Parallel evolutionary algorithms for the reconfigurable transfer line balancing problem

This paper deals with an industrial problem of machining line design, which consists in partitioning a given set of operations into several subsets corresponding to workstations and sequencing the operations to satisfy the technical requirements and achieve the best performance of the line. The prob...

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Bibliographic Details
Published in:Yugoslav Journal of Operations Research Vol. 34; no. 1; pp. 93 - 107
Main Author: Borisovsky, Pavel
Format: Journal Article
Language:English
Published: University of Belgrade 2024
Subjects:
cnc machines
parallel computing
partial order
scalability
setup times
split decoder
ISSN:0354-0243, 1820-743X
Online Access:Get full text
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Summary:This paper deals with an industrial problem of machining line design, which consists in partitioning a given set of operations into several subsets corresponding to workstations and sequencing the operations to satisfy the technical requirements and achieve the best performance of the line. The problem has a complex set of constraints that include partial order on operations, part positioning, inclusion, exclusion, cycle time, and installation of parallel machines on a workstation. The problem is NP-hard and even finding a feasible solution can be a difficult task from the practical point of view. A parallel evolutionary algorithm (EA) is proposed and implemented for execution on a Graphics Processing Unit (GPU). The parallelization in the EA is done by working on several parents in one iteration and in multiple application of mutation operator to the same parent to produce the best offspring. The proposed approach is evaluated on large scale instances and demonstrated superior performance compared to the algorithms from the literature in terms of running time and ability to obtain feasible solutions. It is shown that in comparison to the traditional populational EA scheme the newly proposed algorithm is more suitable for advanced GPUs with a large number of cores.
ISSN:0354-0243
1820-743X
DOI:10.2298/YJOR230415018B