A global economy and increase in customer expectations in terms of cost and services have put a premium on effective supply chain reengineering. It is essential to perform risk-benefit analysis of reengineering alternatives before making a final decision. Simulation provides an effective pragmatic approach to detailed analysis and evaluation of supply chain design and management alternatives. However, the utility of this methodology is hampered by the time and effort required to develop models with sufficient fidelity to the actual supply chain of interest. In this paper, we describe a supply chain modeling framework designed to overcome this difficulty. Using our approach, supply chain models are composed from software components that represent types of supply chain agents (like retailers, manufacturers, transporters), their constituent control elements (like inventory policy), and their interaction protocols (like message types). The underlying library of supply chain modeling components has been derived from analysis of several different supply chains. It provides a reusable base of domain-specific primitives that enables rapid development of customized decision support tools.

Analysis of the job shop scheduling domain has indicated that the crux of the scheduling problem is the determination and satisfaction of a large variety of constraints. Schedules are influenced by such diverse and conflicting factors as due date requirements, cost restrictions, produdion levels. machine capab&bes and substitutability, alternative production processes, order characteristics, resource requirements, and resource availability. This paper describes ISIS. a scheduling system capable of incorporating all relevant constraints in the cowtrwbon ' ofjob shop schedules. We examine both the representation of constraints within ISIS, and the manner in which these constraints are used in conducting a constraintdirected search for an acceptable schedule. The important issues relating to the relaxation of constraints are addressed. Finally, the interawe scheduling faalities provided by lSlS are considered.

W e investigate the management of a merchant wind energy farm co-located with a grid-level storage facility and connected to a market through a transmission line. We formulate this problem as a Markov decision process (MDP) with stochastic wind speed and electricity prices. Consistent with most deregulated electricity markets, our model allows these prices to be negative. As this feature makes it difficult to characterize any optimal policy of our MDP, we show the optimality of a stage-and partial-state-dependent-threshold policy when prices can only be positive. We extend this structure when prices can also be negative to develop heuristic one (H1) that approximately solves a stochastic dynamic program. We then simplify H1 to obtain heuristic two (H2) that relies on a price-dependent-threshold policy and derivative-free deterministic optimization embedded within a Monte Carlo simulation of the random processes of our MDP. We conduct an extensive and data-calibrated numerical study to assess the performance of these heuristics and variants of known ones against the optimal policy, as well as to quantify the effect of negative prices on the value added by and environmental benefit of storage. We find that (i) H1 computes an optimal policy and on average is about 17 times faster to execute than directly obtaining an optimal policy; (ii) H2 has a near optimal policy (with a 2.86% average optimality gap), exhibits a two orders of magnitude average speed advantage over H1, and outperforms the remaining considered heuristics; (iii) storage brings in more value but its environmental benefit falls as negative electricity prices occur more frequently in our model.Note: All experiments are run on a computer with Intel(R) Core(TM) i7-3770K 3.40 GHz CPU and 8 GB RAM.Zhou, Scheller-Wolf, Secomandi, and Smith: Managing Wind-Based Electricity Generation Production and Operations Management 28(4), pp. 970-989,

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In the health care domain, diagnostic service centers provide advice to patients over the phone about what the most appropriate course of action is based on their symptoms. Managers of such centers must strike a balance between accuracy of advice, callers' waiting time and staffing costs by setting the appropriate capacity (staffing) and service depth. We model this problem as a multiple-server queueing system with the servers performing a sequential testing process, and the customers deciding whether to use the service or not, based on their expectation of accuracy and congestion. We find the dual concerns of accuracy and congestion lead to a counterintuitive impact of capacity: Increasing capacity might increase congestion. In addition, (i) Patient population size is an important driver in management decisions, not only in staffing, but also in accuracy of advice; (ii) Increasing asymmetry in error costs may not increase asymmetry in the corresponding error rates; and (iii) The error costs for the two major stake-holders -the service manager and the patient -may impact the optimal staffing level in different ways. Finally, we highlight the relevance of our model and results to challenges in practice elicited during interviews with current clinical researchers and practitioners.

This paper investigates the utility of introducing randomization as a means of boosting the performance of search heuristics. We introduce a particular approach to randomization, called Value-biased stochastic sampling (VBSS), which emphasizes the use of heuristic value in determining stochastic bias. We offer an empirical study of the performance of value-biased and rank-biased approaches to randomizing search heuristics. We also consider the use of these stochastic sampling techniques in conjunction with local hill-climbing. Finally, we contrast the performance of stochastic sampling search with more systematic search procedures as a means of amplifying the performance of search heuristics.

Electricity cannot yet be stored on a large scale, but technological advances leading to cheaper and more efficient industrial batteries make grid-level storage of electricity surpluses a natural choice. Because electricity prices can be negative, it is unclear how the presence of negative prices might affect the storage policy structure known to be optimal when prices are only nonnegative, or even how important it is to consider negative prices when managing an industrial battery. For fast storage (a storage facility that can both be fully emptied and filled up in one decision period), we show analytically that negative prices can substantially alter the optimal storage policy structure, e.g., all else being equal, it can be optimal to empty an almost empty storage facility and fill up an almost full one. For more typical slow grid-level electricity storage, we numerically establish that ignoring negative prices could result in a considerable loss of value when negative prices occur more than 5% of the time. Negative prices raise another possibility: rather than storing surpluses, a merchant might buy negatively priced electricity surpluses and dispose of them, e.g., using load banks. We find that the value of such a disposal strategy is substantial, e.g., about $118 per kilowatt-year when negative prices occur 10% of the time, but smaller than that of the storage strategy, e.g., about $391 per kilowatt-year using a typical battery. However, devices for disposal are much cheaper than those for storage. Our results thus have ramifications for merchants as well as policy makers. This paper was accepted by Serguei Netessine, operations management.