A two-stage three-machine assembly scheduling flowshop problem with both two-agent and learning phenomenon. (April 2019)

• Record Type: Journal Article
• Authors: Wu, Chin-Chia; Chen, Jia-Yang; Lin, Win-Chin; Lai, Kunjung; Bai, Danyu; Lai, Sz-Yun
• Is Part Of: Computers & industrial engineering. Volume 130(2019)
• Page Start: 485
• Page End: 499
• Publication Date: 2019-04
• Subjects: Flow shop scheduling -- Two-stage assembly -- Two-agent -- Hybrid particle swarm optimization -- Total completion time -- A position-based learning function; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2019.02.047
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: We study a 2-stage 3-machine assembly problem with two-agent and learning phenomenon. Several dominances and a lower bound derived are use in the branch-and-bound algorithm. Four versions of hybrid particle swarm optimization algorithms are proposed for finding approximate solutions. Abstract: Two-stage three-machine assembly flow shop, multiple-agent scheduling problems, and scheduling models with time-dependent processing times have been separately receiving continuous attention on research community. All the three phenomena have been shown to exist in many real applications, but no study has so far integrated the two-stage assembly flow shop, two-agent, and time-dependent processing times simultaneously. In view of this lack of integrated study, we investigated a two-stage three-machine assembly flow shop scheduling problem with both two-agent and learning phenomenon. Our objective was minimizing the total completion time of the first agent's jobs subject to a given upper bound imposed on the total completion time of the second agent's jobs. To solve the problem, some dominant propositions and three lower bounds were first derived to be used in the branch-and-bound algorithm for the small-size jobs. Then, four versions of hybrid particle swarm optimization algorithms were proposed to find approximate solutions for small-size and big-size jobs, respectively.