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Non-identical parallel machines batch processing problem to minimize the makespan: Models and algorithms
University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment. (Virtual Production Development (VPD))ORCID iD: 0000-0003-2300-7929
School of Management, University of Bath, United Kingdom.
School of Management, University of Bath, United Kingdom ; Department of Decision Sciences, HEC Montréal, Canada.
2024 (English)In: Computers & Operations Research, ISSN 0305-0548, E-ISSN 1873-765X, Vol. 168, article id 106708Article in journal (Refereed) Published
Abstract [en]

This paper studies a parallel heterogeneous machine batching and scheduling problem in which weighted jobs are first batched, and the batches are then assigned and sequenced on machines of varying capacities. The duration of a batch is the longest time needed to process a job, and the objective is that of minimizing the makespan, or the sum of the batches durations on the machine finishing last. The authors develop polynomial-size mathematical formulations and a variable neighborhood search metaheuristic. Extensive computational results suggest that the flow-based formulation outperforms a compact formulation, despite its larger number of variables. The metaheuristic is capable of producing high-quality solutions within a limited computing time.

Place, publisher, year, edition, pages
Elsevier, 2024. Vol. 168, article id 106708
Keywords [en]
Parallel machines, batch processing machines, makespan minimization, variable neighborhood search
National Category
Computational Mathematics Computer Systems
Research subject
Virtual Production Development (VPD)
Identifiers
URN: urn:nbn:se:his:diva-23891DOI: 10.1016/j.cor.2024.106708ISI: 001249043700001Scopus ID: 2-s2.0-85194702464OAI: oai:DiVA.org:his-23891DiVA, id: diva2:1863066
Note

CC BY 4.0

Available online 25 May 2024

Corresponding author: Maria Battarra

The work reported in this paper was undertaken as part of the Made Smarter Innovation: Centre for People-Led Digitalization, at the University of Bath, University of Nottingham, and Loughborough University. The project is funded by the Engineering and Physical Sciences Research Council (EP-SRC) Grant EP/V062042/1.

Available from: 2024-05-30 Created: 2024-05-30 Last updated: 2024-11-27Bibliographically approved

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Beldar, Pedram

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