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251.
252.
舰艇出海执行任务期间,需要携行一定种类和数量的物资自给。文中研究了这种单级供应条件下,如何确定备件最优携行量的问题。建立了以服务水平为目标,以资源为约束的目标规划模型,模型考虑了随机提前期和备件重要度。提出一种基于边际效费比的增量法用于求解这个模型。最后,给出一个算例并得到了一些结论。 相似文献
253.
Dieter Bos 《Defence and Peace Economics》2013,24(4):279-296
Procurement of advanced technology defence equipment requires appropriate contractual arrangements to achieve efficient R&D investments. This paper analyses the optimality of target‐cost and fixed price contracts and shows that target‐cost pricing can achieve a first best where both fixed‐price contracts and cost reimbursement fail to do so. The main message of this paper is that in incomplete contracting optimality may sometimes be achieved by arrangements which combine several formulae which, individually, would fail to achieve efficiency. 相似文献
254.
255.
Thomas-Durell Young 《战略研究杂志》2013,36(7):1031-1057
By any objective measure, defense institutions in Central and Eastern Europe have all but universally been incapable of producing viable defense plans that are based on objective costing and operational planning data. This situation exists in spite the provision of considerable Western advice and assistance, let alone reporting to and receiving assessments by NATO’s International Staff under Partnership for Peace, as well as via the integrated defense planning and reporting systems. An explanation for this systematic failure across European post-Communist defense institutions can be found in the continued slow development of an over-arching policy framework which directs and approves all activities of the armed forces, as well as the de-centralization of financial decision-making down to capability providers. The essay ends with an examination of the adverse effects of the early introduction of planning programming, budgeting system (PPBS), have had on the development of effective policy and planning capabilities within these defense institutions. 相似文献
256.
In this article, we seek to understand how a capacity‐constrained seller optimally prices and schedules product shipping to customers who are heterogeneous on willingness to pay (WTP) and willingness to wait (WTW). The capacity‐constrained seller does not observe each customer's WTP and WTW and knows only the aggregate distributions of WTP and WTW. The seller's problem is modeled as an M/M/Ns queueing model with multiclass customers and multidimensional information screening. We contribute to the literature by providing an optimal and efficient algorithm. Furthermore, we numerically find that customers with a larger waiting cost enjoys a higher scheduling priority, but customers with higher valuation do not necessarily get a higher scheduling priority. © 2015 Wiley Periodicals, Inc. Naval Research Logistics 62: 215–227, 2015 相似文献
257.
Dimitrios G. Pandelis 《海军后勤学研究》2014,61(6):435-446
We consider two‐stage tandem queueing systems with dedicated servers in each station and a flexible server that is trained to serve both stations. We assume no arrivals, exponential service times, and linear holding costs for jobs present in the system. We study the optimal dynamic assignment of servers to jobs assuming a noncollaborative work discipline with idling and preemptions allowed. For larger holding costs in the first station, we show that (i) nonidling policies are optimal and (ii) if the flexible server is not faster than the dedicated servers, the optimal server allocation strategy has a threshold‐type structure. For all other cases, we provide numerical results that support the optimality of threshold‐type policies. Our numerical experiments also indicate that when the flexible server is faster than the dedicated server of the second station, the optimal policy may have counterintuitive properties, which is not the case when a collaborative service discipline is assumed. © 2014 Wiley Periodicals, Inc. Naval Research Logistics 61: 435–446, 2014 相似文献
258.
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 相似文献
259.
In this study, we illustrate a real‐time approximate dynamic programming (RTADP) method for solving multistage capacity decision problems in a stochastic manufacturing environment, by using an exemplary three‐stage manufacturing system with recycle. The system is a moderate size queuing network, which experiences stochastic variations in demand and product yield. The dynamic capacity decision problem is formulated as a Markov decision process (MDP). The proposed RTADP method starts with a set of heuristics and learns a superior quality solution by interacting with the stochastic system via simulation. The curse‐of‐dimensionality associated with DP methods is alleviated by the adoption of several notions including “evolving set of relevant states,” for which the value function table is built and updated, “adaptive action set” for keeping track of attractive action candidates, and “nonparametric k nearest neighbor averager” for value function approximation. The performance of the learned solution is evaluated against (1) an “ideal” solution derived using a mixed integer programming (MIP) formulation, which assumes full knowledge of future realized values of the stochastic variables (2) a myopic heuristic solution, and (3) a sample path based rolling horizon MIP solution. The policy learned through the RTADP method turned out to be superior to polices of 2 and 3. © 2010 Wiley Periodicals, Inc. Naval Research Logistics 2010 相似文献
260.
Michael N. Katehakis;Ehsan Teymourian;Jian Yang; 《海军后勤学研究》2024,71(8):1220-1236
We investigate a data-driven dynamic inventory control problem involving fixed setup costs and lost sales. Random demand arrivals stem from a demand distribution that is only known to come out of a vast ambiguity set. Lost sales and demand ambiguity would together complicate the problem through censoring, namely, the inability of the firm to observe the lost portion of the demand data. Our main policy idea advocates periodically ordering up to high levels for learning purposes and, in intervening periods, cleverly exploiting the information gained in learning periods. By regret, we mean the price paid for ambiguity in long-run average performances. When demand has a finite support, we can accomplish a regret bound in the order of 𝒪(T2/3·(lnT)1/2) which almost matches a known lower bound as long as inventory costs are genuinely convex. Major policy adjustments are warranted for the more complex case involving an unbounded demand support. The resulting regret could range between 𝒪(T0.779) and 𝒪(T0.889) depending on the nature of moment-related bounds that help characterize the degree of ambiguity. These are improvable to 𝒪(T2/3·(lnT)2) when distributions are light-tailed. Our simulation demonstrates the merits of various policy ideas. 相似文献