工业工程 ›› 2013, Vol. 16 ›› Issue (2): 48-52.

• 专题论述 • 上一篇    下一篇

GRASP在多对一配送网络中ITIO问题上的应用

  

  1. 1.上海理工大学 管理学院,上海 200093;2.威海职业学院 信息工程系, 山东 威海 264210;3. 鲁东大学 交通学院,山东 烟台 264025
  • 出版日期:2013-04-30 发布日期:2013-06-08
  • 作者简介:裴英梅(1974-),女,朝鲜族,辽宁省人,博士研究生,主要研究方向为物流系统工程、供应链管理、最优化理论与方法.
  • 基金资助:

    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)

Application of GRASP to ITIO Problem in ManytoOne Distribution Network

  • Online:2013-04-30 Published:2013-06-08

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。

关键词: 库存与运输, 整合优化, 贪婪随机自适应搜索算法

Abstract: 〖WT5”BZ〗(1. School of Management, University of Shanghai for Science and Technology, Shanghai 200093, China;  2. Information Engineering Department, Weihai Vocational College, Weihai 264210, China;  3. School of Communications, Ludong University, Yantai 264025, China) 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 unlimited) in many-to-one distribution network in the modern 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.

Key words: inventory and transportation, integrated optimization, greedy randomized adaptive search procedure