Improved Fruit Fly Algorithm for Multi-Objective Disassembly Line Balancing Problem Considering Learning Effect

Abstract

Disassembly lines dismantle scrapped products to retain valuable components for recycling and remanufacturing. Many factors affect the productivity of disassembly lines, especially the operating cost of the workstation, the priority of the disassembly task, the skill level of the workers, and the learning proficiency. This study considers the learning effect of disassembly and assembly workers. It optimizes two objectives simultaneously: 1) maximizing the disassembly profit and 2) maximizing the learning outcomes of the workers. A bi-objective mixed integer programming model for the disassembly and assembly balance problem is established to explore the optimal solution. A multi-objective fruit fly optimization algorithm is proposed, which can better solve multi-objective optimization problems by simulating the foraging behaviour of fruit flies and finding the optimal solution set of multiple optimization objectives. The algorithm is compared with the CPLEX solver and other multi-objective optimization algorithms. The experimental results show that the algorithm has obvious advantages.