Please use this identifier to cite or link to this item: https://hdl.handle.net/11499/9551
Title: Hybrid genetic algorithms for minimizing makespan in dynamic job shop scheduling problem
Authors: Kundakcı, Nilsen
Kulak, Osman
Keywords: Dynamic job shop scheduling
Hybrid genetic algorithm
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Algorithms
Benchmarking
Combinatorial optimization
Genetic algorithms
Numerical methods
Optimization
Scheduling
Bench-mark problems
Hybrid genetic algorithms
Job shop scheduling problems
Machine breakdown
Minimizing makespan
Numerical experiments
Production environments
Job shop scheduling
Publisher: Elsevier Ltd
Abstract: Job shop scheduling has been the focus of a substantial amount of research over the last decade and most of these approaches are formulated and designed to address the static job shop scheduling problem. Dynamic events such as random job arrivals, machine breakdowns and changes in processing time, which are inevitable occurrences in production environment, are ignored in static job shop scheduling problem. As dynamic job shop scheduling problem is known NP-hard combinatorial optimization, this paper introduces efficient hybrid Genetic Algorithm (GA) methodologies for minimizing makespan in this kind of problem. Various benchmark problems including the number of jobs, the number of machines, and different dynamic events are generated and detailed numerical experiments are carried out to evaluate the performance of proposed methodologies. The numerical results indicate that the proposed methods produce superior solutions for well-known benchmark problems compared to those reported in the literature. © 2016 Elsevier Ltd. All rights reserved.
URI: https://hdl.handle.net/11499/9551
https://doi.org/10.1016/j.cie.2016.03.011
ISSN: 0360-8352
Appears in Collections:İktisadi ve İdari Bilimler Fakültesi Koleksiyonu
Mühendislik Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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