Dynamic Scheduling of a Production/Inventory System with By-Products and Random Yield

Motivated by semiconductor wafer fabrication, we consider a scheduling problem for a single-server multiclass queue. A single workstation fabricates semiconductor wafers according to a variety of different processes, where each process consists of multiple stages of service with a different general service time distribution at each stage. A batch (or lot) of wafers produced according to a particular process randomly yields chips of many different product types, and completed chips of each type enter a finished goods inventory that services exogenous customer demand for that type. The scheduling problem is to dynamically decide whether the server should be idle or working, and in the latter case, to decide which stage of which process type to serve next. The objective is to minimize the long run expected average cost, which includes costs for holding work-in-process inventory(which may differ by process type and service stage) and backordering and holding finished goods inventory (which may differ by product type). We assume the workstation must be busy the great majority of the time in order to satisfy customer demand, and approximate the scheduling problem by a control problem involving Brownian motion. A scheduling policy is derived by interpreting the exact solution to the Brownian control problem in terms of the production/inventory system. The proposed dynamic scheduling policy takes a relatively simple form and appears to be effective in numerical studies

Supplier and retailer coordination under stochastic price-dependent demand and fast moving items

We consider a centralized supply chain system consisting of a one supplier and one retailer. The customers’ demand is a compound Poisson process with price-dependent intensity and continuous batch size distribution. The intensity of the customers’ arrivals is assumed to be sufficiently high to use a diffusion approximation of the demand process. We assume that the supplier has complete information about the rational retailer’s behavior in the framework of the newsvendor problem. The objective is to find a joint pricing and ordering policy so as to maximize the retailer’s expected profit and supplier’s profit. The equations for the optimal prices main parts are obtained and the example of the price-intensity dependence is considered

Coordinated replenishment systems with discount opportunities

Inventory Control;Inventory Models;inkoop

Components of the Czech Koruna Risk Premium in a Multiple-Dealer FX Market

The paper proposes a continuous time model of an FX market organized as a multiple dealership. The model reflects a number of salient features of the Czech koruna spot market. The dealers have costly access to the best available quotes. They interpret signals from the joint dealer-customer order flow and decide upon their own quotes and trades in the inter-dealer market. Each dealer uses the observed order flow to improve the subjective estimates of the relevant aggregate variables, which are the sources of uncertainty. One of the risk factors is the size of the cross-border dealer transactions in the FX market. These uncertainties have diffusion form and are dealt with according to the principles of portfolio optimization in continuous time. The model is used to explain the country, or risk, premium in the uncovered national return parity equation for the koruna/euro exchange rate. The two country premium terms that I identify in excess of the usual covariance term (a consequence of the 'Jensen inequality effect') are the dealer heterogeneity-induced inter-dealer market order flow component and the dealer Bayesian learning component. As a result, a 'dealer-based total return parity' formula links the exchange rate to both the 'fundamental' factors represented by the differential of the national asset returns, and the microstructural factors represented by heterogeneous dealer knowledge of the aggregate order flow and the fundamentals. Evidence on the cross-border order flow dependence of the Czech koruna risk premium, in accordance with the model prediction, is documented.Bayesian learning, FX microstructure, optimizing dealer, uncovered parity.

The role of the abandonment option in strategic capital allocation: a review of selected literature

We review the most relevant contributions to the abandonment option since the late 1960s. We begin by approaching the contributions to the literature before the emergence of the real options approach to capital investment decisions, and thereafter, under a consistent real options approach, highlighting the interactions between the option to abandon and other types of options. We then identify the methodologies adopted, and the business sectors/ types of investment projects where the abandonment option is more frequently studied. We also debate the strategic role of the abandonment solution in corporate divestitures and under a game-theoretical approach. Finally, we present some concluding remarks and identify how certain gaps found in the literature may constitute opportunities for future research

Revenue Management for Make-to-Order and Make-to-Stock Systems

With the success of Revenue Management (RM) techniques over the past three decades in various segments of the service industry, many manufacturing firms have started exploring innovative RM technologies to improve their profits. This dissertation studies RM for make-to-order (MTO) and make-to-stock (MTS) systems. We start with a problem faced by a MTO firm that has the ability to reject or accept the order and set prices and lead-times to influence demands. The firm is confronted with the problem to decide, which orders to accept or reject and trade-off the price, lead-time and potential for increased demand against capacity constraints, in order to maximize the total profits in a finite planning horizon with deterministic demands. We develop a mathematical model for this problem. Through numerical analysis, we present insights regarding the benefits of price customization and lead-time flexibilities in various demand scenarios. However, the demands of MTO firms are always hard to be predicted in most situations. We further study the above problem under the stochastic demands, with the objective to maximize the long-run average profit. We model the problem as a Semi-Markov Decision Problem (SMDP) and develop a reinforcement learning (RL) algorithm-Q-learning algorithm (QLA), in which a decision agent is assigned to the machine and improves the accuracy of its action-selection decisions via a “learning process. Numerical experiment shows the superior performance of the QLA. Finally, we consider a problem in a MTS production system consists of a single machine in which the demands and the processing times for N types of products are random. The problem is to decide when, what, and how much to produce so that the long-run average profit. We develop a mathematical model and propose two RL algorithms for real-time decision-making. Specifically, one is a Q-learning algorithm for Semi-Markov decision process (QLS) and another is a Q-learning algorithm with a learning-improvement heuristic (QLIH) to further improve the performance of QLS. We compare the performance of QLS and QLIH with a benchmarking Brownian policy and the first-come-first-serve policy. The numerical results show that QLIH outperforms QLS and both benchmarking policies