R2-IBMOLS applied to a practical case of the multiobjective knapsack problem

被引:12
作者
Chabane, Brahim [1 ,2 ]
Basseur, Matthieu [1 ]
Hao, Jin-Kao [1 ,3 ]
机构
[1] Univ Angers, LERIA, 2 Bd Lavoisier, F-49045 Angers, France
[2] GePI Conseil, Grand Maine Allee Grand Launay, F-49000 Angers, France
[3] Inst Univ France, Paris, France
关键词
Action planning; Multiobjective optimization; Decision support; Heuristics; EVOLUTIONARY ALGORITHMS; GENETIC ALGORITHM;
D O I
10.1016/j.eswa.2016.11.007
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The social and medico-social sector is experiencing a fast evolution due to the continuing growth of older population. Yet, social and medico-social structures suffer from a real lack of computerized decision support tools. This work deals with the key issue of elaborating efficient action plans in these structures, which aims to improve the whole quality of these structures. An efficient action plan is a set of actions chosen among many candidate actions which optimize several conflicting objectives and satisfy some imperative constraints. To assist managers to optimize their action plans, we develop a multiobjective decision support system as part of a commercial software. According to the objectives and constraints defined by the decision maker and a set of feasible actions, the software is used to select the actions that optimize the given objectives while satisfying the constraints. After providing a description and a formal model of the action plan optimization problem, we present a solution method using the iterated local search based on quality indicators (IBMOLS). We assess the proposed approach on problem instances with 2-8 objectives and up to 500 candidate actions and demonstrate its usefulness as a key component of a decision support system for social and medico-social structures. (C) 2016 Elsevier Ltd. All rights reserved.
引用
收藏
页码:457 / 468
页数:12
相关论文
共 51 条
[11]  
Barichard V., 2003, Tsinghua Science and Technology, V8, P8
[12]   The efficiency of indicator-based local search for multi-objective combinatorial optimisation problems [J].
Basseur, M. ;
Liefooghe, A. ;
Le, K. ;
Burke, E. K. .
JOURNAL OF HEURISTICS, 2012, 18 (02) :263-296
[13]   Indicator-based multi-objective local search [J].
Basseur, M. ;
Burke, E. K. .
2007 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-10, PROCEEDINGS, 2007, :3100-3107
[14]   Hypervolume-based multi-objective local search [J].
Basseur, Matthieu ;
Zeng, Rong-Qiang ;
Hao, Jin-Kao .
NEURAL COMPUTING & APPLICATIONS, 2012, 21 (08) :1917-1929
[15]   An improved firefly algorithm for solving dynamic multidimensional knapsack problems [J].
Baykasoglu, Adil ;
Ozsoydan, Fehmi Burcin .
EXPERT SYSTEMS WITH APPLICATIONS, 2014, 41 (08) :3712-3725
[16]   Indicator Based Ant Colony Optimization for Multi-Objective Knapsack Problem [J].
Ben Mansour, Imen ;
Alaya, Ines .
KNOWLEDGE-BASED AND INTELLIGENT INFORMATION & ENGINEERING SYSTEMS 19TH ANNUAL CONFERENCE, KES-2015, 2015, 60 :448-457
[17]   On the Properties of the R2 Indicator [J].
Brockhoff, Dimo ;
Wagner, Tobias ;
Trautmann, Heike .
PROCEEDINGS OF THE FOURTEENTH INTERNATIONAL CONFERENCE ON GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, 2012, :465-472
[18]   Improving hypervolume-based multiobjective evolutionary algorithms by using objective reduction methods [J].
Brockhoff, Dimo ;
Zitzler, Eckart .
2007 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-10, PROCEEDINGS, 2007, :2086-2093
[19]   A Practical Case of the Multiobjective Knapsack Problem: Design, Modelling, Tests and Analysis [J].
Chabane, Brahim ;
Basseur, Matthieu ;
Hao, Jin-Kao .
LEARNING AND INTELLIGENT OPTIMIZATION, LION 9, 2015, 8994 :249-255
[20]   An improved genetic algorithm based approach to solve constrained knapsack problem in fuzzy environment [J].
Changdar, Chiranjit ;
Mahapatra, G. S. ;
Pal, Rajat Kumar .
EXPERT SYSTEMS WITH APPLICATIONS, 2015, 42 (04) :2276-2286