Nash implementation with an infinite-dimensional trade space

This paper deals with the problem of implementing the Walras correspondence via Nash equilibria, in exchange economies with infinitely many commodities and finitely many households with possibly non-ordered preferences. We explicitly construct a feasible mechanism enjoying some features, which have natural economic meanings. Under a fairly weak boundary condition, this game fully implements the Walras equilibria. If this condition is not fulfilled, our mechanism nevertheless implements the constrained Walras equilibria. Received: 11 December 2003, Accepted: 29 July 2005 JEL Classification: D41, D43, D51 We thank (without implicating) Prof. Jean-Marc Bonnisseau and Cuong Le Van for helpful comments. The views expressed in this paper reflect those of the authors and not necessarily those of Calyon.     

基于遗传算法的多目标集装箱多式联运运输优化模型

基于集装箱多式联运在进行门到门的运输过程中可以选择多种运输方式和路径的组合进行优化运输这个特点，本文将多式联运的运输优化问题转化成为一个最短路径问题，以成本和时间为优化目标建立了选择最优路径的模型，并选择遗传算法作为求解算法对实例问题进行了求解验证。     

蚁群算法在物流配送路径优化问题上的应用

物流配送路径优化是现代物流配送服务的关键环节之一,需要一个快速而有效的求解算法,构建了物流配送路径优化问题的数学模型,并应用蚁群算法加以求解。在模拟试验基础上,得出了蚁群算法对物流配送路径优化问题十分有效的结论,探索出了一条高效求解物流配送路径优化问题的新途径。     

Revisiting the Miles and Snow strategic framework: uncovering interrelationships between strategic types,capabilities, environmental uncertainty,and firm performance

The Miles and Snow strategic type framework is re‐examined with respect to interrelationships with several theoretically relevant batteries of variables, including SBU strategic capabilities, environmental uncertainty, and performance. A newly developed constrained, multi‐objective, classification methodology is modified to empirically derive an alternative quantitative typology using survey data obtained from 709 firms in three countries (China, Japan, United States). We compare the Miles and Snow typology to the classification empirically derived utilizing this combinatorial optimization clustering procedure. With respect to both variable battery associations and objective statistical criteria, we show that the empirically derived solution clearly dominates the traditional P‐A‐D‐R typology of Miles and Snow. Implications and directions for future research are provided. Copyright © 2004 John Wiley & Sons, Ltd.     

不确定环境下的地震应急救援路径优化研究

针对地震应急救援的特点,引入不确定理论,在地震灾害的背景下,研究震后动态网络环境下的应急救援路径选择问题。用不确定变量表示网络中各路段的破坏程度,综合考虑路径破坏程度及车辆路径连续性等约束条件,以救援时间最短为目标,基于不确定理论建立了动态优化模型,并用改进的遗传算法求解,最后设计算例验证了模型和算法的有效性。     

Numerical Maximisation of Likelihood: A Neglected Alternative to EM?

There is by now a long tradition of using the EM algorithm to find maximum‐likelihood estimates (MLEs) when the data are incomplete in any of a wide range of ways, even when the observed‐data likelihood can easily be evaluated and numerical maximisation of that likelihood is available as a conceptually simple route to the MLEs. It is rare in the literature to see numerical maximisation employed if EM is possible. But with excellent general‐purpose numerical optimisers now available free, there is no longer any reason, as a matter of course, to avoid direct numerical maximisation of likelihood. In this tutorial, I present seven examples of models in which numerical maximisation of likelihood appears to have some advantages over the use of EM as a route to MLEs. The mathematical and coding effort is minimal, as there is no need to derive and code the E and M steps, only a likelihood evaluator. In all the examples, the unconstrained optimiser nlm available in R is used, and transformations are used to impose constraints on parameters. I suggest therefore that the following question be asked of proposed new applications of EM: Can the MLEs be found more simply and directly by using a general‐purpose numerical optimiser?     

