Parallel-batching scheduling with nonlinear processing times on a single and unrelated parallel machines

Min Kong (), Xinbao Liu, Jun Pei (), Panos M. Pardalos and Nenad Mladenovic
Additional contact information
Min Kong: Hefei University of Technology
Xinbao Liu: Hefei University of Technology
Jun Pei: Hefei University of Technology
Panos M. Pardalos: University of Florida
Nenad Mladenovic: Mathematical Institute, Serbian Academy of Sciences and Arts

Journal of Global Optimization, 2020, vol. 78, issue 4, No 3, 693-715

Abstract: Abstract Parallel-batching processing and job deterioration are universal in the real industry. Scholars have deeply investigated the problem of parallel-batching scheduling and the problem of scheduling with deteriorating jobs separately. However, the situations where both parallel-batching processing and job deterioration exist simultaneously were seldom considered. This paper studies the parallel-batching scheduling problem with nonlinear processing times on a single machine, and proposes several structural properties and an optimal algorithm to solve it. Based on the above properties and optimal algorithm for the single machine setting, we further study the problem of parallel-batching scheduling with nonlinear processing times under the unrelated parallel machine setting. Since the unrelated parallel machines scheduling problem is NP-hard, a hybrid SFLA-VNS algorithm combining Shuffle Frog Leap Algorithm (SFLA) with Variable Neighborhood Search Algorithm (VNS) is proposed. Computational experiments and comparison are finally conducted to demonstrate the effectiveness of the proposed algorithm.

Keywords: Parallel-batching; Scheduling; Nonlinear processing times; Meta-heuristic algorithm (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10898-018-0705-3

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