基于多目标猫群算法的混流装配线排序问题

Mixed model assembly line sequencing problem based on multi-objective cat swarm optimization

针对现实混流装配线上各工作站内设备闲置/超载的成本不同的问题,在传统的最小化闲置/辅助工作总成本目标的基础上,考虑不同工作站内设备闲置/超载成本的差异,建立了以改进的最小化工作站闲置/超载总成本、产品变化率和产品切换总时间为目标的多目标优化模型,并设计一种改进多目标猫群优化算法进行求解。提出一种基于线性混合比率的猫行为模式选择方法,以提高算法前期的全局搜索能力和后期的局部寻优能力;提出能生成分布广泛的候选个体、基于多样化搜寻算子的改进搜寻模式,拓展算法的搜索空间,提高算法的全局搜索能力。运用基准实例对所提算法与第二代非支配排序遗传算法、多目标粒子群算法、第二代强度Pareto进化算法进行比较,结果表明所提算法在解的收敛性、分布性和Pareto解的搜索能力上均具有优势。将该算法用于求解某实例企业的混流装配线排序问题,为车间调度人员的决策提供了多样化的选择,且优于车间已有方法的求解结果。

Aiming at the different cost of each workstation＇s idle/overload in practical Mixed Model Assembly Line （MMAL）, a multi-objective optimization model with the improved objective of minimum workstation＇s idle/overload total cost, production variation rate and total product set-up time was built based on traditional minimizing station＇s total idle/overload cost, and an improved multi-objective cat swarm optimization was designed to solve it. To im- prove the global searching ability in earlier stage and local ability in later stage of the algorithm, a method of eat be- havior mode selection based on liner mixed ratio was proposed. An improved seeking mode which could generate wide range candidate was presented based on diversified searching operator to extend the search range and improve the global searching ability of the algorithm. The proposed algorithm was compared with Non-dominated Sorting Genetic Algorithm- Ⅱ （NSGA- Ⅱ ）, Multi-Objective Particle Swarm Optimization（MOPSO） and improved Strength Pareto Evolutionary Algorithm （SPEA2）, and the results showed that the proposed algorithm had advantages on convergence of solutions, distributivity of solutions and search capabilities of Pareto solutions. The proposed algo- rithm was applied in an enterprise＇s Mixed Model Assembly Line Sequencing Problem （MMALSP）, and the diversi- fied choices were provided for workshop scheduling personnel decision, and the results were better than those of ex- isting method on case assembly line.