An elitist cooperative evolutionary bi-level multi-objective decomposition-based algorithm for sustainable supply chain

被引:5
|
作者
Abbassi, Malek [1 ,2 ]
Chaabani, Abir [1 ]
Absi, Nabil [2 ]
Ben Said, Lamjed [1 ]
机构
[1] Univ Tunis, Inst Super Gest Tunis, SMART Lab, Tunis, Tunisia
[2] Univ Clermont Auvergne, Dept Mfg Sci & Logist, Mines St Etienne, CNRS,UMR 6158,LIMOS,CMP, F-13541 Gardanne, France
关键词
Bi-level multi-objective optimisation; decomposition; co-evolution; parallelism; elitism; production-distribution systems; disassembly of end of life products; OPTIMALITY CONDITIONS; OPTIMIZATION; COLLECTION; MODEL;
D O I
10.1080/00207543.2021.1999523
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Many real-life applications are modelled using hierarchical decision-making in which: an upper-level optimisation task is constrained by a lower-level one. Such class of optimisation problems is referred in the literature as Bi-Level Optimisation Problems (BLOPs). Most of the proposed methods tackled the single-objective continuous case adhering to some regularity assumptions. This is at odds with real-world problems which involve mainly discrete variables and expensive objective function evaluations. Besides, the optimisation process becomes exorbitantly time-consuming, especially when optimising several objectives at each level. For this reason, the Multi-objective variant (MBLOP) remains relatively less explored and the number of methods tackling the combinatorial case is much reduced. Motivated by these observations, we propose in this work an elitist decomposition-based evolutionary algorithm to solve MBLOPs, called ECODBEMA. The basic idea of our proposal is to handle, decomposition, elitism and multithreading mechanisms to cope with the MBLOP's high complexity. ECODBEMA is applied to the production-distribution problem and to a sustainable end-of-life products disassembly case-study based on real-data of Aix-en-Provence French city. We compared the optimal solutions of an exact method using CPLEX solver with near-optimal solutions obtained by ECODBEMA. The statistical results show the significant outperformance of ECODBEMA against other multi-objective bi-level optimisation algorithms.
引用
收藏
页码:7013 / 7032
页数:20
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