The geometric process is considered when the distribution of the first interarrival time is assumed to be Weibull. Its one-dimensional probability distribution is derived as a power series expansion of the convolution of the Weibull distributions. Further, the mean value function is expanded into a power series using an integral equation. © 2014 Wiley Periodicals, Inc. Naval Research Logistics, 2014

Consider a patrol problem, where a patroller traverses a graph through edges to detect potential attacks at nodes. An attack takes a random amount of time to complete. The patroller takes one time unit to move to and inspect an adjacent node, and will detect an ongoing attack with some probability. If an attack completes before it is detected, a cost is incurred. The attack time distribution, the cost due to a successful attack, and the detection probability all depend on the attack node. The patroller seeks a patrol policy that minimizes the expected cost incurred when, and if, an attack eventually happens. We consider two cases. A random attacker chooses where to attack according to predetermined probabilities, while a strategic attacker chooses where to attack to incur the maximal expected cost. In each case, computing the optimal solution, although possible, quickly becomes intractable for problems of practical sizes. Our main contribution is to develop efficient index policies—based on Lagrangian relaxation methodology, and also on approximate dynamic programming—which typically achieve within 1% of optimality with computation time orders of magnitude less than what is required to compute the optimal policy for problems of practical sizes. © 2014 Wiley Periodicals, Inc. Naval Research Logistics, 2014

We investigate and compare the impact of the tax reduction policies implemented in the United States and China to stimulate consumer purchase of new automobiles and improve manufacturers' profits. The U.S. policy provides each qualifying consumer with a federal income tax deduction on state and local sales and excise taxes paid on the purchase price (up to a cutoff level), whereas the Chinese policy reduces the vehicle sales tax rate for consumers. We observe that these policy designs are consistent with the tax management system and the economic environment in the respective country. We analytically determine the effects of the two tax reduction policies on the automobile sales and the manufacturer's and the retailer's profits. Numerical examples are then used to provide insights on the importance of certain factors that influence the effects of the two policies. Finally, a numerical experiment with sensitivity analysis based on real data is conducted to compare the merits and characteristics of the two policies under comparable conditions. We find that the U.S. policy is better than the Chinese policy in stimulating the sales of high-end automobiles, whereas the Chinese policy is better than the U.S. policy in improving the sales of low-end automobiles. The U.S. policy is slightly more effective in increasing the profitability of the automobile supply chain; but, in general, the Chinese policy is more cost effective. The methodology developed herein can be used to evaluate other tax reduction policies such as those related to the purchase of energy-saving vehicles and to serve as a decision model to guide the choice of alternative tax reduction policies. © 2014 Wiley Periodicals, Inc. Naval Research Logistics, 2014

Earlier research on the effects of nonoverlapping temporal aggregation on demand forecasting showed the benefits associated with such an approach under a stationary AR(1) or MA(1) processes for decision making conducted at the disaggregate level. The first objective of this note is to extend those important results by considering a more general underlying demand process. The second objective is to assess the conditions under which aggregation may be a preferable approach for improving decision making at the aggregate level as well. We confirm the validity of previous results under more general conditions, and we show the increased benefit resulting from forecasting by temporal aggregation at lower frequency time units. © 2014 Wiley Periodicals, Inc. Naval Research Logistics 61: 489–500, 2014

We study a supply chain in which an original equipment manufacturer (OEM) and a contract manufacturer (CM) compete in the finished goods market. The OEM can decide whether to outsource the intermediate good, a critical component for producing the finished good, from the CM or make in-house production. Technology transition improves the CM's production efficiency, and it can take two different forms: a direct technology transfer from the OEM to the CM or technology spillovers through outsourcing from the OEM to the CM. We document the possibility of strategic outsourcing, that is, the CM supplies the intermediate good to the OEM when she is less efficient than the OEM's in-house production. We find that technology spillovers can strengthen the incentive for strategic outsourcing. Furthermore, compared with direct technology transfers, outsourcing coupled with technology spillovers may generate more technology transition. Outsourcing is a particularly appropriate channel for implicit collusion when the OEM is not very efficient with the production of the intermediate good. Our results suggest that ex post competition on the finished goods can create room for ex ante collaboration and provide some implications on the OEM's outsourcing strategies when facing a competitive CM.© 2014 Wiley Periodicals, Inc. Naval Research Logistics 61: 501–514, 2014

We consider the problem of assessing the value of demand sharing in a multistage supply chain in which the retailer observes stationary autoregressive moving average demand with Gaussian white noise (shocks). Similar to previous research, we assume each supply chain player constructs its best linear forecast of the leadtime demand and uses it to determine the order quantity via a periodic review myopic order-up-to policy. We demonstrate how a typical supply chain player can determine the extent of its available information in the presence of demand sharing by studying the properties of the moving average polynomials of adjacent supply chain players. The retailer's demand is driven by the random shocks appearing in the autoregressive moving average representation for its demand. Under the assumptions we will make in this article, to the retailer, knowing the shock information is equivalent to knowing the demand process (assuming that the model parameters are also known). Thus (in the event of sharing) the retailer's demand sequence and shock sequence would contain the same information to the retailer's supplier. We will show that, once we consider the dynamics of demand propagation further up the chain, it may be that a player's demand and shock sequences will contain different levels of information for an upstream player. Hence, we study how a player can determine its available information under demand sharing, and use this information to forecast leadtime demand. We characterize the value of demand sharing for a typical supply chain player. Furthermore, we show conditions under which (i) it is equivalent to no sharing, (ii) it is equivalent to full information shock sharing, and (iii) it is intermediate in value to the two previously described arrangements. Although it follows from existing literature that demand sharing is equivalent to full information shock sharing between a retailer and supplier, we demonstrate and characterize when this result does not generalize to upstream supply chain players. We then show that demand propagates through a supply chain where any player may share nothing, its demand, or its full information shocks (FIS) with an adjacent upstream player as quasi-ARMA in—quasi-ARMA out. We also provide a convenient form for the propagation of demand in a supply chain that will lend itself to future research applications. © 2014 Wiley Periodicals, Inc. Naval Research Logistics 61: 515–531, 2014

]]>We consider two regression models: linear and logistic. The dependent variable is observed periodically and in each period a Bayesian formulation is used to generate updated forecasts of the dependent variable as new data is observed. One would expect that including new data in the Bayesian updates results in improved forecasts over not including the new data. Our findings indicate that this is not always true. We show there exists a subset of the independent variable space that we call the “region of no learning.” If the independent variable values for a given period in the future are in this region, then the forecast does not change with any new data. Moreover, if the independent variable values are in a neighborhood of the region of no learning, then there may be little benefit to wait for the new data and update the forecast. We propose a statistical approach to characterize this neighborhood which we call the “region of little learning.” Our results provide insights into the trade-offs that exist in situations when the decision maker has an incentive to make an early decision based on an early forecast versus waiting to make a later decision based on an updated forecast. © 2014 Wiley Periodicals, Inc. Naval Research Logistics 61: 532–548, 2014

Most of the research, on the study of the reliability properties of technical systems, assume that the components of the system operate independently. However, in real life situation, it is more reasonable to assume that there is dependency among the components of the system. In this article, we give sufficient conditions based on the signature and the joint distribution of component lifetimes to obtain stochastic ordering results for coherent and mixed systems with exchangeable components. Some stochastic orders on dynamic (or conditional) signature of coherent systems are also provided. © 2014 Wiley Periodicals, Inc. Naval Research Logistics 61: 549–556, 2014