A Hybrid Algorithm for Multi-Objective Optimization of Minimizing Makespan and Total Flow Time in Permutation Flow Shop Scheduling Problems

Abstract

In this work, a hybrid algorithm has been proposed to solve bi-objective permutation flow shop scheduling problem. The primary concern of flow shop scheduling problem considered in this work is to obtain the best sequence, which minimizes the makespan and the total flow time of all jobs. Bi-objective issues are comprehended by doling out uniform weight to every objective function in view of its preference or determining every competent solutions. In the flow shop scheduling environment, many meta-heuristic algorithms have been used to find optimal or near-optimal solutions due to the computational cost of determining exact solutions. This work provides a hybridization of genetic algorithm and simulated annealing algorithm (HGASA) based multi-objective optimization algorithm for flow shop scheduling. The proposed HGASA algorithm is used to solve a bi-objective problem that minimizes the makespan and the total flow time. The performance of the proposed algorithm is demonstrated by applying it to benchmark problems available in the OR-Library. The test results show that the HGASA algorithm performed better in terms of searching quality and efficiency than other meta-heuristic algorithms.

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