JIT供应物流的短驳合并运输集成优化研究

Optimization of Short Split Merged Transportation Problems about Inbound Logistics

针对及时化(just in time,JIT)供应模式的入场物流短驳合并优化问题,本文以某汽车制造厂的入厂物流为例,并以供应物流总成本最小为目标,建立数学模型,同时利用基于递降最佳适合算法(best fit decreasing,BFD)思想的启发式算法和改进的单亲遗传算法(partheno genetic algorithm,PGA)求解,并分析在此运作模式下的成本与效率。仿真结果表明,单亲遗传算法取消了传统遗传算法的交叉算子,取而代之仅在一条染色体上操作基因换位算子,即使种群中每个个体均相同,它也可以通过基因换位、基因到位等遗传算子来实现遗传迭代,不需要初始群体具有广泛多样性,也就不存在早熟收敛的现象。因此,本文提出的算法比BFD启发式算法的供应成本明显降低。该研究可降低供应成本,提高经济效益。

This paper addresses the optimization problem of the Just in time （JIT） supply logistics in the short split merge mode.It takes the inbound logistics of an automotive assembly plant as an example for simulation test,builds a mathematical model aimed at the minimum total cost,and uses a Heuristic algorithm based on the BFD thought and a Partheno genetic algorithm based on the idea of integer coding to solve it.In addition,the paper also analyzes the cost and efficiency of the operation mode.The simulation results show that the Partheno Genetic algorithm replaces the crossover operators of traditional genetic algorithms,instead,only on a chromosome gene transposition operator operation.Even if each individual populations are the same,it can also be transposed,place and other genetic operators to achieve genetic iterations performed.Don＇t need the initial group has wide diversity,and there is no ＂premature convergence＂ phenomenon.Therefore,the proposed algorithm significantly reduced the supply costs compared with BFD heuristic algorithm.This study could reduce supply costs and improve economic efficiency.