A coordinated approach to hedge the risks in stochastic inventory-routing problem

  • Authors:

  • Yee Ming Chen;Chun-Ta Lin

  • Affiliations:

  • Department of Industrial Engineering and Management, Yuan Ze University, 135 Yuanding RD, Chung-li, Taoyuan, 320 Taiwan, ROC;Department of Information Management, Yu-Da College of Business, 168, Hsueh-fu Rd., Tan-wen Village, Chao-chiao Township, Miao-li, Taiwan, ROC

  • Venue:
  • Computers and Industrial Engineering
  • Year:
  • 2009

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Abstract

The inventory routing problem (IRP) studied in this research involves repeated delivery of products from a depot to a set of retailers that face stochastic demands over a long period. The main objective in the IRP is to design the set of routes and delivery quantities that minimize transportation cost while controlling inventory costs. Traditional IRP focuses on risk-neutral decision makers, i.e., characterizing replenishment policies that maximize expected total net present value, or equivalently, minimize expected total cost over a planning horizon. In this research, for incorporating risk aversion, a hedge-based stochastic inventory-routing system (HSIRS) integrated with Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model and Forward Option Pricing (FOP)model based on Black-Scholes model, from hedge point of view, is proposed to solve the multi-product multi-period inventory routing problem with stochastic demand. Computational results demonstrate the importance of this approach not only to risk-averse decision makers, but also to maximize the net present value at an acceptable service level. As a result, an optimal portfolio (R, s, S) system of product group can be generated to maximize the net present value under an acceptable service level in a given planning horizon. Meanwhile, the target group needed to be served and the relative transportation policy also can be determined accordingly based on the time required to be served as a priori partition to minimize the average transportation costs; hence, the routing assignment problem can be successfully optimized through a Predicting Particle Swarm Optimization algorithm.