This article investigates the impact of timing on sellers' information acquisition strategies in a duopoly setting. Market uncertainty is captured by a representative consumer who has a private taste for the product's horizontal attribute, and both sellers can acquire this information either before (ex-ante acquisition) or after (ex-post acquisition) observing their own product qualities. We identify several conflicting effects of information acquisition that vary significantly in its timing and market characteristics. In the monopoly scenario, information acquisition is unambiguously beneficial and ex-ante acquisition is the dominant option, because it helps a seller not only design the proper product but also craft better pricing strategy. By contrast, when there is competition, information acquisition eliminates the buffer role of market uncertainty and leads to the fiercest production or pricing competition, which makes the subsequent effects of acquisition detrimental, and a seller's payoff is nonmonotonic in terms of its acquisition cost. Moreover, compared with the ex-ante information acquisition, ex-post information acquisition normally generates higher sellers' equilibrium payoffs by postponing the timing of acquisition and maintaining product differentiation. Nonetheless, ex-post information acquisition also provides the seller with greater acquisition incentive and occasionally makes him worse off than that in the ex-ante scenario. Thus, in a competitive environment, having the option of information acquisition and flexibility in its timing can be both detrimental and irresistible. © 2016 Wiley Periodicals, Inc. Naval Research Logistics, 2016

We consider an expansion planning problem for Waste-to-Energy (WtE) systems facing uncertainty in future waste supplies. The WtE expansion plans are regarded as strategic, long term decisions, while the waste distribution and treatment are medium to short term operational decisions which can adapt to the actual waste collected. We propose a prediction set uncertainty model which integrates a set of waste generation forecasts and is constructed based on user-specified levels of forecasting errors. Next, we use the prediction sets for WtE expansion scenario analysis. More specifically, for a given WtE expansion plan, the guaranteed net present value (NPV) is evaluated by computing an extreme value forecast trajectory of future waste generation from the prediction set that minimizes the maximum NPV of the WtE project. This problem is essentially a multiple stage min-max dynamic optimization problem. By exploiting the structure of the WtE problem, we show this is equivalent to a simpler min-max optimization problem, which can be further transformed into a single mixed-integer linear program. Furthermore, we extend the model to optimize the guaranteed NPV by searching over the set of all feasible expansion scenarios, and show that this can be solved by an exact cutting plane approach. We also propose a heuristic based on a constant proportion distribution rule for the WtE expansion optimization model, which reduces the problem into a moderate size mixed-integer program. Finally, our computational studies demonstrate that our proposed expansion model solutions are very stable and competitive in performance compared to scenario tree approaches. © 2016 Wiley Periodicals, Inc. Naval Research Logistics, 2016

In this article, we present a multistage model to optimize inventory control decisions under stochastic demand and continuous review. We first formulate the general problem for continuous stages and use a decomposition solution approach: since it is never optimal to let orders cross, the general problem can be broken into a set of single-unit subproblems that can be solved in a sequential fashion. These subproblems are optimal control problems for which a differential equation must be solved. This can be done easily by recursively identifying coefficients and performing a line search. The methodology is then extended to a discrete number of stages and allows us to compute the optimal solution in an efficient manner, with a competitive complexity.© 2016 Wiley Periodicals, Inc. Naval Research Logistics, 2015

In a caching game introduced by Alpern et al. (Alpern et al., Lecture notes in computer science (2010) 220–233) a Hider who can dig to a total fixed depth normalized to 1 buries a fixed number of objects among *n* discrete locations. A Searcher who can dig to a total depth of *h* searches the locations with the aim of finding all of the hidden objects. If he does so, he wins, otherwise the Hider wins. This zero-sum game is complicated to analyze even for small values of its parameters, and for the case of 2 hidden objects has been completely solved only when the game is played in up to 3 locations. For some values of *h* the solution of the game with 2 objects hidden in 4 locations is known, but the solution in the remaining cases was an open question recently highlighted by Fokkink et al. (Fokkink et al., Search theory: A game theoretic perspective (2014) 85–104). Here we solve the remaining cases of the game with 2 objects hidden in 4 locations. We also give some more general results for the game, in particular using a geometrical argument to show that when there are 2 objects hidden in *n* locations and *n*∞, the value of the game is asymptotically equal to *h*/*n* for *h*≥*n*/2. © 2015 Wiley Periodicals, Inc. Naval Research Logistics, 2015

