Abstract:
The vehicle routing problem (VRP) is a well known in logistics operation. The vehicle routing problem with simultaneous delivery and pickup (VRPSDP) is an extension of VRP that considers simultaneous distribution and collection of goods to/from customers. It is a critical and vital problem in reverse logistics. This thesis studies the vehicle routing problem with simultaneous delivery and pickup (VRPSDP) for the healthy environment. This research develops a mathematical optimization model for VRPSDP called environmental vehicle routing problem with simultaneous delivery and pickup (EVRPSDP) model by usingthe traveling distance and the load of vehicle. The aim of this model is to determine the efficient vehicle route under optimizing the fuel consumption of a vehicle, which results in reducing energy consumption as well as pollutant emissions in the air (GHG). A Hybrid Genetic Algorithm (HGA) is designed in this research to solve the fuel optimization (EVRPSDP) model efficiently. In order to compare the operational efficiency of HGA, genetic algorithm (GA) is implemented to solve the EVRPSDP model. The computational experiment is conducted and the results of computational experiments show the performance of HGA is superior to that of GA in terms of the total fuel consumption (L) of a vehicle.
