In this article, we consider a loss-averse newsvendor with stochastic demand. The newsvendor might procure options when demand is unknown, and decide how many options to execute only after demand is revealed. If the newsvendor reserves too many options, he would incur high reservation costs. Yet reserving too few could result in lost sales. So the newsvendor faces a trade-off between reservation costs and losing sales. When there are multiple options available, the newsvendor has to consider how many units of each to reserve by studying the trade-off between flexibility and costs. We show how the newsvendor's loss aversion behavior affects his ordering decision, and propose an efficient algorithm to compute his optimal solution in the general case with *n* options. We also present examples showing how the newsvendor's ordering strategy changes as loss aversion rises. © 2014 Wiley Periodicals, Inc. Naval Research Logistics, 2014

The two-level problem studied in this article consists of optimizing the refueling costs of a fleet of locomotives over a railway network. The goal consists of determining: (1) the number of refueling trucks contracted for each yard (truck assignment problem denoted TAP) and (2) the refueling plan of each locomotive (fuel distribution problem denoted FDP). As the FDP can be solved efficiently with existing methods, the focus is put on the TAP only. In a first version of the problem (denoted (P1)), various linear costs (e.g., fuel, fixed cost associated with each refueling, weekly operating costs of trucks) have to be minimized while satisfying a set of constraints (e.g., limited capacities of the locomotives and the trucks). In contrast with the existing literature on this problem, two types of nonlinear cost components will also be considered, based on the following ideas: (1) if several trucks from the same fuel supplier are contracted for the same yard, the supplier is likely to propose discounted prices for that yard (Problem (P2)); (2) if a train stops too often on its route, a penalty is incurred, which represents the dissatisfaction of the clients (Problem (P3)). Even if exact methods based on a mixed integer linear program formulation are available for (P1), they are not appropriate anymore to tackle (P2) and (P3). Various methods are proposed for the TAP: a descent local search, a tabu search, and a learning tabu search (LTS). The latter is a new type of local search algorithm. It involves a learning process relying on a trail system, and it can be applied to any combinatorial optimization problem. Results are reported and discussed for a large set of instances (for (P1), (P2), and (P3)), and show the good performance of LTS. © 2014 Wiley Periodicals, Inc. Naval Research Logistics, 2014

There are *n* boxes with box *i* having a quota value Balls arrive sequentially, with each ball having a binary vector attached to it, with the interpretation being that if *X _{i}* = 1 then that ball is eligible to be put in box

This article provides an efficient heuristic based on decomposition for the twin robots scheduling problem (TRSP). TRSP concerns two moving robots executing storage and retrieval requests in parallel along a shared pathway. The depots are located at both ends of the line and a dedicated robot is assigned to each of them. While moving goods between their respective depots and some storage locations on the line, noncrossing constraints among robots need to be considered. Our heuristic uses a dynamic programming framework to determine the schedule of one robot while keeping the other one's fixed. It finds near-optimal solutions even for large problem instances with hundreds of jobs in a short time span. © 2014 Wiley Periodicals, Inc. Naval Research Logistics, 2014

A firm making quantity decision under uncertainty loses profit if its private information is leaked to competitors. Outsourcing increases this risk as a third party supplier may leak information for its own benefit. The firm may choose to conceal information from the competitors by entering in a confidentiality agreement with the supplier. This, however, diminishes the firm's ability to dampen competition by signaling a higher quantity commitment. We examine this trade-off in a stylized supply chain in which two firms, endowed with private demand information, order sequentially from a common supplier, and engage in differentiated quantity competition. In our model, the supplier can set different wholesale prices for firms, and the second-mover firm could be better informed. Contrary to what is expected, information concealment is not always beneficial to the first mover. We characterize conditions under which the first mover firm will not prefer concealing information. We show that this depends on the relative informativeness of the second mover and is moderated by competition intensity. We examine the supplier's incentive in participating in information concealment, and develop a contract that enables it for wider set of parameter values. We extend our analysis to examine firms' incentive to improve information. © 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, 61: 557–576, 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, 61: 577–598, 2014

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, 61: 599–603, 2014

In this article, we consider an online retailer who sells two similar products (A and B) over a finite selling period. Any stock left at the end of the period has no value (like clothes going out of fashion at the end of a season). Aside from selling the products at regular prices, he may offer an additional option that sells a probabilistic good, “A or B,” at a discounted price. Whenever a customer buys a probabilistic good, he needs to assign one of the products for the fulfillment. Considering the choice behavior of potential customers, we model the problem using continuous-time, discrete-state, finite-horizon dynamic programming. We study the optimal admission decisions and devise two scenarios, whose value functions can be used as benchmarks to evaluate the demand induction effect and demand dilution effect of probabilistic selling (PS). We further investigate an extension of the base MDP (Markov Decision Process) model in which the fulfillment of probabilistic sales is uncontrollable by the retailer. A special case of the extended model can be used as a benchmark to quantify the potential inventory pooling effect of PS. Finally, numerical experiments are conducted to evaluate the overall profit improvement, and the effects from adopting the PS strategy. © 2014 Wiley Periodicals, Inc. Naval Research Logistics, 61: 604–620, 2014

In this study, we consider a bicriteria multiresource generalized assignment problem. Our criteria are the total assignment load and maximum assignment load over all agents. We aim to generate all nondominated objective vectors and the corresponding efficient solutions. We propose several lower and upper bounds and use them in our optimization and heuristic algorithms. The computational results have shown the satisfactory behaviors of our approaches. © 2014 Wiley Periodicals, Inc. Naval Research Logistics, 61: 621–636, 2014

This article deals with supply chain systems in which lateral transshipments are allowed. For a system with two retailers facing stochastic demand, we relax the assumption of negligible fixed transshipment costs, thus, extending existing results for the single-item case and introducing a new model with multiple items. The goal is to determine optimal transshipment and replenishment policies, such that the total centralized expected profit of both retailers is maximized. For the single-item problem with fixed transshipment costs, we develop optimality conditions, analyze the expected profit function, and identify the optimal solution. We extend our analysis to multiple items with joint fixed transshipment costs, a problem that has not been investigated previously in the literature, and show how the optimality conditions may be extended for any number of items. Due to the complexity involved in solving these conditions, we suggest a simple heuristic based on the single-item results. Finally, we conduct a numerical study that provides managerial insights on the solutions obtained in various settings and demonstrates that the suggested heuristic performs very well. © 2014 Wiley Periodicals, Inc. Naval Research Logistics, 61: 637–664, 2014