A Bacterial Foraging Optimization Algorithm And Simulation Analysis Based Method For Balancing Multi-Objective Disassembly Line

Due to more rigid environmental legislation and government regulation, and the economic attractiveness of reusing products, manufacturing enterprises pay more and more attention to the recycling of waste products. Disassembly is the first critical step of product recovery. Organizing disassembly production process in a flowing way will not only improve disassembly efficiency but also promote the development of its automation and industrialization.The optimization procedure of disassembly line balancing problem (DLBP) involves dealing with multiple objectives owing to its complexity. The main objectives to be achieved for the DLBP are as follows:equilibrate workload, remove hazardous and high-demand components as early as possible, minimize the disassembly cost and minimize the direction change times. Traditional algorithms could not handle the conflict between objectives properly and might get local optimum prematurely. To hedge against these shortcomings, the paper proposed a Pareto based multi-objective bacterial foraging optimization (MBFO) algorithm. The algorithm used a Pareto non-dominated sorting operator to grade the bacterial population. For those solutions which belong to the same grade, it adopted a crowding distance operator for the second rank. After chemotaxis phase, the algorithm introduced an elitism preservation strategy so that it would improve the convergence performance of the proposed algorithm. Furthermore, the algorithm used a global information sharing strategy to guide the bacterial population searching toward the well distributed Pareto optimal front. The proposed algorithm was tested in using series of numerical experiments with different size. For small scale example, compared with the known methods, the MBFO algorithm could give 6 additional balanced solutions which have different emphasis, so they can provide the manufacturing producers a larger decision-making space. For large scale example, the performances of three optimization objectives were improved by 50.4%,7.6% and 14.9% respectively.While balancing a specific disassembly line for printers’recovery, the paper took into account a fact that invalid operation time would affect the line’s cycle time. Thus, a disassembly line mathematical model with set-up times was established. Solutions which meet the line’s planning capacity with lower disassembly cost and higher operating efficiency were gained by using the proposed MBFO algorithm. The simulation analysis of the solutions showed that lots of blocking and waiting exist on the line due to the fluctuation of operating time and the occurrence of equipment failure. In order to improve the effectiveness of the line, the paper ameliorated the line by adding a buffer zone behind each workstation. The simulation results of the ameliorated solution indicated that the line’s average blocking rate was down by 38.75%, and its average waiting rate was down by 69.91%, and the line’s throughput was up by 20.40%. As a result, the design process of balancing a disassembly line would be more practicable by using the method of combining algorithm optimization and simulation analysis. Hence the method has a strong application value.