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

An Empirical Analysis of Forecast Sharing in the Semiconductor Equipment Supply Chain

We study the demand forecast-sharing process between a buyer of customized production equipment and a set of equipment suppliers. Based on a large data collection we undertook in the semiconductor equipment supply chain, we empirically investigate the relationship between the buyer\u27s forecasting behavior and the supplier\u27s delivery performance. The buyer\u27s forecasting behavior is characterized by the frequency and magnitude of forecast revisions it requests (forecast volatility) as well as by the fraction of orders that were forecasted but never actually purchased (forecast inflation). The supplier\u27s delivery performance is measured by its ability to meet delivery dates requested by the customers. Based on a duration analysis, we are able to show that suppliers penalize buyers for unreliable forecasts by providing lower service levels. Vice versa, we also show that buyers penalize suppliers that have a history of poor service by providing them with overly inflated forecasts

Supplier Selection under Uncertainty: A Detailed Case Study

The part of purchasing in supply chain management has received huge attention as the years go by. Purchasing increases efficiency and competitiveness among other benefits but to realize these benefits it is imperative to select and maintain competent suppliers. However, many factors affect a fir

Multi-Echelon Inventory Optimization and Demand-Side Management: Models and Algorithms

Inventory management is a fudamental problem in supply chain management. It is widely used in practice, but it is also intrinsically hard to optimize, even for relatively simple inventory system structures. This challenge has also been heightened under the threat of supply disruptions. Whenever a supply source is disrupted, the inventory system is paralyzed, and tremenduous costs can occur as a consequence. Designing a reliable and robust inventory system that can withstand supply disruptions is vital for an inventory system\u27s performance.First we consider a basic type of inventory network, an assembly system, which produces a single end product from one or several components. A property called long-run balance allows an assembly system to be reduced to a serial system when disruptions are not present. We show that a modified version is still true under disruption risk. Based on this property, we propose a method for reducing the system into a serial system with extra inventory at certain stages that face supply disruptions. We also propose a heuristic for solving the reduced system. A numerical study shows that this heuristic performs very well, yielding significant cost savings when compared with the best-known algorithm.Next we study another basic inventory network structure, a distribution system. We study continuous-review, multi-echelon distribution systems subject to supply disruptions, with Poisson customer demands under a first-come, first-served allocation policy. We develop a recursive optimization heuristic, which applies a bottom-up approach that sequentially approximates the base-stock levels of all the locations. Our numerical study shows that it performs very well.Finally we consider a problem related to smart grids, an area where supply and demand are still decisive factors. Instead of matching supply with demand, as in the first two parts of the dissertation, now we concentrate on the interaction between supply and demand. We consider an electricity service provider that wishes to set prices for a large customer (user or aggregator) with flexible loads so that the resulting load profile matches a predetermined profile as closely as possible. We model the deterministic demand case as a bilevel problem in which the service provider sets price coefficients and the customer responds by shifting loads forward in time. We derive optimality conditions for the lower-level problem to obtain a single-level problem that can be solved efficiently. For the stochastic-demand case, we approximate the consumer\u27s best response function and use this approximation to calculate the service provider\u27s optimal strategy. Our numerical study shows the tractability of the new models for both the deterministic and stochastic cases, and that our pricing scheme is very effective for the service provider to shape consumer demand

An EOQ model with multiple suppliers and random capacity

We consider an EOQ model with multiple suppliers that have random capacities, which leads to uncertain yield in orders. A given order is fully received from a supplier if the order quantity is less than the supplier's capacity; otherwise, the quantity received is equal to the available capacity. The optimal order quantities for the suppliers can be obtained as the unique solution of an implicit set of equations in which the expected unsatisfied order is the same for each supplier. Further characterizations and properties are obtained for the uniform and exponential capacity cases with discussions on the issues related to diversification among suppliers. © 2005 Wiley Periodicals, Inc

