Sebastian. R. Gnanapragasam

Work place: Department of Mathematics, the Open University of Sri Lanka, Nawala, Nugegoda, Sri Lanka



Research Interests: Mathematics, Mathematics of Computing, Computational Mathematics


Mr. Sebastian R. Gnanapragasam is from Western Province, Sri Lanka. He is an M.Sc. graduate in Operations Research from the University of Moratuwa, Sri Lanka and currently reading for a PhD in Mathematics at the University of Peradeniya, Sri Lanka. His main research topics are Vehicle Routing Problem and Time Series Analysis. He is currently working as a Senior Lecturer in Mathematics at the Open University of Sri Lanka.

Author Articles
Optimal Solution to the Capacitated Vehicle Routing Problem with Moving Shipment at the Cross-docking Terminal

By Sebastian. R. Gnanapragasam Wasantha. B. Daundasekera

DOI:, Pub. Date: 8 Oct. 2022

An innovative and creative docking technique known as cross- docking (CD) strategy was initiated in 1930s to make supply chain fast and productive. However, it only became popular from 1980s. Vehicle routing between suppliers/customers to CD terminal (CDT) is one of operational level problems at CDT. Moreover, moving unloaded products from indoors to outdoors of CDT is one of the internal operations inside a CDT. The main difference between this study and the existing studies which find in the literature is that to consider the activities inside CDT. Also, loading or unloading shipments at all the nodes including CDT are taken into account. Moreover, homogenous fleets of vehicles within pickup or delivery process are assumed, but heterogeneous fleets of vehicles between pickup and delivery processes are assumed in this study. A mixed integer non-linear programming model is developed to address this problem. In our proposed model, costs of transportation between nodes, service at nodes including CDT, moving shipments inside CDT and vehicle operation are considered as the contributors to the total cost. The proposed model was tested for fifteen randomly generated small scale problems using Branch and Bound algorithm and the algorithm was run using LINGO (version 18) optimization software. The average computational time to reach the optimal solution is estimated. The study revealed that for small scale problems, the convergence rate of the problems rises to polynomial with degree 6. Also, the study shows that for moderately large and large scale problems the computational time to reach the optimal solution is exponential. Therefore, this study recommends using a suitable evolutionary algorithm to reach a near optimal solution for moderately large and large scale problems. It further recommends that, this model can be used for last time planning for similar small scale problems.

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