A coordinated approach to hedge the risks in stochastic inventory-routing problem
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 I RP is to design the set of routes and delivery quantities that minimize transportation cost while controlling inventory costs. Traditional IRP focuses Oil 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. Ill 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, all 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 oil 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. Crown copyright (C) 2008 Published by Elsevier Ltd. All rights reserved.
inventory routing problem (irp) black-scholes model garch model forward option pricing model particle swarm optimization (pso)