Intelligent Optimization Algorithms For The P-Median Problem

Location problems are typical in operations research. It exists everywhere. It has beenstudied widely for almost one hundred years since it was developed. People research how toimprove the location models or how to design right algorithms for corresponding models.This paper studies mainly about the application of intelligent optimization algorithms for thep-median problem, which is one of the most typical location problems.Part one introduces the background of facility location problems, including thesignificance, evolutive stages, typical classification, and present research status, followed bythe main research of this paper.Part two mentions basic definition and widely used integer programming formulation ofthe p-median problems.In part three, It describes in detail on the basic procedures, critical elements with crucialinfluence on the performances, and general characteristics of the tabu search algorithm andgenetic algorithm of intelligent optimization algorithms.In part four, it concentrates specially on the application of tabu search algorithm andgenetic algorithm in solving the p-median problem. Based on existed algorithms, a newimproving tabu search algorithm for uncapacitated p-median problem is put forward, whichuses objective function difference as evaluation function instead of objective function inoriginal Rolland efficient tabu search algorithm. It also proposes an improving geneticalgorithm according to the evolutional principle for uncapacitated p-median problem, which isbased on Osman et cls' efficient genetic algorithm. They differ in the selecting of parents ofcrossover operator. Numerical instances show that both two improving algorithms performbetter than original algorithms. It also shows how to design tabu search algorithm and geneticalgorithm for the capacitated p-median problem at the end of this section.The last part concludes the research of this paper and presents the future research onp-median problems.