摘要
通过应用贪婪随机自适应搜索算法(GRASP)求解多对一配送系统中的库存与运输整合优化问题(ITIO),解决了在系统中产品种类、供应商数量或车辆运载能力增加时,计算量呈指数性增加而难以得到优化解的难题。首先,运用距离比例启发式算法获得初始解;其次,运用供应商转移指派算法在其邻域寻找最佳解;第三,以上两步的反复迭代获得最优解。通过算例分析验证了GRASP算法在解决ITIO问题时能迅速找到优化解,解的质量随着问题规模的扩大而改善。
It is known that the computational complexity in solving the integrated inventory-transportation optimization （ITIO） problem is exponential with the number of product types, the number of suppliers, and vehicle capacity. Thus, it is very difficult to obtain an optimal solution. To solve this problem, in view of different combinations of vehicle capacity （ limited or unlimited） and shipping frequency （ limited or un- limited） in many-to-one distribution network in the modem distribution logistics system, this problem is solved by using greedy randomized adaptive search procedure （GRASP） in this paper. It is a three-stage method. At stage 1, distance ratio heuristic is applied to obtain an initial feasible solution. At stage 2, supplier assignment transfer algorithm is applied to search for the best solution in its neighborhood so as to improve the solutions obtained from stage 1. At stage 3, it repeats the procedure of stages 1 and 2 in an iterative way until a global best solution is achieved. Numerical experiments show that the proposed method can find a good solution with less computation. Also, the solution quality increases as the problem size increases.
出处
《工业工程》
北大核心
2013年第2期48-52,共5页
Industrial Engineering Journal
基金
教育部人文社会科学规划基金资助项目(10YJA630187)
高等学校博士点基金资助项目(20093120110008)
上海市重点学科建设资助项目(S30504)
上海市研究生教育创新基金资助项目(JWCXSL1021)
鲁东大学校基金资助项目(LY2011008)
关键词
库存与运输
整合优化
贪婪随机自适应搜索算法
inventory and transportation
integrated optimization
greedy randomized adaptive search pro-cedure