The trade-off between fixed vehicle costs and time-dependent arrival penalties in a routing problem

This paper introduces the vehicle routing problem with soft time windows (VRPSTW) in which problem definition differs from ones previously defined in literature. Branch-and-price approach is employed, resulting in a set partitioning master problem and its new subproblem. Novel techniques are consequently developed to solve this new subproblem. Experimental results report the comparisons of these solution techniques under the branch-and-price framework. The VRPSTW solutions have further been compared to the state-of-the-art literature, signifying the superiority of the VRPSTW on this issue.     

Optimal constrained investment in the Cramer-Lundberg model

We consider an insurance company whose surplus is represented by the classical Cramer-Lundberg process. The company can invest its surplus in a risk-free asset and in a risky asset, governed by the Black-Scholes equation. There is a constraint that the insurance company can only invest in the risky asset at a limited leveraging level; more precisely, when purchasing, the ratio of the investment amount in the risky asset to the surplus level is no more than a; and when short-selling, the proportion of the proceeds from the short-selling to the surplus level is no more than b. The objective is to find an optimal investment policy that minimizes the probability of ruin. The minimal ruin probability as a function of the initial surplus is characterized by a classical solution to the corresponding Hamilton-Jacobi-Bellman (HJB) equation. We study the optimal control policy and its properties. The interrelation between the parameters of the model plays a crucial role in the qualitative behavior of the optimal policy. For example, for some ratios between a and b, quite unusual and at first ostensibly counterintuitive policies may appear, like short-selling a stock with a higher rate of return to earn lower interest, or borrowing at a higher rate to invest in a stock with lower rate of return. This is in sharp contrast with the unrestricted case, first studied in Hipp and Plum, or with the case of no short-selling and no borrowing studied in Azcue and Muler.     

Dually sustainable urban mobility option: Shared-taxi operations with electric vehicles

Electric vehicles (EVs) are energy efficient and often presented as a zero-emission transport mode to achieve long-term decarbonization visions in the transport sector. The implementation of a sustainable transportation environment through EV utilization, however, requires the addressing of certain cost and environmental concerns such as limited driving range and battery-charging issues before its full potential can be realized. Nevertheless, a specific type of use of EVs, namely in taxi services, may elicit positive public opinion, as it promises a commitment toward sustainability in urban life. In light of this, this study proposes an integrated approach that combines EV operation with a conceptual design for shared-ride taxi services. As some productivity loss may be naturally expected due to the time spent in charging, it is important to look at whether such performance loss from the passenger and system standpoints can be offset with ingenuity in operational design. In this study, an EV taxi charge-replenishing scheme that can be coupled with a real-time taxi-dispatch algorithm is designed. The proposed EV charging schemes for taxi services are studied via simulations and the effects of the limited driving range and battery-charging details are examined from a system performance viewpoint. The simulation study also reveals illustrative results on the impact of the EV taxi fleet's operation on the charging system. Next, a real-time shared-taxi operation scheme that allows ride sharing with other passengers is proposed to maximize the operational efficiency. The simulation results suggest that the shared-taxi concept can be a viable option to improve on the limitations caused by EV operation. In addition, the importance of projected charging demands and queue delays at different charging locations are also addressed. Some limitations and a future research agenda are also discussed.     

A heuristic approach for the green vehicle routing problem with multiple technologies and partial recharges

This paper presents several heuristics for a variation of the vehicle routing problem in which the transportation fleet is composed of electric vehicles with limited autonomy in need for recharge during their duties. In addition to the routing plan, the amount of energy recharged and the technology used must also be determined. Constructive and local search heuristics are proposed, which are exploited within a non deterministic Simulated Annealing framework. Extensive computational results on varying instances are reported, evaluating the performance of the proposed algorithms and analyzing the distinctive elements of the problem (size, geographical configuration, recharge stations, autonomy, technologies, etc.).