Solving Scheduling Problem in a Distributed Manufacturing System Using a Discrete Fruit Fly Optimization Algorithm

Xiaohui Zhang, Xinhua Liu, Shufeng Tang, Grzegorz Królczyk and Zhixiong Li
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Xiaohui Zhang: School of Mechanical and Electrical Engineering, China University of Mining & Technology, Xuzhou 221116, China
Xinhua Liu: School of Mechanical and Electrical Engineering, China University of Mining & Technology, Xuzhou 221116, China
Shufeng Tang: School of Mechanical Engineering, Inner Mongolia University of Technology, Hohhot 010051, China
Grzegorz Królczyk: Department of Manufacturing Engineering and Automation Products, Opole University of Technology, 45758 Opole, Poland
Zhixiong Li: Suzhou Automotive Research Institute, Tsinghua University, Suzhou 215134, China

Energies, 2019, vol. 12, issue 17, 1-24

Abstract: This study attempts to optimize the scheduling decision to save production cost (e.g., energy consumption) in a distributed manufacturing environment that comprises multiple distributed factories and where each factory has one flow shop with blocking constraints. A new scheduling optimization model is developed based on a discrete fruit fly optimization algorithm (DFOA). In this new evolutionary optimization method, three heuristic methods were proposed to initialize the DFOA model with good quality and diversity. In the smell-based search phase of DFOA, four neighborhood structures according to factory reassignment and job sequencing adjustment were designed to help explore a larger solution space. Furthermore, two local search methods were incorporated into the framework of variable neighborhood descent (VND) to enhance exploitation. In the vision-based search phase, an effective update criterion was developed. Hence, the proposed DFOA has a large probability to find an optimal solution to the scheduling optimization problem. Experimental validation was performed to evaluate the effectiveness of the proposed initialization schemes, neighborhood strategy, and local search methods. Additionally, the proposed DFOA was compared with well-known heuristics and metaheuristics on small-scale and large-scale test instances. The analysis results demonstrate that the search and optimization ability of the proposed DFOA is superior to well-known algorithms on precision and convergence.

Keywords: energy saving and efficiency; distributed manufacturing system; blocking constraint; distributed flow shop scheduling; fruit fly optimization algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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