A collaborative iterative greedy algorithm for the scheduling of distributed heterogeneous hybrid flow shop with blocking constraints. (1st September 2022)

• Record Type: Journal Article
• Authors: Qin, Hao-Xiang; Han, Yu-Yan; Liu, Yi-Ping; Li, Jun-Qing; Pan, Quan-Ke; Xue-Han,
• Is Part Of: Expert systems with applications. Volume 201(2022)
• Publication Date: 2022-09-01
• Subjects: Blocking -- Distributed heterogeneous hybrid flow shop problems -- Collaborative iterative greedy algorithm -- Energy efficiency; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.117256
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Mathematical notations and constraints of DHHFSP with blocking constraints is formulated. NEH-Increase heuristic strategy is designed to allocate the jobs to heterogeneous factories. The neighborhood search strategies cooperatively optimize the scheduling sequence. A local intensification strategy is presented to reinforce the exploration ability. Abstract: The hybrid flow shop and distributed flow shop problems have been extensively studied due to their wide industrial applications. However, the distributed heterogeneous hybrid flow shop problems (DHHFSP) with blocking constraints have not yet been well studied up to date. This paper considers how to arrange a variety of jobs to different heterogeneous factories, and each factory has a minimal makespan. The innovations of this paper lie in presenting a mathematical model of the DHHFSP with blocking constraints and designing a collaborative iterative greedy (CIG) algorithm. The CIG contains the problem-specific initialization strategy, the neighborhood search strategy, the destruction-reconstruction strategy, and the local intensification strategy. The cross-factory and inner-factory neighborhood search strategies based on two swap operators are adopted to reduce the blocking time. The local intensification strategy is developed to optimize the scheduling sequence of each factory. The proposed algorithm is empirically compared with five state-of-the-art algorithms on 60 different instance sets. The experimental results show that the proposed algorithm significantly outperforms the compared ones in terms of objective values and relative percentage deviation values.