An inventory model with two suppliers under yield uncertainity

Cataloged from PDF version of article.In this study, an inventory model with one retailer and two suppliers is considered for a single item. Di erent from most of the models in inventory literature, we do not make the assumption that we receive all the quantity that we ordered. It is assumed that a random fraction of the lot size is actually delivered by the suppliers. Hence, the model is constructed under yield uncertainty for both binomial yield and stochastically proportional yield model. The demand rate is constant, and backordering is allowed. The ob jective is to minimize the long-run average cost and nd the near optimal values for the decision variables; order quantities and reorder point. Furthermore, the regions where diversi cation among suppliers is bene cial are investigated. The results are generalized to \M" suppliers (M>2) and solution method is proposed. Finally, experimental study is carried out for the two-suppliers problem.Gürbüz, Mustafa ÇağrıM.S

The Application of Dynamic Models in Operations Management

The dynamic program is a principal method for analyzing stochastic optimization problems. This dissertation studies three operations management problems that arise in the dynamic environment. The principal motivation behind these comes from the applicability in three areas: the agricultural supply chain, the container shipping industry, and supply chain financing. In the first chapter, we consider the hog production industry, where the hog raising farm should decide the selling strategy among several selling options. The farm also faces the uncertain yield of different weights of hogs and spot price volatility from other interactive markets. In the second chapter, we formulate a blockchain-based cargo reservation system, where a token is designed to be used as a booking deposit to compensate the contractual party if the other side fails to honor the booking, i.e., the overbooking from the service provider and customer no-show. In the third chapter, we study advance payment as a financing instrument in a multitier supply chain to mitigate the supply disruption risk and compare the traditional system (with limited visibility) with the blockchain-enabled system (with perfect visibility). The main goal of this chapter is to shed light on how blockchain adoption impacts agents\u27 operational and financial decisions and profit levels in a multitier supply chain. We apply the genre of dynamic models to formulate all three problems, but we address them by different methodologies because of the difference in the contexts. The first two problems possess structural properties adequate to find the optimal structural policy for a dynamic program, whereas the last problem can be applied to game theory. In the hog production chapter, we find that the optimal selling strategy for the hog farm is non-monotone. The counter-intuitive situation, namely, the farm does not fulfill the long-term contract but sells to the open market to speculate the high spot price, happens when the open market is good enough. We also propose a newsvendor-like heuristic policy that improves the profit of the hog farm by over 25%. We find the service provider has different acceptance strategies for the maritime container shipping problem with and without overbooking. He always prefers reliable customers without overbooking but prefers unreliable customers with overbooking in some circumstances. In the deep-tier supplier chain finance, take a game-theoretic approach to compare how blockchain-enabled deep-tier financing schemes affect a financially constrained supply chain\u27s optimal risk-mitigation and financial strategies. We find that although improved visibility via blockchain adoption can help the manufacturer make informed supply chain financing decisions, whether it can benefit all supply chain members depends on the financing schemes in use. Blockchain-enabled delegate financing increases risk-mitigation investments and benefits all three tiers of the supply chain only when tier-2 is severely capital-constrained with the working capital below a threshold. Because delegate financing endows the intermediary tier-1 supplier leverage over the manufacturer, the inefficiency inhibits an all-win outcome when the tier-2 is not severely capital-constrained. Blockchain-enabled cross-tier direct financing exhibits a compelling performance as it always leads to win-win-win outcomes (and thus ubiquitously implementable) regardless of the supplier\u27s working capital profile

Highway Performance and Time-Sensitive Industries, 1998

This report addresses delays to freight shippers. Although the focus is on just-in-time (JIT) businesses, the authors also note that non JIT businesses also suffer delays that impact their productivity. The table of contents lists the following headings: chapter 1 - introduction - a trial application: the Des Moines metropolitan area; structure of the report; chapter 2 - reliability at the forefront of freight transport demand - manufacturing and inventory; just-in-time operations in the U.S.; transportation consequences; summary; chapter 3 - JIT operations in Iowa - survey and sample; trucking activity and service; just-in-time truck transportation in Iowa; assessment of factors affecting truck transportation service; summary and conclusions; chapter 4 - travel time uncertainty induced by incidents - a probabilistic model for incident occurrences and durations; calculation of delay; trial application; conclusions; and chapter 5 - conclusions and recommendations - conclusions; recommendations