- 學位論文
以避險手法解決存貨分配最佳化問題
Hedging Inventory Risks in the Distribution Optimization Problem
摘要
本研究係在一個配銷中心面對多個銷售商，面臨動態需求變化環境下，來探討在長期規劃下存貨配送問題 (Inventory Routing Problem)之最佳化。此存貨配送最佳化問題之最主要目標是在存貨成本控制規劃下，設計相關配送路線與存貨配送量使其總成本達到最小化。不同於傳統存貨配送問題之探討係站在風險中立之考量下，本研究係從風險規避之角度來考量多產品、多期間在長期規劃下，協同整合存貨補貨政策與運輸政策以達到總利潤之最大化或總存貨成本之最小化。在此前提下，本研究乃從避險角度上，結合GARCH 模型 (Generalized Autogressive Conditional Heteroskedasticity Model)與遠期選擇權模式(Forward Option Pricing Model)，以避險手法來建置一套可解決多產品、多期間動態需求下之存貨配送系統(A hedge-based stochastic inventory-routing system, HSIRS)。從實例演算中，證實本研究所提出之避險系統不僅可達到風險規避之目的，更可在一定服務水準要求下，達到淨現值(Net Present Value, NPV)最大化之目標。依此，多產品組合下之最佳補貨政策與共同補貨周期亦可在淨現值最佳化前提下求得。同時，不同於其他研究主要係從地理所在位置為配送路線規劃客戶選擇之依據，本研究係以客戶之存貨補貨週期(Inventory Review Period)為配送客戶群選擇之依據，並以改良式粒子群聚最佳化演算法(Modified Particle Swarm Optimization, PSO)進行運輸指派以達到運輸成本最小化之目標。
並列摘要
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, we propose an approach for incorporating risk aversion in multi-item multi-period inventory policy model that coordinate inventory and transportation management. A hedge-based stochastic inventory-routing system (HSIRS) integrated with Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model and Forward Option Pricing 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 modified Particle Swarm Optimization algorithm.