Parallel-batching machines scheduling problem with a truncated time-dependent learning effect via a hybrid CS-JADE algorithm. (2nd January 2020)

• Record Type: Journal Article
• Authors: Liu, Siwen; Liu, Xinbao; Pei, Jun; Pardalos, Panos M.; Song, Qingru
• Is Part Of: Optimization methods and software. Volume 35:Number 1(2020)
• Page Start: 116
• Page End: 141
• Publication Date: 2020-01-02
• Subjects: Scheduling -- parallel-batching -- time-dependent learning effect -- hybrid algorithm; Mathematical optimization -- Periodicals; Algorithms -- Periodicals; 519.7
• DOI: 10.1080/10556788.2019.1577415
• Languages: English
• ISSNs: 1055-6788

Abstract

ABSTRACT: This research investigates the parallel-batching scheduling problems with a time-dependent learning effect where the job processing time is a decreasing function of its starting time. Both the single-machine and parallel-machine circumstances are considered, and the objective is to minimize the makespan. For the single parallel-batching machine scheduling problem, some structural properties and a heuristic algorithm are developed to solve it. Since the parallel-batching machines scheduling problem is NP-hard, a hybrid CS-JADE algorithm combining improved cuckoo search algorithm (CS) and self-adaptive differential evolution (DE) is proposed to solve this problem. The computational experiments indicate that the proposed hybrid algorithm performs well both in effectiveness and efficiency.