Stochastic Programming for the Optimization of Transportation-Inventory Strategy

  • Deyi, Mou (Institute of Mathematics for Applications, Civil Aviation University of China) ;
  • Xiaoqian, Zhang (Institute of Mathematics for Applications, Civil Aviation University of China)
  • Received : 2016.03.07
  • Accepted : 2016.10.06
  • Published : 2017.03.30


In today's competitive environment, supply chain management is a major concern for a company. Two of the key issues in supply chain management are transportation and inventory management. To achieve significant savings, companies should integrate these two issues instead of treating them separately. In this paper we develop a framework for modeling stochastic programming in a supply chain that is subject to demand uncertainty. With reasonable assumptions, two stochastic programming models are presented, respectively, including a single-period and a multi-period situations. Our assumptions allow us to capture the stochastic nature of the problem and translate it into a deterministic model. And then, based on the genetic algorithm and stochastic simulation, a solution method is developed to solve the model. Finally, the computational results are provided to demonstrate the effectiveness of our model and algorithm.


Transportation-Inventory;Joint Optimization;Random Demand;Stochastic Dynamic Programming;Hybrid Intelligent Algorithm


Supported by : Central Universities


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