Multi-objective optimisation in flexible assembly job shop scheduling using a distributed ant colony system

被引:95
作者
Zhang, Sicheng [1 ]
Li, Xiang [1 ]
Zhang, Bowen [1 ]
Wang, Shouyang [2 ]
机构
[1] Beijing Univ Chem Technol, Coll Econ & Management, 15 North 3rd Ring Rd East, Beijing 100029, Peoples R China
[2] Chinese Acad Sci, Acad Math & Syst Sci, 55 Zhongguancun East Rd, Beijing 100190, Peoples R China
基金
中国国家自然科学基金;
关键词
Scheduling; Flexible assembly job shop scheduling; Multi-objective optimisation; Meta-heuristics; Ant colony optimisation; PARTICLE SWARM OPTIMIZATION; INTEGER PROGRAMMING-MODEL; EVOLUTIONARY ALGORITHM; ORDER REVIEW/RELEASE; DISPATCHING RULES; GENETIC ALGORITHM; MINIMIZE MAKESPAN; SEARCH ALGORITHM; FLOWSHOP; SETUP;
D O I
10.1016/j.ejor.2019.11.016
中图分类号
C93 [管理学];
学科分类号
12 ; 1201 ; 1202 ; 120202 ;
摘要
This paper studies the production scheduling problem in a flexible manufacturing system with two adjacent working areas, whose products are incorporated with flexible non-linear process plans and assembling operations. The basic parts are produced in one area before they are transported to the other area for assembly. The assembling structures of products are either flat or multi-levelled. Sequence-dependent setup times of operations and transition times of jobs between machines are considered separately from processing times. Lot streaming is considered beforehand such that each job represents a basic part instead of a batch of identical parts. Identical subassemblies are shared by all possible assembling operations, instead of being pre-associated with any product. Makespan, total tardiness and total workload are taken as objectives to be optimised. We propose a distributed ant colony system to solve the problem and explore the Pareto front. The approach is first compared with other methods, using several sets of hypothetical test cases with different sizes and complexities; then, it is applied to solve a ball valve production scheduling problem under different scenarios. We show that the proposed approach outperforms most of the other methods for the tested problems, especially for large-scale instances, making it a valuable and competitive approach for solving practical production scheduling problems. (C) 2019 Elsevier B.V. All rights reserved.
引用
收藏
页码:441 / 460
页数:20
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