A GRASP heuristic for the multi-objective permutation flowshop scheduling problem

被引：22

作者：

Claudio Arroyo, Jose Elias ^{[1
]}

de Souza Pereira, Ana Amelia ^{[1
]}

机构：

[1] Univ Fed Vicosa, Dept Informat, BR-36570000 Vicosa, MG, Brazil

来源：

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY | 2011年 / 55卷 / 5-8期

关键词：

Flowshop scheduling; Multi-objective combinatorial optimization; Heuristics; GRASP; GENETIC LOCAL SEARCH; ANT COLONY OPTIMIZATION; TABU SEARCH; EVOLUTIONARY ALGORITHMS; SCATTER SEARCH; TOTAL FLOWTIME; MAKESPAN; MULTIPLE; OBJECTIVES; MINIMIZE;

D O I：

10.1007/s00170-010-3100-x

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper presents a multi-objective greedy randomized adaptive search procedure (GRASP)-based heuristic for solving the permutation flowshop scheduling problem in order to minimize two and three objectives simultaneously: (1) makespan and maximum tardiness; (2) makespan, maximum tardiness, and total flowtime. GRASP is a competitive metaheuristic for solving combinatorial optimization problems. We have customized the basic concepts of GRASP algorithm to solve a multi-objective problem and a new algorithm named multi-objective GRASP algorithm is proposed. In order to find a variety of non-dominated solutions, the heuristic blends two typical approaches used in multi-objective optimization: scalarizing functions and Pareto dominance. For instances involving two machines, the heuristic is compared with a bi-objective branch-and-bound algorithm proposed in the literature. For instances involving up to 80 jobs and 20 machines, the non-dominated solutions obtained by the heuristic are compared with solutions obtained by multi-objective genetic algorithms from the literature. Computational results indicate that GRASP is a promising approach for multi-objective optimization.

引用

页码：741 / 753

页数：13

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