Parameterisation of demand-driven material requirements planning: a multi-objective genetic algorithm
针对需求驱动物料需求计划（DDMRP）参数难以确定的问题，提出一种多目标遗传算法，同时优化准时交货率和库存水平，并通过仿真验证了有效性。
Demand-Driven Material Requirements Planning (DDMRP) is a recent inventory management method that has generated considerable interest in both academia and industry. Many recent papers have demonstrated the superiority of DDMRP over classical methods like MRP or Kanban, an observation confirmed by companies that have implemented DDMRP. However, DDMRP depends on many parameters that affect its performance. Only general rules are given by the authors of the method to fix these parameters but no algorithm. The present paper aims to fill this gap by proposing a multi-objective optimisation algorithm to fix a set of eight identified parameters. The suggested genetic algorithm is coupled with a simulation algorithm that computes the objective functions. Two opposing objective functions are considered: first, the maximisation of orders delivered on-time to the customer and, second, the minimisation of on-hand inventory. A set of data instances was generated to test the suggested method. Fronts of non-dominated solutions are found for all these instances.