An optimization technique using hybrid GA-SA algorithm for multi-objective scheduling problem

Abstract

A Mixed-model assembly line is widely employed to perform the assembly operation in industries and the time needed to release products to market is frequently considered by many researchers. However, providing an appropriate level of flexibility to meet customer demand variations is critical for companies survival in this competitive market. The problem of production planning in terms of sequencing various product model is studied here. A manufacturing system is presented to show the application of this problem. A proposed multi-objective function is given to minimize the overall make-span of a mixed-model assembly line, but with additional goals also considered, such as balancing the assembly line and minimizing the variation of completion time. We propose a solution aimed to solve the problem in successive stages. For each stage, a mathematical model formally describes the problem and the main difficulties faced are explained. Due to the high complexity of problem solving procedures by classical mathematic techniques, this paper presents a new approach of hybrid genetic algorithm-simulated annealing (GA-SA) implementation in order to meet the problem objectives. A proposed hybrid scheme is executed to overcome problem complexity and to meet the problem objectives. In order to check the efficiency of hybrid search techniques, a comparison is made between the results obtained by hybrid GA-SA and GA, and the comparison validates the effectiveness of the presented hybrid search technique.