An inventory-transportation system with stochastic demand

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• 作者：Luca Bertazzi (1)
  Simona S. M. Cherubini (1)
  
• 关键词：Transportation ; Inventory ; Reoptimization ; Monte Carlo Simulation
• 刊名：Computational Management Science
• 出版年：2013
• 出版时间：February 2013
• 年：2013
• 卷：10
• 期：1
• 页码：1-20
• 全文大小：226KB
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• 作者单位：Luca Bertazzi (1)
  Simona S. M. Cherubini (1)
  
  1. Department of Quantitative Methods, University of Brescia, Brescia, Italy
  
• ISSN：1619-6988

文摘

We study a logistic system in which a supplier has to deliver a set of products to a set of retailers to face a stochastic demand over a given time horizon. The transportation from the supplier to each retailer can be performed either directly, by expensive and fast vehicles, or through an intermediate depot, by less expensive but slower vehicles. At most one time period is required in the former case, while two time periods are needed in the latter case. A variable transportation cost is charged in the former case, while a fixed transportation cost per journey is charged in the latter case. An inventory cost is charged at the intermediate depot. The problem is to determine, for each time period and for each product, the quantity to send from the supplier to the depot, from the depot to each retailer and from the supplier to each retailer, in order to minimize the total expected cost. We first show that the classical benchmark policy, in which the demand of each product at each retailer is set equal to the average demand, can give a solution which is infinitely worse with respect to the optimal solution. Then, we propose two classes of policies to solve this problem. The first class, referred to as Horizon Policies, is composed of policies which require the solution of the overall problem over the time horizon. The second class, referred to as Reoptimization Policies, is composed of a myopic policy and several rolling-horizon policies in which the problem is reoptimized at each time period, once the demand of the time period is revealed. We evaluate the performance of each policy dynamically, by using Monte Carlo Simulation.