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Research on Clone Immune Algorithm in Supply Chains of Spare Parts Distribution

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Tutor: QiaoPeiLi
School: Harbin University of Science and Technology
Course: Computer technology
Keywords: supply chains,spare parts,distribution,satisfaction,clone immune
CLC: TP18
Type: Master's thesis
Year:  2014
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Abstract:
In the current market situation of economic globalization, supply chainmanagement (SCM) as the advanced and efficient management, is one of theeffective ways to enhance the competitiveness of enterprises and economicbenefits. In recent years, with the development of market environment, thedevelopment direction of enterprises gradually develops from relatively matureproducts supply chain system to spare parts supply chain. Spare parts as the mainproduct in after-sales service area, have been attached more importance bymanufacturing companies. Especially, Due to the product profits are continuouslycompressed, after-sales service has become a key in marketing and manufacturing.Allocation optimization is a sub-problem of the supply chain management. Byconsidering the factors about distribution costs, construction costs and operatingcosts, rational coordination of all links of the supply chain network, thisallocation optimization makes the minimum total operating costs, thus realizemaximum profits goals.This paper mainly holds an emphasis on service spare parts supply chainsystem. Considering the characteristics of spare parts distribution, it builds aformation of multi-product distribution allocation model. To achieve“service-oriented” marketing purposes in after-sales business, it introduces themethod of the time-based customer satisfaction quantification. This paper studiessupply chains of spare parts distribution allocation problem usingcustomers’ satisfaction quantization function and integration of cost functionmethod. To establish spare parts supply chain allocation model with the premiseof the quality of services, it achieves a reasonable coordination in the logisticsnetwork members, while optimizing the overall realization of benefits.In model solution, the huge population size that genetic algorithm requireswill inevitably lead to much slower evolution, which is a systematical limitation of traditional Genetic Algorithm (GA). So, this paper proposes an improved cloneimmune genetic algorithm. Based on the merit-based selection of different fitnessindividuals, this improved method adaptively clones the superior individuals,thus increase the operation efficiency, decrease the time of iteration and couldobtain the optimal solution as soon as possible. Therefore, the performance of thealgorithm can be improved significantly. The result of the experiments provesthat the improved clone immune genetic algorithm shows better consequencethan the traditional GA.To verify the correctness of allocation model through the application ofautomotive spare parts distribution instance, use the traditional GA and cloningimmune algorithm for solving this model respectively. The calculation resultsobtained were compared to verify that employing clone immune algorithm forsolving the model is correct and reasonable.
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