The Vehicle Routing Problem With Simultaneous Pickup And Delivery And Soft Time Windows

With the continuous improvement of the specialization level of logistics, logistics distribution industry has also been developed quickly in our country. In the distribution activities, the vehicle routing problem of distribution is one of the key problems to realize distribution rationalization, the optimization of the vehicle routing will help enterprises to reduce logistical cost, improve the quality of service and enhance economic efficiency. In practice work, in order to save cost, improve efficiency and protect environment, distribution enterprises often operate simultaneously the distribution of forward logistics and the recycling of reverse logistics, then forming the vehicle routing problem with simultaneous delivery and pickup. So, it is of theoretical and practical senses to some extents to study the vehicle routing problem with simultaneous delivery and pickup.The main research of this thesis is the vehicle routing problem with simultaneous pickup and delivery and soft time windows (VRPSPDSTW). First, the knowledge of logistics distribution and the classic vehicle routing problem and their solution algorithms are described, on this basis, it reviews the characteristics and the past studies on the VRPSPDSTW, and sets up the solution model about VRPSPDSTW considering the coefficient of vehicle fully loaded and designs an improved genetic algorithm to solve it. This algorithm uses heuristics cross operator based on the optimal of distance and applies the exchange and shift approaches to produce offspring, and the adaptive mechanism for dynamic adjustment of the probability of the crossover and variation is also used. At last, According to analysis of properties of the algorithm and comparing with the result of the related literature by solving the same example shows that the improved genetic algorithm can get the better satisfactory solution of the VRPSPDSTW, while the stability and convergence of the algorithm is also better.