Abstract
This paper addresses a heterogeneous fleet vehicle routing problem (VRP) having simultaneous pickup and delivery, which plays an important role in the green logistics. The decision making is on both the fleet composition and the vehicle routings. We formulate the problem into a mathematical model and propose an evolutionary algorithm (EA) hybridized with the nearest neighborhood algorithm (NNA). The EA has two populations. The first one is employed for representing vehicles to be used to travel to customers and the second one is for representing each route which is assigned to each vehicle. Permutation representation is used for encoding individuals in both of two populations. After we obtain a solution by combining and decoding the information of each individual from both populations, the NNA is applied to get a better route for each vehicle. Two populations evolve separately in each generation. Truncation selection, order crossover, and swap mutation operation are commonly adopted for each of both populations. The developed EA can be implemented into one of two approach, either GA-based one or CCEA-based one according to the pattern of evaluating individuals from each of both populations. A computational study is carried out to compare GA-based approach with CCEA-based one. Numerical experiments show that our GA-based approach tends to outperform CCEA-based one in terms of the quality of solutions generated.
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