A Population-Based Iterated Greedy Algorithm for Distributed Assembly No-Wait Flow-Shop Scheduling Problem

This article investigates a distributed assembly no-wait flow-shop scheduling problem (DANWFSP), which has important applications in manufacturing systems. The objective is to minimize the total flowtime. A mixed-integer linear programming model of DANWFSP with total flowtime criterion is proposed....

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Vydáno v:IEEE transactions on industrial informatics Ročník 19; číslo 5; s. 6692 - 6705
Hlavní autoři: Zhao, Fuqing, Xu, Zesong, Wang, Ling, Zhu, Ningning, Xu, Tianpeng, Jonrinaldi, J.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.05.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Acceptance criteria
Algorithms
Assembly
Distributed assembly scheduling
Greedy algorithms
Integer programming
iterated greedy algorithm
Job shop scheduling
Linear programming
Mixed integer
no-wait flow-shop
Processor scheduling
Production facilities
Search methods
Search problems
Sociology
Statistics
total flowtime
ISSN:1551-3203, 1941-0050
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Shrnutí:This article investigates a distributed assembly no-wait flow-shop scheduling problem (DANWFSP), which has important applications in manufacturing systems. The objective is to minimize the total flowtime. A mixed-integer linear programming model of DANWFSP with total flowtime criterion is proposed. A population-based iterated greedy algorithm (PBIGA) is presented to address the problem. A new constructive heuristic is presented to generate an initial population with high quality. For DANWFSP, an accelerated NR3 algorithm is proposed to assign jobs to the factories, which improves the efficiency of the algorithm and saves CPU time. To enhance the effectiveness of the PBIGA, the local search method and the destruction-construction mechanisms are designed for the product sequence and job sequence, respectively. A selection mechanism is presented to determine, which individuals execute the local search method. An acceptance criterion is proposed to determine whether the offspring are adopted by the population. Finally, the PBIGA and seven state-of-the-art algorithms are tested on 810 large-scale benchmark instances. The experimental results show that the presented PBIGA is an effective algorithm to address the problem and performs better than recently state-of-the-art algorithms compared in this article.
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ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2022.3192881