A Discrete Multi-Objective Rider Optimization Algorithm for Hybrid Flowshop Scheduling Problem Considering Makespan, Noise and Dust Pollution

Abstract

Many optimization algorithms have been proposed to solve hybrid flowshop scheduling problem (HFSP). However, with the development of industry and society, labor right and labor safety have become important problem to consider in production scheduling. So the green HFSP considering makespan, noise and dust pollution becomes an urgent problem to be solved. In this paper, the rider optimization algorithm (ROA) is modified into the multi-objective rider optimization algorithm (MOROA) using Pareto archive and neighborhood sorting techniques. The Pareto archive and neighborhood sorting technology make the Pareto optimal solution set of MOROA have higher coverage and more solutions. Then MOROA is discretized into discrete MOROA (DMOROA) to solve the HFSP considering makespan, noise and dust pollution. DMOROA is tested on 10, 30 and 50 jobs HFSP considering makespan, noise and dust pollution. The test results are compared with two multi-objective algorithms to verify the performance of DMOROA. And the test results verify that the DMOROA is superior to the comparison algorithms in search accuracy, number of non-dominated solutions, diversity of solution set and stability. Therefore, DMOROA is effective in solving multi-objective HFSP considering makespan, noise and dust pollution.

Keywords

• Hybrid flowshop scheduling problem
• multi-objective meta-heuristic algorithm
• rider optimization algorithm
• labor right