Research On Key Materials Sourcing Planning Under Supply Uncertainty For Equipment Manufacturing Enterprises

The structure of equipment product is very complex and a great variety of raw materials,parts,and finished products of it need to be purchased,outsourced or manufactured.There are some materials in equipment manufacturing enterprises,like key raw materials,series parts of make-to-order(MTO)equipment products and custom parts of engineering-to-order(ETO)equipment products,which have characteristics such as high customization,strict requirement in quality and reliability,complex production process,limited supply channels and capability,tense delivery lead time and high supply uncertainty,so multiple sourcing strategy is usually taken by companies.If these materials are delayed in delivery or out of stock,it will have great influence on in-time product production and on-time order delivery.Traditional risk response strategies like inventory mitigation,backup supply and emergency purchasing from spot market can make little sense in the purchasing process of these materials so they are the key and difficult point of equipment manufacturing purchasing management,called key materials in this paper.Thus,it makes great significance for equipment manufacturing enterprises to make scientific and reasonable key material purchasing planning,and make it coordinate with production planning,which is of great realistic significance for the equipment manufacturing enterprises to reduce the overall operation cost,increase production management efficiency,improve the responsiveness for supply uncertainty and strengthen the comprehensive competitiveness.Besides,it enriches the theory research about multiple sourcing planning under supply uncertainty.Based on the practical production background of equipment manufacturing enterprises,there are three common forms of supply uncertainty:supply disruption,uncertain supply quantity and uncertain delivery lead time.This study segments and extracts the problems of purchasing key materials under supply uncertainty according to the production mode of equipment products,characteristics of purchasing process,the supply requirements of the production of products and parts and the interrelation between sourcing planning and production planning under uncertainty.Three kinds of sourcing planning of key materials are systematically researched,that are key raw materials sourcing planning under supply disruption,series parts of MTO equipment product sourcing planning under uncertain supply quantity and custom parts of ETO product souring planning under uncertain supply time.The mathematical programming models are established and the corresponding intelligent algorithms are designed to solve these models.Specifically,the detailed researches are as follows:(1)For a kind of key raw materials with high performance requirement,long purchasing cycle,and large production difficulty,aiming at solving the problem of unmet demands due to random supply disruption,considering multi-constraints like initial supplier number requirement,minimum order lot size and so on,a two-stage multiple sourcing planning integer programming model for routine ordering and emergency replenishment is proposed.The large feasible solution space and high dimension of decision variables make traditional mathematical programming algorithm difficult to obtain satisfactory feasible solution under large-scale problems.Based on the standard particle swarm optimization algorithm(PSO),by improving the population initialization and update mechanism,and by designing the adaptive neighborhood length local search mechanism,an improved particle swarm optimization(IPSO)algorithm is designed to solve the model.The example shows that the proposed algorithm can effectively solve the complex two-stage multiple sourcing problem.Compared with the practical decision,it is proved that the two-stage sourcing planning can efficiently mitigate potential random supply disruptions,and it can also significantly reduce the total purchasing cost.Finally,the sensitivity analysis of the model parameters is carried out.(2)For a kind of series parts of MTO equipment products with multi-series,multi-variety,and small-batch production mode,aiming at solving the problem of material not kitting,infeasible production planning and delayed orders because of uncertain supply quantity,in consideration of the condition of suppliers' order delay and replenishment,and multi-constraints like material kitting,production combination constraint of different series products and so on,a multi-objective integer programming model is proposed for joint optimization of sourcing planning and production planning of multi-series products.A hybrid particle swarm optimization algorithm is proposed to solve the model.Based on the SPSO algorithm,the algorithm introduces the empirical rules-based initialization strategy,and then an adaptive neighborhood search(ANS)strategy based on DS operator and a diversity strategy based on stochastic mutation and population reconstruction are designed.Meanwhile,the simulated annealing(SA)algorithm controlled with probability is introduced.The effectiveness of the proposed algorithm is verified by an application example.Compared with independent decision-making,the joint sourcing planning and production planning can not only guarantee the optimal total purchasing value and the customer service level,but also reduce the fixed ordering cost and order delay penalty.Finally,sensitivity analysis of the model parameters is carried out.(3)For a kind of custom parts of ETO large whole-set of equipment products,aiming at solving the problem of infeasible original order sequence due to the random delay in delivery of suppliers,considering multi-constraints like sequential constraint of whole-set orders,limited capacity of critical equipment,and so on,from the perspective of mutual influence and restriction between sourcing and whole-set order planning,a discrete bilevel programming model for coordination optimization of sourcing planning and whole-set order sequencing in multiple planning periods is proposed.Both the upper and lower objective functions and constraints of the model are nonlinear and nonconvex and it is difficult to guarantee the uniqueness of the optimal solution for lower programming.Based on the multi-objective non-dominated sorting theories,a strategy based on hierarchical iterative evolutionary algorithm(HIEA),which is based on improved PSO algorithm and improved genetic algorithm(GA),is proposed.Then,the results of application example show that the proposed method can obtain better non-dominated solution sets and it has high efficiency.Compared with the experiential decentralized decision,the coordinated whole-set order sequencing and sourcing planning of the custom parts can not only reduce the total purchasing cost,but also improve the on-time delivery level.Finally,the model is extended to compare the optimal decisions under different optimization objectives of whole-set order problem.