On solving the multi-vehicle routing problem with stochastic demand: mathematical formulations and efficient heuristics

• Main Authors: Yu-hsin Hsu, 許玉欣
• Other Authors: Shine-Der Lee
• Format: Others
• Language: zh-TW
• Published: 2007
• Online Access: http://ndltd.ncl.edu.tw/handle/09090590870878908328

碩士 === 國立成功大學 === 工業與資訊管理學系碩博士班 === 95 === 　　We consider the multi-vehicle routing problem, in which demands of customers are stochastic. Due to this randomness, a vehicle may fail to satisfy the demand of some customers when accumulated demands of customers in a designated route exceed the capacity of the vehicle. Cost to reschedule alternative route or dispatch additional vehicle to meet customer’s demand incurred when the route failure occurs. 　　In this thesis, the stochastic vehicle routing problem is formulated as a 0-1 integer programming model with probabilistic constraint. Due to the complexity of mathematical model, a streamlined two-stage formulation is then proposed to address this difficult problem. At the first stage, each customer is assigned to a vehicle by the reformulated stochastic linear assignment model, subject to the route failure constraint. The expected operating, including vehicle routing and route failure, is then computed for each vehicle at the second stage. 　　A heuristic solution procedure is developed to solve this stochastic vehicle routing problem. A simplified assignment model, based on expected demand of customers, is solved to determine the routing zone of each vehicle. The optimum route for each vehicle to serve the customers is then determined by solving traveling salesman problem; and the expected total routing cost is finally computed. Numerical experiments, with problem sizes up to 35 customers and 8 vehicles, have been performed. Our computational tests have shown that the expected operating cost of the routes developed by the new approach is significantly lower than that of the classical dynamic approach. The new approach also outperforms the existing approaches in terms of computational times or resources.