# A knowledge based freight management decision support system incorporating economies of scale: multimodal minimum cost flow optimization approach

- 316 Downloads

## Abstract

This study developed a framework incorporating economies of scale into the multimodal minimum cost flow problem. To properly account for the economies of scale observed in practice, we explicitly modelled economies of scale on quantity, distance and vehicle size in a given multimodal freight network. The proposed multimodal minimum cost flow problem formulation has concave equations due to economies of scale for quantity, non-linear equations due to economies of scale for both quantity and distance, and non-continuous equations due to the economies of scale for vehicle size. A genetic algorithm was applied to find acceptable route, mode, and vehicle size choices for the multimodal minimum cost flow problem. We demonstrated how the economies of scale influenced system (mode), route choices, and total cost under various demand/service capacity scenarios. Our results will lead into more realistic assessments of intermodal system by explicitly considering the three types of economies of scale.

## Keywords

Decision support system Freight management Mode choice Minimum cost flow problem Economies of scale## References

- 1.Bärthel F, Woxenius J (2004) Developing intermodal transport for small flows over short distances. Transp Plan Technol 27:403–424CrossRefGoogle Scholar
- 2.Bontekoning YM, Priemus H (2004) Breakthrough innovations in intermodal freight transport. Transp Plan Technol 27:335–345CrossRefGoogle Scholar
- 3.Chang T-S (2008) Best routes selection in international intermodal networks. Comput Oper Res 35:2877–2891CrossRefGoogle Scholar
- 4.Cullinance K, Khanna M (1999) Economies of scale in large container ships. J Transp Econ Policy 33:185–208Google Scholar
- 5.Cullinane K, Khanna M (1999) Economies of scale in large container ships. J Transp Econ Policy 33:185–208Google Scholar
- 6.Deb K (2000) An efficient constraint handling method for genetic algorithms. Comput Methods Appl Mech Eng 186:311–338CrossRefGoogle Scholar
- 7.EC (2001) White Paper—European Transport Policy for 2010: time to decide. ECGoogle Scholar
- 8.ECMT (1998) Terminology on combined transport. ECMT(European Conference of Ministers of Transport), ParisGoogle Scholar
- 9.Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann ArborGoogle Scholar
- 10.Horner MW, O’Kelly ME (2001) Embedding economies of scale concepts for hub network design. J Transp Geogr 9:255–265CrossRefGoogle Scholar
- 11.Janic M (2007) Modelling the full costs of an intermodal and road freight transport network. Transp Res Part D 12:33–44CrossRefGoogle Scholar
- 12.Janic M (2008) An assessment of the performance of the European long intermodal freight trains (LIFTS). Transp Res Part A Policy Pract 42:1326–1339CrossRefGoogle Scholar
- 13.Jara-Díaz SR, Donoso PP, Araneda JA (1992) Estimation of marginal transport costs: the flow aggregation function approach. J Transp Econ Policy 26:35–48Google Scholar
- 14.Kim NS, Van Wee B (2009) Assessment of CO
_{2}emissions for truck-only and rail-based intermodal freight systems in Europe. Transp Plan Technol 32:313–330CrossRefGoogle Scholar - 15.Kreutzberger ED (2008) Distance and time in intermodal goods transport networks in Europe: a generic approach. Transp Res Part A Policy Pract 42:973–993CrossRefGoogle Scholar
- 16.McCann P (2001) A proof of the relationship between optimal vehicle size, haulage and the structure of distance-transport cost. Transp Res Part A 35:671–693Google Scholar
- 17.Michalewicz Z (1995) Genetic algorithms, numerical optimization, and constraints. In: Sixth international conference on genetic algorithms, Morgan Kauffman, San MateoGoogle Scholar
- 18.Michalewicz Z, Fogel DB (2000) How to solve it: modern heuristics. Springer, BerlinCrossRefGoogle Scholar
- 19.Michalewicz Z, Schoenauer M (1996) Evolutionary algorithms for constrained parameter optimization problems. Evol Comput 4:1–32CrossRefGoogle Scholar
- 20.O’Kelly ME, Bryan DL (1998) Hub location with flow economies of scale. Transp Res Part B 32:605–616CrossRefGoogle Scholar
- 21.Piramuthu S, Shaw MJ (2009) Learning-enhanced adaptive DSS: a Design Science perspective. Inf Technol Manag 10:41–54CrossRefGoogle Scholar
- 22.Racunica I, Wynter L (2005) Optimal location of intermodal freight hubs. Transp Res Part B 39:453–477CrossRefGoogle Scholar
- 23.Rees J, Koehler G (2001) Evolution in groups: a genetic algorithm approach to group decision support systems. Inf Technol Manag 3:213–227CrossRefGoogle Scholar
- 24.Skorin-Kapov D, Skorin-Kapov J, O’Kelly ME (1996) Tight linear programming relaxations of uncapacitated p-hub median problems. Eur J Oper Res 94:582–593CrossRefGoogle Scholar
- 25.Sikora R, Piramuthu S (2005) Efficient genetic algorithm based data mining using feature selection with Hausdorff distance. Inf Technol Manag 6:315–331CrossRefGoogle Scholar
- 26.USDOT (1991) The intermodal surface transportation efficiency act of 1991. U.S. Department of Transportation, WashingtonGoogle Scholar
- 27.Wang X, Wang H, Wang H, Zhang L, Cao X (2011) Constructing a decision support system for management of employee turnover risk. Inf Technol Manag 12(2):187–196CrossRefGoogle Scholar