An improved artificial bee colony algorithm for addressing distributed flow shop with distance coefficient in a prefabricated system

This paper proposes an improved artificial bee colony (IABC) algorithm for addressing the distributed flow shop considering the distance coefficient found in precast concrete production system, with the minimisation of the makespan. In the proposed algorithm, each solution is first represented by a...

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Bibliographic Details
Published inInternational journal of production research Vol. 57; no. 22; pp. 6922 - 6942
Main Authors Li, Jun-qing, Bai, Shun-Chang, Duan, Pei-yong, Sang, Hong-yan, Han, Yu-yan, Zheng, Zhi-xin
Format Journal Article
LanguageEnglish
Published London Taylor & Francis 17.11.2019
Taylor & Francis LLC
Subjects
Algorithms
artificial bee colony algorithm
distance coefficient
distributed scheduling
Factories
flow shop
Industrial plants
Job shops
Operation scheduling
Optimization
Precast concrete
prefabricated production
Prefabrication
Production scheduling
Reinforced concrete
Search algorithms
Swarm intelligence
Workload
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Summary:This paper proposes an improved artificial bee colony (IABC) algorithm for addressing the distributed flow shop considering the distance coefficient found in precast concrete production system, with the minimisation of the makespan. In the proposed algorithm, each solution is first represented by a two-dimensional vector, where the first dimensional vector is the factory and the second dimensional vector lists the operation scheduling sequence of each factory. Second, considering the distributed problem feature, a distributed iterated greedy heuristic (DIG) is developed where destruction and construction processes are designed in detail while considering the distributed structures. Third, an efficient population initialisation method that considers the factory workload balance is presented. Then, a local search approach that randomly replaces two factories with two randomly selected jobs and that finds an optimal position for the two inserted operations via the DIG method is proposed. For the canonical ABC algorithm, using the DIG approach, the main three parts are improved, namely, the employee, onlooker, and scout bees. Finally, the proposed algorithm is tested on sets of extended instances based on the well-known benchmarks. Through an analysis of the experimental results, the highly effective proposed IABC algorithm is compared to several efficient algorithms drawn from the literature.
ISSN:0020-7543
1366-588X
DOI:10.1080/00207543.2019.1571687