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. INDEX TERMS Hybrid flowshop scheduling problem, multi-objective meta-heuristic algorithm, rider optimization algorithm, labor right.

Social Sustainability in Production Planning: A Systematic Literature Review

“…GA [73] j-s (1) GA [74] f-s (1) DMORA [75] f-s (1) MOCGWO [76] j-s (5) GA [77] p-m X -con. [78] p-m X X heuristic [79] p-m X heuristic AP-Affecting Productivity, con.-constraint, DMORA-Discrete Multi-Objective Rider optimization Algorithm, EE-Energy Expenditure, EM-Effect Modeling, EP-Employee Preferences, ER-Ergonomic Risks, f-s-flow shop scheduling, GA-Genetic Algorithm, j-s-job shop scheduling, MOCGWO-Multi-Objective Cellular Grey Wolf Optimizer, p-m-parallel-machine scheduling, s-m-single-machine scheduling, VL-Value Limiting, (1)noise emission, (5)-combined/general assessment methods.…”

“…Furthermore, the ergonomic risks from noise emission and from a combined/general risk assessment are considered. A machine-dependent noise emission (according to [80]) is addressed in [72,73] for a job shop configuration and in [74] for flow shop scheduling according to an individual machine noise matrix. A machine-dependent and operationmode-dependent noise emission (based on [73]) is introduced by [75] for a flow shop configuration.…”

“…A machine-dependent and operationmode-dependent noise emission (based on [73]) is introduced by [75] for a flow shop configuration. Next to the noise emission, further objectives are directly minimized within a multi-objective function, for example makespan [72][73][74][75], energy consumption [73,75], and total dust pollution [74]. Ref.…”

Impact Review Analysis & Scope of Noise Pollution for Energy Harvesting

“…Where x is solution vector,Let p and q are solution; Solution p can dominate q if and only if fi(p)≤fi(q). Using this technique, energy consumption can be minimized but computational time is more than the meta heuristic method [Fu et al, 2020;Sener and Koltun, 2018]..…”

Multi-Objective Optimization of Switched Reluctance Machine Design Using Jaya Algorithm (MO-Jaya)

“…Optimization is the search for the best solution (the optimal solution) for a certain problem [3]. Various types of optimization algorithms exist, including traditional optimization techniques that have many limitations and advanced population-based meta-heuristic algorithms [4][5][6]. Optimization is needed with a switched reluctance machine (SRM) design to reach better designs.…”