An energy-efficient multi-objective permutation flow shop scheduling problem using an improved hybrid cuckoo search algorithm

Wenbin Gu,Min Dai,Zhuo Li,Minghai Yuan

doi:10.1177/16878140211023603

Abstract

The flow shop scheduling problem has been widely studied in recent years, but the research on multi-objective flow shop scheduling with green indicators is still relatively limited. It is urgent to strengthen the research on effective methods to solve such interesting problems. To consider the economic and environmental factors simultaneously, the paper investigates the multi-objective permutation flow shop scheduling problems (MOPFSP) which minimizes the makespan and total carbon emissions. Since MOPFSP is proved to be a NP-hard problem for more than two machines. A hybrid cuckoo search algorithm (HCSA) is proposed to solve the problems. Firstly, a largest-order-value method is proposed to enhance the performance of HCS algorithm in the solution space of MOPFSP. Then, an adaptive factor of step size is designed to control the search scopes in the evolution phases. Finally, a multi-neighborhood local search rule is addressed in order to find the optimal sub-regions obtained by the HCSA. Numerical experiments show that HCSA can solve MOPFSP efficiently.

Highlights

With the increasingly severe environmental problems in the manufacturing industry, the green manufacturing has received considerable attention.[1,2] The problem model studied in this paper contains environmental indicators, which meets the current needs of ecological environment governance
The multi-objective permutation flow shop problem (MOPFSP) with green indicators studied in this paper has a strong industrial background.[6]
The results show that the HCS algorithm can solve practical MOPFSP problems quickly and effectively

Summary

Introduction

With the increasingly severe environmental problems in the manufacturing industry, the green manufacturing has received considerable attention.[1,2] The problem model studied in this paper contains environmental indicators, which meets the current needs of ecological environment governance. It is of great significance to design cuckoo algorithm that can dynamically adjust step size control factor to solve the green flow shop scheduling problem.

Results

Conclusion