ハイブリッドGAによる需要変動を考慮した在庫付き多段階ロジスティクス?システム設計問題の研究

摘要

ロジスティクス問題には，時系列的に変化する顧客需要rnを満足させるために，効率的?発展的な「もの」の流れやrn保管などをコントロールし，全体的な最適化を図り，改善rnすることを目的としたものがある。rnロジスティクスの普及に伴い，各業務形態に即したロジrnスティクス?システムの研究が盛んに進められてきている。%The logistics model used in this study is 3-stage model employed by an automobile company, which aims to solve traffic problems at a total minimum cost. Recently, research on the metaheuristics method has advanced as an approximate means for solving optimization problems like this model. These problems can be solved using various methods such as the genetic algorithm (GA), simulated annealing, and tabu search. GA is superior in robustness and adjustability toward a change in the structure of these problems. However, GA has a disadvantage in that it has a slightly inefficient search performance because it carries out a multi-point search. A hybrid GA that combines another method is attracting considerable attention since it can compensate for a fault to a partial solution that early convergence gives a bad influence on a result. In this study, we propose a novel hybrid random key-based GA(h-rkGA) that combines local search and parameter tuning of crossover rate and mutation rate; h-rkGA is an improved version of the random key-based GA (rk-GA). We attempted comparative experiments with spanning tree-based GA, priority based GA and random key-based GA. Further, we attempted comparative experiments with "h-GA by Only local search" and "h-GA by only parameter tuning". We reported the effectiveness of the proposed method on the basis of the results of these experiments.