On the core of cost-revenue games: Minimum cost spanning tree games with revenues

In this paper, we analyze cost sharing problems arising from a general service by explicitly taking into account the generated revenues. To this cost-revenue sharing problem, we associate a cooperative game with transferable utility, called cost-revenue game. By considering cooperation among the agents using the general service, the value of a coalition is defined as the maximum net revenues that the coalition may obtain by means of cooperation. As a result, a coalition may profit from not allowing all its members to get the service that generates the revenues. We focus on the study of the core of cost-revenue games. Under the assumption that cooperation among the members of the grand coalition grants the use of the service under consideration to all its members, it is shown that a cost-revenue game has a nonempty core for any vector of revenues if, and only if, the dual game of the cost game has a large core. Using this result, we investigate minimum cost spanning tree games with revenues. We show that if every connection cost can take only two values (low or high cost), then, the corresponding minimum cost spanning tree game with revenues has a nonempty core. Furthermore, we provide an example of a minimum cost spanning tree game with revenues with an empty core where every connection cost can take only one of three values (low, medium, or high cost).     

Single-machine group scheduling with processing times dependent on position,starting time and allotted resource

This article considers scheduling problems on a single machine with learning effect, deteriorating jobs and resource allocation under group technology (GT) assumption. We assume that the actual processing time of a job depends on the job position, the group position, the starting time and the amount of resource allocated to them concurrently, and the actual setup times of groups depend on the group position and the amount of resource allocated to them concurrently. Two resource allocation functions are examined for minimizing the weighted sum of makespan and total resource cost. We prove that the problems have polynomial solutions under the condition that the number of jobs in each group are the same.     

Black-Litterman投资组合模型的进一步推导分析

在理论上通过推导首次得出了Black-Litterman模型(B-L模型)最优权重与信心水平的公式.在各资产收益不相关及单一绝对观点的假设下,得出各资产的B-L模型最优权重与信心水平的简化表达式.借助于此,还对信心水平与最优权重公式的进一步理论分析,并以光大证券的"乌龙指"做实证,详细分析投资者在没有市场观点、拥有内幕信息、以及信心水平在某范围变化时,其所持各投资品权重的特点.     

Sequential smoothing for turning point detection with application to financial decisions

A fundamental problem in financial trading is the correct and timely identification of turning points in stock value series. This detection enables to perform profitable investment decisions, such as buying‐at‐low and selling‐at‐high. This paper evaluates the ability of sequential smoothing methods to detect turning points in financial time series. The novel idea is to select smoothing and alarm coefficients on the gain performance of the trading strategy. Application to real data shows that recursive smoothers outperform two‐sided filters at the out‐of‐sample level. Copyright © 2012 John Wiley & Sons, Ltd.     

基于多种用海功能的海域使用权拍卖机制研究

海域使用权拍卖是对海域资源进行市场化配置的重要方式.基于海域资源的立体性和层次性特征,提出了多种用海功能约束下的海域使用权拍卖机制,设计了综合效用函数,对海域使用权进行经济价值与生态补偿的综合评价,促进海域资源的多层次利用与开发.仿真结果表明:相比较于单一功能的海域使用权拍卖,多属性拍卖能够改善海域使用权拍卖效率,同时拍卖结果也提高了政府对海域使用权拍卖的满意度和最终效用,为我国海域使用权拍卖方式的创新提供了理论基础.     

金融危机后中国跨境短期资本流动与人民币汇率及资产价格波动关联研究

基于综合资产收益率平价理论构建理论模型,研究探讨了中国跨境短期资本流动规模与资产价格及人民币汇率预期变动之间的动态关系.然后在此基础上通过建立VAR模型,采用格兰杰因果检验以及脉冲响应分析等方法实证分析了2010年7月至2015年6月中国跨境短期资本流动、人民币汇率预期波动、利率、房价和股价变动之间的关联关系.实证结果表明:中国房地产市场、股票市场上涨会吸引短期跨境资本流入;美元利率上升和人民币贬值预期会引致短期跨境资本的流出;短期跨境资本流入会造成国内利率降低,但对房地产市场、股票市场的影响不显著;中国房地产市场与股票市场之间会有联动效应,人民币的贬值预期也会引致房地产价格下降.     

Analytical models for supplier selection and order quantity allocation

In the literature, decision models and techniques for supplier selection do not often consider inventory management of the items being purchased as part of the analysis. In this article, two mixed integer nonlinear programming models are proposed to select the best set of suppliers and determine the proper allocation of order quantities while minimizing the annual ordering, inventory holding, and purchasing costs under suppliers’ capacity and quality constraints. The first model allows independent order quantities for each supplier while the second model restricts all order quantities to be of equal size, as it would be required in a multi-stage (supply chain) inventory model. Illustrative examples are used to highlight the advantages of the proposed models over a previous model introduced in the literature.     

Trade-offs between target hardening and overarching protection

Defenders concerned about protecting multiple targets can either protect them individually (through target hardening), or collectively (through overarching protections such as border security, public health, emergency response, or intelligence). Decision makers may find it relatively straightforward to choose which targets to harden, but are likely to find it more difficult to compare seemingly incommensurate forms of protection - e.g., target hardening, versus a reduction in the likelihood of weapons being smuggled across the border. Unfortunately, little previous research has addressed this question, and fundamental research is needed to provide guidance and practical solution approaches. In this paper, we first develop a model to optimally allocate resources between target hardening and overarching protection, then investigate the factors affecting the relative desirability of target hardening versus overarching protection, and finally apply our model to a case study involving critical assets in Wisconsin. The case study demonstrates the value of our method by showing that the optimal solution obtained using our model is in some cases substantially better than the historical budget allocation.     

How Fast Is the Bandit?

Abstract

In this article we investigate the rate of convergence of the so-called two-armed bandit algorithm. The behavior of the algorithm turns out to be highly non standard: no central limit theorem, possible occurrence of two different rates of convergence with positive probability.     

Multi-objectives Tabu Search based algorithm for progressive resource allocation

Military course of action planning involves time and space synchronization as well as resource and asset allocation. A mission could be seen as a defined set of logical ordered tasks with time and space constraints. The resources to task rules require that available assets should be allocated to each task. A combination of assets might be required to execute a given task. The couple (task, resources) constitutes an action. This problem is formulated as a multi-objectives scheduling and resource allocation problem. Any solution is assessed based on a number of conflicting and heterogeneous objectives. In fact, military planning officers should keep perfecting the plan based on the Commander’s criteria for success. The scheduling problem and resource allocation problem are considered as NP-Hard Problems [A. Guitouni, B. Urli, J.-M. Martel, Course of action planning: A project based modelling, Working Paper, Faculté des sciences de l’ administration, Université Laval, Québec, 2005]. This paper is concerned with the multi-objectives resource allocation problem. Our objective is to find adequate resource allocation for given courses of action schedule. To optimize this problem, this paper investigates non-exact solution methods, like meta-heuristic methods for finding potential efficient solutions. A progressive resource allocation methodology is proposed based on Tabu Search and multi-objectives concepts. This technique explores the search space so as to find a set of potential efficient solutions without aggregating the objectives into a single objective function. It is guided by the principle of maximizing the usage of any resource before considering a replacement resource. Thus, a given resource is allocated to the maximum number of tasks for a given courses of action schedule. A good allocation is a potential efficient solution. These solutions are retained by applying a combination of a dominance rule and a multi-criteria filtering method. The performance of the proposed Pareto-based approach is compared to two aggregation approaches: weighted-sum and the lexicographic techniques. The result shows that a Pareto-based approach is providing better solutions and allowing more flexibility to the decision-maker.