Serial-batching group scheduling with release times and the combined effects of deterioration and truncated job-dependent learning

Wenjuan Fan, Jun Pei (), Xinbao Liu (), Panos M. Pardalos and Min Kong
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Wenjuan Fan: Hefei University of Technology
Jun Pei: Hefei University of Technology
Xinbao Liu: Hefei University of Technology
Panos M. Pardalos: University of Florida
Min Kong: Hefei University of Technology

Journal of Global Optimization, 2018, vol. 71, issue 1, No 10, 147-163

Abstract: Abstract This paper investigates a single machine serial-batching scheduling problem considering release times, setup time, and group scheduling, with the combined effects of deterioration and truncated job-dependent learning. The objective of the studied problem is to minimize the makespan. Firstly, we analyze the special case where all groups have the same arrival time, and propose the optimal structural properties on jobs sequencing, jobs batching, batches sequencing, and groups sequencing. Next, the corresponding batching rule and algorithm are developed. Based on these properties and the scheduling algorithm, we develop a hybrid VNS–ASHLO algorithm incorporating variable neighborhood search (VNS) and adaptive simplified human learning optimization (ASHLO) algorithms to solve the general case of the studied problem. Computational experiments on randomly generated instances are conducted to compare the proposed VNS–ASHLO with the algorithms of VNS, ASHLO, Simulated Annealing (SA), and Particle Swarm Optimization (PSO). The results based on instances of different scales show the effectiveness and efficiency of the proposed algorithm.

Keywords: Scheduling; Serial-batching; Group scheduling; Release time; Deterioration; Truncated job-dependent learning (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (7)

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DOI: 10.1007/s10898-017-0536-7

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