An application study on vehicle routing problem based on improved genetic algorithm
ICPCA/SWS'12 Proceedings of the 2012 international conference on Pervasive Computing and the Networked World
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Despite the fact that the vehicle routing problem (VRP) with its variants has been widely explored in operations research, there is very little published research on the VRP concerning real world constraint combinations and large problem sizes. In this work a heuristic solution approach for the VRP with real world constraints is presented driven by the requirements defined by clients in the courier, express and parcel (CEP) delivery industry in order to support their routing plan decisions and driver assignments. The solution algorithm used combines several local-search-based heuristics with constructive elements to solve the VRP with driver learning (VRPDL). As conceptual proof large instances for the capacitated VRP (CVRP) including 560 to 1200 customers are tested and compared to known benchmark results. From those instances new sub-instances are created and sequentially tested adding the driver learning constraint. Finally, the solver is applied to real world CEP instances with driver learning.