A Pareto-optimal genetic algorithm for warehouse multi-objective optimization

被引：39

作者：

Poulos, PN ^{[1
]}

Rigatos, GG ^{[1
]}

Tzafestas, SG ^{[1
]}

Koukos, AK ^{[1
]}

机构：

[1] Natl Tech Univ Athens, Dept Elect & Comp Engn, Intelligent Robot & Automat Lab, GR-15773 Athens, Greece

来源：

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE | 2001年 / 14卷 / 06期

关键词：

genetic algorithms; multi-objective optimization; pareto optimality; constrained placements; automated warehouse management;

D O I：

10.1016/S0952-1976(01)00036-7

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The automated warehouse management requires to fulfill objectives that are usually conflicting with each other. The decisions taken must ensure optimized usage of resources. cost reduction and better customer service. The warehouse replenishment task is a typical example of multi-objective optimization. In this paper. a genetic algorithm with a new crossover operator is developed to solve the replenishment problem. This algorithm is applied to real warehouse data and produces Pareto-optimal permutations of the stored products. A fuzzy rule-base is proposed to increase the diversity of the optimal solutions. (C) 2002 Elsevier Science Ltd. All rights reserved.

引用

页码：737 / 749

页数：13

共 14 条

[1] FUZZY METHODOLOGIES FOR INTERACTIVE MULTICRITERIA OPTIMIZATION [J].