Quantitative analysis of multi-periodic supply chain contracts with options via stochastic programming

We propose a stochastic programming approach for quantitative analysis of supply contracts, involving flexibility, between a buyer and a supplier, in a supply chain framework. Specifically, we consider the case of multi-periodic contracts in the face of correlated demands. To design such contracts, one has to estimate the savings or costs induced for both parties, as well as the optimal orders and commitments. We show how to model the stochastic process of the demand and the decision problem for both parties using the algebraic modeling language AMPL. The resulting linear programs are solved with a commercial linear programming solver; we compute the economic performance of these contracts, giving evidence that this methodology allows to gain insight into realistic problems.stochastic programming; supply contract; linear programming; modeling software; decision tree

Aggregate constrained inventory systems with independent multi-product demand: control practices and theoretical limitations

In practice, inventory managers are often confronted with a need to consider one or more aggregate constraints. These aggregate constraints result from available workspace, workforce, maximum investment or target service level. We consider independent multi-item inventory problems with aggregate constraints and one of the following characteristics: deterministic leadtime demand, newsvendor, basestock policy, rQ policy and sS policy. We analyze some recent relevant references and investigate the considered versions of the problem, the proposed model formulations and the algorithmic approaches. Finally we highlight the limitations from a practical viewpoint for these models and point out some possible direction for future improvements

Exact Fill Rates for (R, s, S) Inventory Control With Gamma Distributed Demand

For the familiar (R; s; S) inventory control system only approximate expressions exist for the fill rate, i.e. the fraction of demand that can be satisfied from stock.Best-known are the approximations derived from renewal theory by Tijms & Groenevelt (1984), holding under specific conditions; in particular, S ¡ s should be reasonably large.They considered, more specifically, the cases of normally and gamma distributed demand.Here, an exact expression for the fill rate is derived, holding generally in the situation that demand has a gamma distribution with known integer-valued parameters, while lead time is constant.This formula is checked through extensive simulations; besides, detailed comparisons are made with Tijms & Groenevelt's approximation.demand;inventory control;simulation

Computing (R, S) policies with correlated demand

This paper considers the single-item single-stocking non-stationary stochastic lot-sizing problem under correlated demand. By operating under a nonstationary (R, S) policy, in which R denote the reorder period and S the associated order-up-to-level, we introduce a mixed integer linear programming (MILP) model which can be easily implemented by using off-theshelf optimisation software. Our modelling strategy can tackle a wide range of time-seriesbased demand processes, such as autoregressive (AR), moving average(MA), autoregressive moving average(ARMA), and autoregressive with autoregressive conditional heteroskedasticity process(AR-ARCH). In an extensive computational study, we compare the performance of our model against the optimal policy obtained via stochastic dynamic programming. Our results demonstrate that the optimality gap of our approach averages 2.28% and that computational performance is good

A numerical study of expressions for fill rate for single stage inventory system with periodic review.

Fill rate is one of the most important measurements for inventory systems in the supply chain management. The primary goal of this thesis is to give a comprehensive review of existing analytical expressions for the system fill rate, and provide numerical comparison for all relevant expressions in terms of their accuracy against (simulated) fill rate from the Monte Carlo simulation. We prove relationships between several expressions. Although majority of the expressions discussed herein are designed for standard periodic review system, we conduct numerical simulations for the general periodic review system. Under this general periodic review setting, numerical results indicate that all else being equal, replenishment lead time has larger effect on the system\u27s fill rate than does the review interval. In addition, numerical comparison suggests that Johnson et al.\u27s approach, Zhang and Zhang\u27s approach, Hadley and Whitin\u27s approach dominate the traditional approach, exponential approximation and Silver\u27s modified approach. The dominance is especially true for cases with high demand variability. For general periodic review system, our numerical results indicate that scaling is necessary for Silver\u27s modified, Johnson et al.\u27s and Johnson et al.\u27s modified approaches

An integrated model for cash transfer system design problem

This paper presents an integrated model that incorporates strategic, tactical, and operational decisions for a cash transfer management system of a bank. The aim of the model is to decide on the location of cash management centers, number and routes of vehicles, and the cash inventory management policies to minimize the cost of owning and operating a cash transfer system while maintaining a pre-defined service level. Owing to the difficulty of finding optimal decisions in such integrated models, an iterative solution approach is proposed in which strategic, tactical, and operational problems are solved separately via a feedback mechanism. Numerical results show that such an approach is quite effective in reaching greatly improved solutions with just a few iterations, making it a promising approach for similar integrated models

[[alternative]]A Study of Some Stochastic Inventory Models for Controllable Lead Time

計畫編號：NSC89-2213-E032-013研究期間：199908~200007研究經費：196,000[[sponsorship]]行政院國家科學委員

Forecasting the Intermittent Demand for Slow-Moving Items

Organizations with large-scale inventory systems typically have a large proportion of items for which demand is intermittent and low volume. We examine different approaches to forecasting for such products, paying particular attention to the need for inventory planning over a multi-period lead-time when the underlying process may be non-stationary. We develop a forecasting framework based upon the zero-inflated Poisson distribution (ZIP), which enables the explicit evaluation of the multi-period lead-time demand distribution in special cases and an effective simulation scheme more generally. We also develop performance measures related to the entire predictive distribution, rather than focusing exclusively upon point predictions. The ZIP model is compared to a number of existing methods using data on the monthly demand for 1,046 automobile parts, provided by a US automobile manufacturer. We conclude that the ZIP scheme compares favorably to other approaches, including variations of Croston's method as well as providing a straightforward basis for inventory planning.Croston's method; Exponential smoothing; Intermittent demand; Inventory control; Prediction likelihood; State space models; Zero-inflated Poisson distribution