Acta Scientiarum Naturalium Universitatis Sunyatseni ›› 2020, Vol. 59 ›› Issue (4): 89-97.doi: 10.13471/j.cnki.acta.snus.2019.03.30.2019B031

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Location routing problem with outsourcing strategy 

JIN Wenzhou, ZHU Zixuan, WU Weitiao, LI Ting   

  1. School of Civil and Transportation EngineeringSouth China University of TechnologyGuangzhou 510640China
  • Received:2019-03-30 Online:2020-07-25 Published:2020-07-25

Abstract:  The rapid development of logistics outsourcing has brought new opportunities for the supply chain integration design. This paper proposes a location routing problem planning model with the outsourcing strategy, with the objective to reduce the logistics system cost via partially outsourcing vehicle tasks. Considering the constraints in actual operation such as time windows, and the composition of self-delivery and outsourcing transportation costs, we develop a model to minimize the total cost of the logistics network by a jointly optimizing the distribution of facility locations and vehicle routing decision. The system includes fixed cost of self-vehicle and variable transportation costs, the fixed costs of distribution center construction, and the transportation costs of outsourcing. According to the characteristics of the problem, the hierarchical coding method is designed for genetic algorithm. Through the simulation example, the proposed strategy is compared with the traditional complete self-delivery and outsourcing operation mode. The results show that the proposed strategy could effectively reduce the additional cost of self-delivery and the construction cost of the distribution center, thus reducing the system cost. Our proposed is experimentally proved to reduce the system cost by 8.08% and 25.03% compared to the complete self-delivery and outsourcing operation mode, respectively. Finally, the impact of different outsourcing price on the systematic decisions is investigated by sensitivity analysis.

Key words: transportation economy,  , location routing problem,  , outsourcing distribution,  , time window,  , genetic algorithm

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