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.     

The gravity p-median model

In this paper we propose a new model for the p-median problem. In the standard p-median problem it is assumed that each demand point is served by the closest facility. In many situations (for example, when demand points are communities of customers and each customer makes his own selection of the facility) demand is divided among the facilities. Each customer selects a facility which is not necessarily the closest one. In the gravity p-median problem it is assumed that customers divide their patronage among the facilities with the probability that a customer patronizes a facility being proportional to the attractiveness of that facility and to a decreasing utility function of the distance to the facility.     

Buffer sizing in multi-product multi-reactor batch processes: Impact of allocation and campaign sizing policies

This paper studies the impact of management policies, such as product allocation and campaign sizing, on the required size of the finished goods inventories in a multi-product multi-reactor batch process. Demand, setup and batch processing times for these products are assumed to be stochastic, and the inventory buffer for every product type needs to be such that target customer service levels are met. To perform this analysis, we develop a queueing model that allows us to explicitly estimate service levels as a function of the buffer size, and the allocation/campaign sizing policies. This model can be used to evaluate the service level given an existing buffer configuration, as well as to determine the buffer sizes required across products to meet a pre-specified service level. It also allows us to formulate a number of insights into how product allocation decisions and campaign planning policies affect buffer sizing decisions in symmetric production systems.     

图书采购流程中资金分配模型研究

本应用现代经济理论的择优分配原理，在图书产出效益函数的性质探讨的基础上，将效益函数的约束条件转化为对资金变量的约束条件，从而简化了资金分配方案的求解。提出了资金在各类别图书中的分配模型，指导图书馆图书采购工作。     

热电联产中热、电分摊比的合理确定

符号表D蒸汽量HF燃料热值W电量下角标h供热比拥m煤耗。分摊比e环境，电n乏汽h比烙q比热量f给水0新汽在热电联产的成本分摊中，如何确定热、电分摊比，一直是一个很有争议的问题［‘－‘］。采用不同分摊方法，其热、电所得的经济效益相差甚大。因此，它直接关系到热力价格的合理制定，影响着供热企业的经济效益和热用户的利益，对热电联产事业的发展有着重要影响。本文在分析现有各种分摊方法的基础上，提出一个新的更为合理的分摊方法。1现有的分摊方法及其缺陷和应用范围1．1热量法将总热耗量按热、电两种能量数量的比例来分配［‘－‘…     

Statistical Multiplexing of Homogeneous Fractional Brownian Streams

In this paper, the statistical multiplexing of independent fractional Brownian traffic streams with the same Hurst value 0.5<H<1 is studied. The buffer overflow probabilities based on steady-state and transient queue length tail distributions are used respectively as the common performance criterion. Under general conditions, the minimal buffer allocation to the merged traffic is identified in either case so that strictly positive bandwidth savings are realized. Impact of the common H value on multiplexing gains is investigated. The analytical results are applicable in data network engineering problems, where ATM is deployed as the transport network carrying long-range dependent data traffic.     

Core equality results for sequences

Given densities μ and v, we characterize nonnegative matrices T such that the μ-statistical core of x equals the v-statistical core of Tx for every real-valued bounded sequence.     

敏捷供应链管理中最优激励方案的实现问题研究

合理的利益分配策略可以稳定敏捷供应链的运营过程,加速敏捷供应链目标的实现.基于委托-代理理论,研究了敏捷供应链利益分配中最优激励方案的设计问题,得出在信息对称条件下由于合作伙伴企业的努力水平可以为主导企业所观测,因而容易制定最优激励方案并实现利益分配.而在更多的信息不对称条件下,需要对合作伙伴企业的产出结果进行监督和评估,通过不同努力水平下的分布概率间接地获得努力水平信息.该结论对于敏捷供应链利益分配系统的设计与实现具有重要指导意义.对最优激励方案在敏捷供应链利益分配中的实现过程进行了说明,通过适当的评价指标体系和评价方法,实现了利益分配中考虑努力水平等因素的激励策略.     

Precisely installing gold nanoparticles at the core/shell interface of micellar assemblies of triblock copolymers

Two reduction-cleavable ABA triblock copolymers possessing two disulfide linkages, PMMA-ss-PMEO₃MA-ss-PMMA and PDEA-ss-PEO-ss-PDEA were synthesized via facile substitution reactions from homopolymer precursors, where PMMA, PMEO₃MA, PDEA, and PEO represent poly(methyl methacrylate), poly(tri(ethylene glycol) monomethyl ether methacrylate, poly(2-(diethylamino)ethyl methacrylate), and poly(ethylene oxide), respectively. Spherical micelles were obtained through supramolecular self-assembly of these two triblock copolymers in aqueous solutions. The resultant micelles with abundant disulfide bonds could serve as soft templates and precisely accommodate gold nanoparticles in the core/shell interface as a result of the formation of Au-S bonds.     

Capital allocation for portfolios with non-linear risk aggregation

Existing risk capital allocation methods, such as the Euler rule, work under the explicit assumption that portfolios are formed as linear combinations of random loss/profit variables, with the firm being able to choose the portfolio weights. This assumption is unrealistic in an insurance context, where arbitrary scaling of risks is generally not possible. Here, we model risks as being partially generated by Lévy processes, capturing the non-linear aggregation of risk. The model leads to non-homogeneous fuzzy games, for which the Euler rule is not applicable. For such games, we seek capital allocations that are in the core, that is, do not provide incentives for splitting portfolios. We show that the Euler rule of an auxiliary linearised fuzzy game (non-uniquely) satisfies the core property and, thus, provides a plausible and easily implemented capital allocation. In contrast, the Aumann–Shapley allocation does not generally belong to the core. For the non-homogeneous fuzzy games studied, Tasche’s (1999) criterion of suitability for performance measurement is adapted and it is shown that the proposed allocation method gives appropriate signals for improving the portfolio underwriting profit.