卷 2643-2649页

作者： Cecchini, Rocio L. Ponzoni, Ignacio Carballido, Jessica A. Univ Nacl Sur Dept Ciencias & Ingn Computac Lab Invest & Desarrollo Comp Cient LIDeCC RA-8000 Bahia Blanca Buenos Aires Argentina UNS CONICET Planta Piloto Ingn Quim CCT Bahia Blanca Ex CRIBA Bahia Blanca Buenos Aires Argentina

The design of optimal sensor networks for an industrial process is a complex problem that requires the resolution of several tasks with a high level of expertise. The first of these subproblems consists in selecting an initial sensor network as the starting point for the instrumentation design. This particular task constitutes a combinatorial optimization problem, where several goals are prosecuted by the designer. Therefore, the initialization procedure can be defined as a multi-objective optimization problem. In this paper, the use of multi-objective evolutionary approaches to assist experts in the design of an initial sensor network is proposed and analyzed. The aim is to contrast the advantages and limitations of Pareto and non-Pareto techniques in the context of this industrial application. The algorithms consider objectives related to cost, reliability and level of information associated with a sensor network. The techniques were evaluated by means of a comparative analysis for a strongly non-linear mathematical model that represents an ammonia synthesis plant. Results have been contrasted in terms of the set coverage and spacing metrics. As a final conclusion, the non-Pareto strategy converged closer to the Pareto front than the Pareto-based algorithms. In contrast, the Pareto-based algorithms achieved better relative distance among solutions than the non-Pareto method. In all cases, the use of evolutionary computation is useful for the expert to take the final decision on the preferred initial sensor network. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Intelligent sensor network design Evolutionary computation multi-objective combinatorial optimization

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A bi-objective model for robust resource-constrained project scheduling

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INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2005年第2期96卷 175-187页

作者： Al-Fawzan, MA Haouari, M King Abdulaziz City Sci & Technol Riyadh 11442 Saudi Arabia Ecole Polytech Tunisie La Marsa 2078 Tunisia

A common problem which arises in project management is the fact that the planned schedule is often disrupted by several uncontrollable factors like additional time that might be required for rework and correction of detected defects. As a result, project managers are often unable to meet the promised completion dates. It is therefore vital to take into account such possible disruptions and their potential negative consequences at the project schedule design stage. In this paper, we address the issue of designing a project schedule which is not only short in time, but also less vulnerable to disruptions due to reworks and other undesirable conditions. To that aim, we introduce the concept of schedule robustness and we develop a bi-objective resource-constrained project scheduling model. We consider the objectives of robustness maximization along with makespan minimization. We develop a tabu search algorithm in order to generate an approximate set of efficient solutions. Several variants of the algorithm are tested and compared on a large set of benchmark problems. (c) 2004 Elsevier B.V. All rights reserved.

关键词： resource-constrained project scheduling robust scheduling multi-objective combinatorial optimization tabu search

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A two phase method for multi-objective integer programming and its application to the assignment problem with three objectives

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DISCRETE optimization 2010年第3期7卷 149-165页

作者： Przybylski, Anthony Gandibleux, Xavier Ehrgott, Matthias Univ Nantes LINA UMR CNRS 6241 F-44322 Nantes 03 France Univ Auckland Dept Engn Sci Auckland Mail Ctr Auckland 1142 New Zealand

In this paper, we present a generalization of the two phase method to solve multi-objective integer programmes with p > 2 objectives. We apply the method to the assignment problem with three objectives. We have recently proposed an algorithm for the first phase, computing all supported efficient solutions. The second phase consists in the definition and the exploration of the search area inside of which nonsupported nondominated points may exist. This search area is not defined by trivial geometric constructions in the multi-objective case, and is therefore difficult to describe and to explore. The lower and upper bound sets introduced by Ehrgott and Gandibleux in 2001 are used as a basis for this description. Experimental results on the three-objective assignment problem where we use a ranking algorithm to explore the search area show the efficiency of the method. (C) 2010 Elsevier B.V. All rights reserved.

关键词： multi-objective combinatorial optimization Two phase method Assignment problem Ranking

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