Cassava Transportation Planning under Uncertain Demand Using Hybrid Algorithm: Case Study of Roi Et Province

Abstract:

Cassava transportation planning usually involves unexpected demand, which may result in shortage supply. Furthermore, a distribution center at which cassava is collected is difficult to be located since the demand is unknown. In this research, hybrid forecasting model for predicting future demand in order to determine transshipment points is proposed. In addition, cluster analysis and particle swarm optimization are used for creating potential zones and determine a proper location as a new hub. Finally, the optimal value of a transportation network model using both forecasted value and actual value obtained from linear programming technique are tested and compared. The results indicate that the hybrid forecasting model provides the lowest error and forecasting value provides average error of optimal value compared to actual value by 19.81%. Moreover, zoning technique can be able to improve shipping volume fulfilled to a large truck.

• Periodical: Advanced Materials Research (Volumes 931-932)
• Pages: 1658-1663
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.931-932.1658
• Online since: May 2014
• Authors: Thoranin Sujjaviriyasup, Komkrit Pitiruek*
• Keywords: Hybrid Forecasting Model, K-Means Cluster, Particle Swarm Optimization (PSO), Transportation Network

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