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

Abstract

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.

Keywords:

• prefabricated production
• flow shop
• distributed scheduling
• artificial bee colony algorithm
• distance coefficient

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research was partially supported by the National Natural Science Foundation of China [grant numbers 61773192, 61773246, and 61803192]; the Shandong Province Higher Educational Science and Technology Program [grant number J17KZ005]; the Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education [grant number K93-9-2017-02]; the State Key Laboratory of Synthetical Automation for Process Industries [grant number PAL-N201602], and major basic research projects in Shandong [grant number ZR2018ZB0419].