Supply Chain Distribution Network Optimization Model And Algorithm With Grey Demand

A supply chain distribution network is an important component in a supplychain. In supply chain management, the optimization problems of distributionnetworks have always been a key strategic issue. It was considered as animportant research in the field of logistics and supply chain management. Inrecent years, the uncertain optimization of distribution networks in supply chainhas become a focused research. At present, domestic and foreign scholarshave done extensive and in-depth researches to the randomness and fuzziness,but have not yet worked on the greyness in the optimization problems ofdistribution networks in the supply chain. In actuality, there is plenty ofinformation about the uncertainty in the supply chain distribution network, whichincludes random information, fuzzy information, and also includes greyinformation, for instance grey customer demand. Grey demand uncertaintyexists objectively,but it doesn’t get enough attention in the theoreticalresearch.Thus, the study on the optimization problem of distribution networks inthe supply chain under grey demand will undoubtedly be important fortheoretical study and practical application.The research results are asupplement and perfection of supply chain distribution network optimization,and improve in practice the capacity of dealing with uncertain information ofgrey demand in the process of building a distribution network in supply chain.Firstly, this thesis researches dimensional reduction algorithm for nodes inoptimization of distribution networks in the supply chain.The dimension disastercaused by the exponential increase of dimensions of nodes,becomes abottleneck of supply chain distribution network optimization problem.Themethod of weighted Topsis based on grey correlation analysis isestablished.The number of distribution network facility nodes can be reduced toan acceptable level, thus greatly reducing the computational complexity ofsolution algorithm for distribution network optimization model.The effectivenessof this method is finally proved by example of supplier selection.Secondly, to focus on typical four-stage distribution network of supply chain with many suppliers, plants, distribution centers and sales centers, distributionnetwork optimization model considering many raw materials and products ispresented,which is based on the problem of supply chain distributionoptimization under grey demand. The tasks of this problem involve the choice ofthe facilities (plants and distribution centers) to be opened and the distributionnetwork designed to satisfy the demand with minimum cost. According to thespecialty of the grey optimization model, the model is transformed into the greychance-constrained model,and then two hybrid PSO intelligent algorithmsbased on grey chance-constrained programming are proposed for it. One issolution algorithm for distribution network grey optimization model based onhybrid intelligent algorithms，which adopted two-layer solution mechanism.Theupper layer mixed with simulation annealing algorithm in the method forimproving the algorithm’s performance to solve the choice of the facilities(plants and distribution centers) to be opened.The lower layer develops PSOand grey simulation to solve the distribution network design problem to satisfythe demand. The other algorithm is solution algorithm for distribution networkgrey optimization model based on optimization of the position coefficient. PSOis used in the process of optimizing the position coefficient in grey number’swhitening transformation,and grey simulation technology is used to process thecomplex opportunity restriction.So the methods of optimizing of grey number’swhitening transformation have been enriched.The computer simulation showsthat the model is feasible and the algorithm is effective. And the solutionanalysis is provided.Finally, this thesis summarizes results obtained, and then presents someproblems for further study.