Dual resource constrained flexible job shop scheduling with sequence-dependent setup time
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27510%2F24%3A10255162" target="_blank" >RIV/61989100:27510/24:10255162 - isvavai.cz</a>
Result on the web
<a href="https://onlinelibrary.wiley.com/doi/10.1111/exsy.13669" target="_blank" >https://onlinelibrary.wiley.com/doi/10.1111/exsy.13669</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1111/exsy.13669" target="_blank" >10.1111/exsy.13669</a>
Alternative languages
Result language
angličtina
Original language name
Dual resource constrained flexible job shop scheduling with sequence-dependent setup time
Original language description
This study addresses the imperative need for efficient solutions in the context of the dual resource constrained flexible job shop scheduling problem with sequence-dependent setup times (DRCFJS-SDSTs). We introduce a pioneering tri-objective mixed-integer linear mathematical model tailored to this complex challenge. Our model is designed to optimize the assignment of operations to candidate multi-skilled machines and operators, with the primary goals of minimizing operators' idleness cost and sequence-dependent setup time-related expenses. Additionally, it aims to mitigate total tardiness and earliness penalties while regulating maximum machine workload. Given the NP-hard nature of the proposed DRCFJS-SDST, we employ the epsilon constraint method to derive exact optimal solutions for small-scale problems. For larger instances, we develop a modified variant of the multi-objective invasive weed optimization (MOIWO) algorithm, enhanced by a fuzzy sorting algorithm for competitive exclusion. In the absence of established benchmarks in the literature, we validate our solutions against those generated by multi-objective particle swarm optimization (MOPSO) and non-dominated sorted genetic algorithm (NSGA-II). Through comparative analysis, we demonstrate the superior performance of MOIWO. Specifically, when compared with NSGA-II, MOIWO achieves success rates of 90.83% and shows similar performance in 4.17% of cases. Moreover, compared with MOPSO, MOIWO achieves success rates of 84.17% and exhibits similar performance in 9.17% of cases. These findings contribute significantly to the advancement of scheduling optimization methodologies.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
50200 - Economics and Business
Result continuities
Project
<a href="/en/project/GA18-15530S" target="_blank" >GA18-15530S: Aplikace vícekriteriálního programování na problémy v pružné výrobě a projektovém plánování: teorie a aplikace</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Expert Systems
ISSN
0266-4720
e-ISSN
1468-0394
Volume of the periodical
41
Issue of the periodical within the volume
10
Country of publishing house
US - UNITED STATES
Number of pages
38
Pages from-to
"e13669"
UT code for WoS article
001253891300001
EID of the result in the Scopus database
2-s2.0-85196851130