Two-Stage Stochastic Programming for a Reconfigurable Unrelated Parallel Machine Scheduling Problem
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F25%3A00387783" target="_blank" >RIV/68407700:21730/25:00387783 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1016/j.ifacol.2025.09.294" target="_blank" >https://doi.org/10.1016/j.ifacol.2025.09.294</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.ifacol.2025.09.294" target="_blank" >10.1016/j.ifacol.2025.09.294</a>
Alternative languages
Result language
angličtina
Original language name
Two-Stage Stochastic Programming for a Reconfigurable Unrelated Parallel Machine Scheduling Problem
Original language description
Production managers are faced with uncertainties like machine failures, demand uncertainty, and workers’ availability. Reconfigurable Manufacturing Systems (RMSs) provide a flexible solution to these challenges. Considering worker availability, this paper presents a two-stage stochastic programming model for optimizing unrelated parallel machine scheduling in the RMS. The model adopts a resource flow-based framework to ensure that workers with different skills and availabilities are allocated efficiently throughout job sequences. The objective of the mathematical model is to minimize the expected makespan across multiple scenarios. Eighteen experiments were conducted to evaluate the model, in which key parameters were varied at various levels, including worker’s availability, reconfiguration time, and processing time. The Analysis of Variance (ANOVA) reveals that processing time had the most significant impact on the makespan, followed by worker’s availability and reconfiguration time. The results demonstrate the model’s ability to generate flexible schedules under uncertainty, offering valuable insights for enhancing the adaptability and efficiency of RMS operations.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/EH22_008%2F0004590" target="_blank" >EH22_008/0004590: Robotics and advanced industrial production</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
11th IFAC Conference on Manufacturing Modelling, Management and Control MIM 2025
ISBN
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ISSN
2405-8971
e-ISSN
2405-8963
Number of pages
6
Pages from-to
1748-1753
Publisher name
Elsevier BV
Place of publication
Linz
Event location
Trondheim
Event date
Jun 30, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001583825700293