Determination of Fleet Size for LTL Transportation With Dynamic Demand

  • Ko, Chang Seong (Department of Industrial Engineering, Kyungsung University) ;
  • Chung, Ki-Ho (Department of Management Information Systems, Kyungsung University) ;
  • Shin, Jae-Yeong (Department of Logistics Engineering, Korea Maritime University)
  • 발행 : 2002.11.01

초록

This study suggests an approach for determining fleet size for LTL (less -than-truckload) transportation with dynamic demand for on-time supply of the parts between the assembly line in an automobile company and its part suppliers in Korea. The vehicles operated by the transportation trucking companies in Korea in general can be classified into three types depending on the ways how their expenses occur; company -owned truck, mandated truck which is owned by outsider who entrusts the company with its operation, and rented truck (outsourcing) . With the forecasted monthly production data a year, a heuristic algorithm is developed to determine the number of company-owned trucks, mandated trucks, and rented trucks in order to minimize the expected annual operating cost, which is based on the solution technologies used in the aggregate production planning and vehicle routing problem. Finally the algorithm is tested for the problem how the trucking company transports parts for the automobile company.

키워드

참고문헌

  1. Akinc, U. and G. M. Roodman, 'A new approach to aggregate production planning,' IIE Transactions 18 (1986), 88-94 https://doi.org/10.1080/07408178608975334
  2. Bodin, L. D., B. L. Golden, A. A. Assad, and M. O. Ball, 'Routing and scheduling of vehicles and crews: the state of the art,' Computers and Operations Research 10 (1983), 63-211 https://doi.org/10.1016/0305-0548(83)90030-8
  3. Bowman, E. H., 'Production scheduling by the transportation method of linear programming,' Operations Research 4 (1956), 100-103 https://doi.org/10.1287/opre.4.1.100
  4. Buxey, G., 'A managerial perspective on aggregate planning,' International Journal of Production Economics 41 (1995), 127-133 https://doi.org/10.1016/0925-5273(94)00070-0
  5. Glover, F., 'Tabu search: tutorial,' Interfaces 20 (1990), 74-94 https://doi.org/10.1287/inte.20.4.74
  6. Ko, C. S., J. Y. Shin, K. H. Chung, H. Hwang, and K. H. Kim, 'An analytical approach for allocation and scheduling of container vehicles in Korea,' Proceeding of International Conference on Production Research 2000, Bangkok, Thailand, 2-4 August
  7. Logendran, R. and C. S. Ko, 'Manufacturing cell formation in the presence of flexible cell locations and material transporters,' Computers and Industrial Engineering 33 (1997), 545-548 https://doi.org/10.1016/S0360-8352(97)00189-7
  8. Nam, S. J. and R Logendran, 'Aggregate production planning - a survey of models and methodologies,' European Journal of Operational Research 61 (1992), 255-272 https://doi.org/10.1016/0377-2217(92)90356-E
  9. Posner, M. E. and W. Szwarc, 'A transportation type aggregate production model with backlogging,' Management Science 29 (1983), 188-199 https://doi.org/10.1287/mnsc.29.2.188
  10. Rosenkrantz, D., R. Sterns, and P. Lewis, 'An analysis of several heuristics for the traveling salesman problem,' SIAM Journal of Computing 6 (1977), 563-581 https://doi.org/10.1137/0206041
  11. Singhal, K. and V. Adlakha, 'Cost and shortage trade-offs in aggregate production planning,' Decision Science 20 (1989), 158-164 https://doi.org/10.1111/j.1540-5915.1989.tb01404.x
  12. Tadei, R, M. Trubian, J. L. Avendano, F. Della Croce, and G. Menga, 'Aggregate planning and scheduling in the food industry: a case study,' European Journal of Operational Research 87 (1995), 564-573 https://doi.org/10.1016/0377-2217(95)00230-8