A BRKGA-DE algorithm for parallel-batching scheduling with deterioration and learning effects on parallel machines under preventive maintenance consideration

This paper introduces a parallel-batching scheduling problem with deterioration and learning effects on parallel machines, where the actual processing time of a job is subject to the phenomena of deterioration and learning. All jobs are first divided into different parallel batches, and the processi...

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Published inAnnals of mathematics and artificial intelligence Vol. 88; no. 1-3; pp. 237 - 267
Main Authors Kong, Min, Liu, Xinbao, Pei, Jun, Cheng, Hao, Pardalos, Panos M.
Format Journal Article
LanguageEnglish
Published Cham Springer International Publishing 01.03.2020
Springer
Springer Nature B.V
Subjects
Algorithms
Analysis
Artificial Intelligence
Complex Systems
Computer Science
Evolutionary computation
Genetic algorithms
Integrated circuit fabrication
Maintenance and repair
Mathematics
Preventive maintenance
Scheduling
Semiconductor production equipment
Sequences
Learning
Scheduling
Parallel-batching
Deterioration
BRKGA-DE
90B35
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ISSN1012-2443
1573-7470
DOI10.1007/s10472-018-9602-1

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Summary:This paper introduces a parallel-batching scheduling problem with deterioration and learning effects on parallel machines, where the actual processing time of a job is subject to the phenomena of deterioration and learning. All jobs are first divided into different parallel batches, and the processing time of the batches is equal to the largest processing time of their belonged jobs. Then, the generated batches are assigned to parallel machines to be processed. Motivated by the characteristics of machine maintenance activities in a semiconductor manufacturing process, we take the machine preventive maintenance into account, i.e., the machine should be maintained after a fixed number of batches have been completed. In order to solve the problem, we analyze several structural properties with respect to the batch formation and sequencing. Based on these properties, a hybrid BRKGA-DE algorithm combining biased random-key genetic algorithm (BRKGA) and Differential Evolution (DE) is proposed to solve the parallel-batching scheduling problem. A series of computational experiments is conducted to demonstrate the effectiveness and efficiency of the proposed algorithm.
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ISSN:1012-2443
1573-7470
DOI:10.1007/s10472-018-9602-1