Genetic algorithm for a delivery problem with mixed time windows

Abstract

This research aims to improve transportation planning decisions for a production company, which produces seasoning powder in Thailand and the logistics provider. Due to restrictions in Bangkok and its metropolitan area, the routing problem becomes one with two types of time windows. A mixed integer programming model is formulated, which aims to minimize a cost function which consists of fixed vehicle costs, variable vehicle costs and fuel costs. This approach has its limits in terms of problem size. Therefore a genetic algorithm (GA) has been developed to approximate the optimal solution. The proposed GA has a specific initialization algorithm which generates feasible random solutions. A partial factorial design of GA parameters is implemented to determine the suitable parameter values, which guide the genetic algorithm. The solution of the GA and the mixed integer programming model of the current problems were compared. The maximum optimal gap was between 0 and 0.21%, while the computational time was reduced between 67.78 and 99.45%. The results show that the planning time by a dispatcher is reduced significantly and the cost is strongly reduced, due to the fact that less vehicles are used.