Clusters analysis application on transportation network

Abstract

The government of Sri Lanka established several economic centres in the provinces according to the budget proposals in the year 1998. The Dambulla economic centre was the first such centre that was established on the 01st of April 1999. Thereafter, a number of economic centres were established throughout the island. However, the Dambulla main hub remained the central warehouse of vegetables in the island. This paper deals with a vehicle scheduling problem related to transportation, and investigates a method whereby a solution can be arrived at to overcome the problem using linear programming (LP). Marketing Department Logistics (MDL) Ltd needs to distribute vegetables and fruits to different provinces. Its main hub is situated near the Dambulla vegetable and fruit market, and minor hubs are situated in different provinces in Sri Lanka. The main objective of this research is building a cost minimized model which creates a suitable method for delivering vegetables and fruits from the Dambulla major hub through its minor hubs to outlets in the provinces. Hence, to optimize the cost of outbound distribution, a mathematical model has been developed by using Integer Linear Programming, and by using reliable sources to collect data. Software assistance was obtained using the LINGO 06 optimizer, Java, MS Access and MS Excel tools to solve this mathematical model. This study is based on the Dambulla economic centre. This is an initial step to bring a correct protocol to arrange a transport model to distribute the vegetables and fruits from this centre in a cost-effective way. According to this study, all districts in Sri Lanka could be divided into four clusters. At the beginning of this research, we assumed that each district contains two warehouses and three vendors. This model is flexible enough to be re-scheduled at any request. It paves the way to create a larger model for solving any type of transportation planning problem.

Bibliography

1. Bazaraa, M. S., Jarvis, J. J., & Sherali, H. D. (1990). Linear programming and network flows. John Wiley & Sons.

2. Bidaud, J., & Safir, C. (2008). Pre study for a central warehouse. Central Europe: The school of industrial engineering and management of KTH.

3. Chalaturnyk, A. (2008). A fast algorithm for finding Hamilton cycles. Winnipeg: University of Manitoba.

4. Charnes, A., Glover, F., & Klingman, D. (1970). Letter to the Editor – A Note on a Distribution Problem. Operations Research, 18(6), 1213-1216.

5. Drezner, Z., Scott, C., & Song, J. S. (2009). The Central Warehouse Location Problem Revisited”. Irvine: University of California.

6. Hakim, A., & Kabir, R. (2017). An efficient approach for finding an initial basic of solution for transportation problems. Progress in Nonlinear Dynamics and Chaos, 5(1), 17-23.

7. Machackova, J. (2009). Economic evaluation of a warehouse investment in central Europe: Case study at Nokian Heavy Tyres Ltd. Europe: University of Applied Science.

8. Murty, K. (1992). Network programming. Prentice Hall, Upper Saddle River, N.J.

9. Pandian, P., & Natarajan, G. (2010). A new approach for solving transportation problems with mixed constraints. Journal of Physical Sciences, 14, 53-61.

10. Rodrigue, J. P., & Notteboom, T. (2010). Comparative North American and European gateway logistics: the regionalism of freight distribution. Journal of Transport Geography, 18(4), 497-507.

11. Sahoo, S., & Pal, B. (2012). Truck Allocation Model Using Linear Programming and Queueing Theory. Rourkela: National Institute of Technology.