Cost allocation of capacity investment games

Consider a manufacturer serving a set of retail stores each of which faces deterministic demands in a finite planning horizon. At the beginning of the planning horizon, the production capacity of the manufacturer is built, followed by production, outsourcing to third party manufacturers if necessary and distribution to the retail stores. Because the retail stores are usually managed by different managers who act as independent profit centers, it is desirable that the total cost is divided among the retail stores so that their incentives can be appropriately captured and thus efficient operations can be achieved. Under various conditions, we prove that there is a fair allocation of costs among the retail stores in the sense that no subset of retail stores subsidizes others, or equivalently, the resulting capacity investment game has a nonempty core, that is, the capacity investment game is a balanced game. In addition, our proof provides a mechanism to compute a fair cost allocation. © 2013 Wiley Periodicals, Inc. Naval Research Logistics 60: 512–523, 2013     

指挥员作战决策行为的EWA学习模型分析

摘要：为在根本上提高作战决策质量,从行为学角度入手,以指挥员这一“人”为中心,并结合认知科学、心理学等知识对指挥员作战决策行为进行研究。从博弈角度出发,通过引入EWA模型,对指挥员作战决策行为进行分析和优化。结果表明,EWA模型,特别是加入“老练”指挥员后的模型能够明显改善指挥员决策行为,对指挥员,尤其是经验匮乏的指挥员决策行为表现有着重要的作用。     

Pricing bridges to cross a river

We consider a pricing problem in directed, uncapacitated networks. Tariffs must be defined by an operator, the leader, for a subset of m arcs, the tariff arcs. Costs of all other arcs in the network are assumed to be given. There are n clients, the followers, and after the tariffs have been determined, the clients route their demands independent of each other on paths with minimal total cost. The problem is to find tariffs that maximize the operator's revenue. Motivated by applications in telecommunication networks, we consider a restricted version of this problem, assuming that each client utilizes at most one of the operator's tariff arcs. The problem is equivalent to pricing bridges that clients can use in order to cross a river. We prove that this problem is APX‐hard. Moreover, we analyze the effect of uniform pricing, proving that it yields both an m approximation and a (1 + lnD)‐approximation. Here, D is upper bounded by the total demand of all clients. In addition, we consider the problem under the additional restriction that the operator must not reject any of the clients. We prove that this problem does not admit approximation algorithms with any reasonable performance guarantee, unless P = NP, and we prove the existence of an n‐approximation algorithm. © 2007 Wiley Periodicals, Inc. Naval Research Logistics, 2007