We consider a dynamic pricing model in which the instantaneous rate of the demand arrival process is dependent on not only the current price charged by the concerned firm, but also the present state of the world. While reflecting the current economic condition, the state evolves in a Markovian fashion. This model represents the real-life situation in which the sales season is relatively long compared to the fast pace at which the outside environment changes. We establish the value of being better informed on the state of the world. When reasonable monotonicity conditions are met, we show that better present economic conditions will lead to higher prices. Our computational study is partially calibrated with real data. It demonstrates that the benefit of heeding varying economic conditions is on par with the value of embracing randomness in the demand process. © 2015 Wiley Periodicals, Inc. Naval Research Logistics, 2015

We consider two specially structured assemble-to-order (ATO) systems—the N- and W-systems—under continuous review, stochastic demand, and nonidentical component replenishment leadtimes. Using a hybrid approach that combines sample-path analysis, linear programming, and the tower property of conditional expectation, we characterize the optimal component replenishment policy and common-component allocation rule, present comparative statics of the optimal policy parameters, and show that some commonly used heuristic policies can lead to significant optimality loss. The optimality results require certain symmetry in the cost parameters. In the absence of this symmetry, we show that, for systems with high demand volume, the asymptotically optimal policy has essentially the same structure; otherwise, the optimal policies have no clear structure. For these latter systems, we develop heuristic policies and show their effectiveness. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 62: 617–645, 2015

This article is devoted to the study of an M/G/1 queue with a particular vacation discipline. The server is due to take a vacation as soon as it has served exactly *N* customers since the end of the previous vacation. *N* may be either a constant or a random variable. If the system becomes empty before the server has served *N* customers, then it stays idle until the next customer arrival. Such a vacation discipline arises, for example, in production systems and in order picking in warehouses. We determine the joint transform of the length of a visit period and the number of customers in the system at the end of that period. We also derive the generating function of the number of customers at a random instant, and the Laplace–Stieltjes transform of the delay of a customer. © 2015 Wiley Periodicals, Inc. Naval Research Logistics 62: 646–658, 2015

There are *n* customers that need to be served. Customer *i* will only wait in queue for an exponentially distributed time with rate *λ*_{i} before departing the system. The service time of customer *i* has distribution *F*_{i}, and on completion of service of customer *i* a positive reward *r*_{i} is earned. There is a single server and the problem is to choose, after each service completion, which currently in queue customer to serve next so as to maximize the expected total return. © 2015 Wiley Periodicals, Inc. Naval Research Logistics 62: 659–663, 2015

If the number of customers in a queueing system as a function of time has a proper limiting steady-state distribution, then that steady-state distribution can be estimated from system data by fitting a general stationary birth-and-death (BD) process model to the data and solving for its steady-state distribution using the familiar local-balance steady-state equation for BD processes, even if the actual process is not a BD process. We show that this indirect way to estimate the steady-state distribution can be effective for periodic queues, because the fitted birth and death rates often have special structure allowing them to be estimated efficiently by fitting parametric functions with only a few parameters, for example, 2. We focus on the multiserver *M*_{t}/*GI*/*s* queue with a nonhomogeneous Poisson arrival process having a periodic time-varying rate function. We establish properties of its steady-state distribution and fitted BD rates. We also show that the fitted BD rates can be a useful diagnostic tool to see if an *M*_{t}/*GI*/*s* model is appropriate for a complex queueing system. © 2015 Wiley Periodicals, Inc. Naval Research Logistics 62: 664–685, 2015

While accepting consumer returns has long been proposed as a solution to resolve the consumer valuation uncertainty problem, there are still a sizable portion of retailers who insist on a “no return” policy. In this article, we offer an economic rationale for these seemingly unreasonable strategies in a supply chain context. We demonstrate when and why the retailer may benefit from refusing consumer returns, even though offering consumer returns allows the supply chain to implement the expostmarket segmentation. Granting the retailer the right to refuse consumer returns may sometimes improve supply chain efficiency: it eliminates the manufacturer's attempt to induce inefficient consumer returns and bring the equilibrium back to that in the vertically integrated benchmark. We also find that the refund and the retail price can move in the opposite directions when product reliability varies, and consumer returns have a nontrivial impact on the quality choice. © 2015 Wiley Periodicals, Inc. Naval Research Logistics 62: 686–701, 2015