A principal component analysis using SPSS for Multi-objective Decision Location Allocation Problem

摘要

In order to solve the location allocation problem with multi-objective decision (MDLAP), this paper creatively combines a cost-based mathematical optimization model, which transforms the distribution location problem into a two-stage logistics location selection decision one. In the process of solving the bottom model, this paper puts forward many methods such as data standardization, entropy weight, principal component analysis and mathematical expressions by using SPSS soft. In the process of solving the top model, this paper use immune algorithm to apply experimental simulation which based on logistics demand and location data. In the process of analysis, there are many methods to be provided to solve the above model, such as the weighted linear regression method, the similarity analysis system clustering method, the principal component regression method and the Immune algorithm. As a result, the 97 candidate service area in Shandong Province are selected into 9 service areas of Wei Fang, Qingdao, Ping Du, Q Fu and so on, in order to be the better optimal logistics development area. This model avoids the ambiguity of the traditional methods, and we can better solve the optimal number and location problem in the LAP.