Brainstorming Optimization Algorithm For Disassembly Line Balancing Problem

Since the rapid development of science and technology and the accelerated upgrading rate of electronic products,dealing with the recovery,recycling,and remanufacturing of end-of-life products is of increasing importance.The design and balance of the disassembly line are of great significance to the economic benefits of remanufacturing enterprises and the concept of sustainable development in China.This paper studies a Multi-product Multi-objective Disassembly-line-balancing Problem(MMDP).According to the characteristics of MMDP,both the mathematical models of the Multi-product Linear Disassembly-line-balancing Problem(MLDP)and the Multi-product U-shaped Disassembly-line-balancing Problem(MUDP)are established with these optimization objectives of maximizing the profit and minimizing the smoothness index.Considering the complexity of MMDP and the excellent performance of a brainstorming optimization algorithm in solving optimization problems,this paper improves the brainstorming optimization algorithm to solve MMDP.The improvements of the algorithm designed in this paper are as follows:(1)The feasible solution generation method,a double-phase heuristic algorithm is used to generate feasible solutions under the constraints of task precedence and conflict relationships;(2)The design process of a clustering algorithm,combined with the characteristics of multi-objective optimization problems,a clustering algorithm based on the Pareto dominance relation is designed to improve the convergence of the algorithm;(3)The process of a new individual generation,according to the characteristics of the disassembly line balancing problem,different crossover and mutation operators in a genetic algorithm is used to replace the random number disturbance,which increases the diversity of the algorithm;(4)The selection phase,fast non-dominated sorting and crowding distance calculation methods are used to update the population,which improves the search efficiency of the algorithm.For the MLDP,an MLDP model and a multi-robotic MLDP model are established.The feasibility and effectiveness of the proposed algorithm are verified by solving these two models.Numerical experimental results show that the algorithm has better performance than other classical algorithms.For the MUDP,an MUDP model and a multi-robotic MUDP model are constructed.The algorithm is applied to solve the two models,and the feasibility and efficiency of the algorithm are tested by some real-world instances.Compared with the linear disassembly line,the U-shaped disassembly line can allocate tasks more flexibly,which improves the utilization of workstations.