Modified honey bees mating optimization algorithm for multi-objective uncertain integrated process planning and scheduling problem

Xiaoyu Wen,Xinyu Li,Kanghong Wang,Liang Gao,Hao Li

doi:10.1177/1729881420925236

Abstract

The new technology of intelligent manufacturing makes the data of process planning and shop scheduling easier to interconnect, and the integration optimization of different manufacturing processes is an important technology to ensure the implementation of intelligent manufacturing. Integrated process planning and scheduling is a significant research focus in recent years, which could improve the performance of manufacturing system. At present, the research on integrated process planning and scheduling is insufficient to consider the multi-objective and uncertain characteristics widely existing in real manufacturing environment. Therefore, multi-objective integrated process planning and scheduling problem with uncertain processing time and due date is addressed in this article. The mathematical model of multi-objective uncertain integrated process planning and scheduling problem with uncertain processing time and fuzzy due date is established based on fuzzy set, in which the calculation method of uncertainty measurement objective is designed. An effective modified honey bees mating optimization algorithm has been designed to solve the proposed model. Queens set is constructed to maintain the non-dominated solutions found in the optimization process. The calculation method of mating probability between drone and queen bee based on Euclidean distance is designed. Fuzzy operators were utilized to evaluate fitness, judge the non-dominated relationship, and decode the scheduling solution. Different instances were designed and carried out to test the performance of the proposed method. The results show that the proposed method is very effective for solving multi-objective uncertain integrated process planning and scheduling.

Highlights

Modern manufacturing faces multiple pressures from the economy, society, and the environment, and manufacturing companies need to further improve efficiency, stabilize production, and enhance brand effects
With the rise of intelligent manufacturing, the new technology of intelligent manufacturing makes the data of process planning and shop scheduling easier to interconnect, and the integration optimization of different manufacturing processes is an important technology to ensure the implementation of intelligent manufacturing.[6,7,8]
The bold-faced values indicate that the values obtained by modified HBMO (MHBMO) are better than the values obtained by non-dominated sorted genetic algorithm-II (NSGA-II)

Summary

Introduction

Modern manufacturing faces multiple pressures from the economy, society, and the environment, and manufacturing companies need to further improve efficiency, stabilize production, and enhance brand effects. Research on the theory of operational optimization of advanced manufacturing systems is important for the development of manufacturing companies. To match the real-time status of the workshop, 20%–30% of the previously processed plans must be replanned.[5] With the rise of intelligent manufacturing, the new technology of intelligent manufacturing makes the data of process planning and shop scheduling easier to interconnect, and the integration optimization of different manufacturing processes is an important technology to ensure the implementation of intelligent manufacturing.[6,7,8] Integrated process planning and scheduling (IPPS) can effectively overcome the conflicts caused by independent research, improving the efficiency of manufacturing systems.[9]

Results

Discussion

Conclusion