考虑消费者路径选择的多元需求FIFLP

考虑了顾客的多元需求和需求流的路径选择的截流设施选址问题,网络上顾客行走的OD对存在不同的路径,引用重力模型来确定路径的选择概率,运用需求系数函数实现需求流的部分截得,建立了数目约束下的截得最大流量模型,给出了求解模型的启发式算法,先用增加算法求初始解,再用交换算法改进。最后给出一个算例,通过与精确解的比较,证明了算法的可行性和有效性。     

一种需求和采购价均为时变的EOQ模型

本文提出了一种需求和采购价均为时变的EOQ模型,证明了该模型的总库存成本目标函数在给定条件下为凸函数,给出了寻求最佳采购次数及服务水平的算法,并对该模型进行了数值仿真和灵敏度分析。     

关于Stulman超储物资限额模型的二个注记

本文对Ｓｔｕｌｍａｎ超储物资限额模型作了二点补充讨论，给出了确定型需求条件下超储物资限额的表达式，并探讨了将该模型推广于一般随机需求环境下的一种可能途径。     

ATIS环境下交通配流的动态演化模型

考虑交通网络中实现用户均衡态的过程,引入决策出行费用的概念,提出一个 ATIS 环境下的交通配流动态演化模型,模型中的 OD 需求是可变内生的.利用稳定性定理分析了该动态系统的稳定性,说明在一定条件下,系统的用户均衡态是稳定的.采用改进的欧拉法完成了模型的数值试验,结果表明该动态系统确实可以达到用户均衡态,同时也发现,某些模型参数的变化可能导致路径流量和 OD 费用演化轨迹的无规则摆动.     

一类供应链的Stackelberg主从对策问题研究

本文研究了供应链中的一般订货模式和协调模式,提出了一类供应链Stackelberg主从对策问题,卖方作为主方给出最小补充期策略,买方作为从方以最优库存策略响应。考虑一致价格折扣弥补买方的库存成本增加,以及买方的库存成本合理化,建立买方需求确定下的卖方成本优化模型。最后,应用遗传算法对石油分销系统Stackelberg主从对策问题离线仿真计算,得出Stackelberg主从对策均衡解。     

促进知识共享的装备保障服务新模式

本文分析了大型装备保障服务模式的知识共享需求,讨论了适应装备保障服务特点的装备保障服务跨组织运行模型框架,给出了基于知识共享的装备保障服务解决方案形成模式.     

基于可控提前期的两供应商-单制造商协同模型

本文建立了一个基于BOM表关系的两供应商-单制造商供应链模型,在需求服从正态分布和给定服务水平约束条件下,决策制造商分别向两供应商的订货提前期和订货批量,使得供应链成本最优.文章通过模型分析得出所建立的模型是一个凸规划问题,存在唯一的最优解,并且给出了模型的求解算法,最后通过一个算例说明了模型和算法的有效性.     

考虑不同碳排放处理模式的动态供应商选择及采购批量问题研究

不同的碳排放处理模式及不确定的市场需求等因素影响下,如何选择供应商并确定采购批量直接影响企业的运营和效益。本文在多时间周期、多产品种类、多供应商及随机需求情形下,同时考虑不同碳排放处理模式,分析动态供应商选择及采购批量等最优决策问题,构建混合整数非线性规划模型。通过设计变异算子和扰动因子来改进粒子群算法,力求在短时间内求解大规模决策问题。针对不同规模供应商选择及采购批量决策问题,采用精确方法、近似方法和改进粒子群算法求解。数值实验验证了模型及改进粒子群算法的有效性和可行性,分析了碳税、碳交易价格及碳限额对供应链管理的影响,并给出了供应商选择及碳排放处理的决策参考建议。     

基于需求转移的易逝性产品最优动态定价策略

本文针对易逝性产品中新产品对老产品需求的转移作用,应用收入管理方法得出老产品的最优动态定价策略,并应用最大凹向包络理论给出了简化算法的方法.数值算例表明在存在需求转移的情况下,应更早的提供较低的价格.最后作者给出了包含生产和定价的综合模型.     

非平稳需求下考虑碳配额的供应链选址-库存模型与算法研究

针对非平稳需求下考虑碳配额的多期、多需求情景的三级供应链选址-库存问题，构建了库存策略（t，s，S）下供应链运营期望收益最大化的两阶段选址-库存随机优化模型，依据供应链企业不同着眼点下的决策流程，提出了一种三步骤的分层级启发式算法，该算法包含了选址导向和需求导向的两种子问题序贯求解模式。数值算例验证了在不同问题规模及需求类型下算法求解的有效性，同时分析了供应链网络设计、各成本占比和运营收益对不同供应链成本结构、需求不确定性与碳配额的敏感性，并给出了管理上的启示。     

基于随机p-鲁棒优化的国家血液战略储备库选址-库存模型

以往的救灾实践对建立国家血液战略储备体系提出了迫切要求。国家血液战略储备库的建设问题亟待解决。由于血液产品特性以及应急血液保障特性的存在,使得国家血液战略储备库的选址决策具有一定的复杂性。本文将问题定位为选址-库存问题。首先,以应急条件下血液保障及时度最高为目标,构建了一个不确定环境下考虑多情景、多血型、多阶段、带提前期、有容量限制、日常随机需求、有预算约束及协同定位的国家血液战略储备库选址-库存模型。同时,为了规避应急条件下的不确定风险,进一步构建了国家血液战略储备库选址-库存问题的随机p-鲁棒优化模型。该模型为离散非线性混合整数规划模型,难以快速精确求解。故基于模型性质,设计了相应的遗传算法。最后,设计了两组算例验证模型与算法的有效性。其中,第1组算例基于我国大陆地区31个省级血液中心与省级行政区的数据,并根据不同预算值给出6个算例,得到了国家血液战略储备库的选址-库存决策方案。第2组算例为6个不同规模的模拟算例,用来测试不同规模下的算法性能。算例结果表明：遗传算法的性能更好;鲁棒解与确定性模型最优值相差不大（最大差距≤1.08%）,可降低不确定性导致的风险。实践中,可对本文所建模型稍作改进,应用于具有类似特征的易腐品（药品、粮食等）应急物资储备库选址-库存决策。     

Traffic regulation with single- and dual-homed ISPs under a percentile-based pricing policy

We investigate how a customer (an enterprise or a large organization), when facing a percentile-based pricing policy, can optimally balance the Internet access cost and the traffic buffering delay penalty by traffic regulation. The problem is referred to as the Optimal Traffic Regulation (OTR) problem. Solutions to various cases of the OTR problem are provided. For a customer with a single-homed ISP, we present optimal solutions to the OTR problem based on dynamic programming for the offline case with a known traffic demand pattern. A real-time traffic scheduling algorithm is proposed to deal with the online case where the traffic demands are different from a given demand pattern. We further extend the dynamic programming model to the case of dual-homed ISPs. Experimental results on the data from an Internet trace confirm the effectiveness of our solutions. Part of the results has been presented in the First International Conference on Scalable Information Systems.     

Exact and heuristic algorithms for solving the generalized minimum filter placement problem

We consider a problem of placing route-based filters in a communication network to limit the number of forged address attacks to a prescribed level. Nodes in the network communicate by exchanging packets along arcs, and the originating node embeds the origin and destination addresses within each packet that it sends. In the absence of a validation mechanism, one node can send packets to another node using a forged origin address to launch an attack against that node. Route-based filters can be established at various nodes on the communication network to protect against these attacks. A route-based filter examines each packet arriving at a node, and determines whether or not the origin address could be legitimate, based on the arc on which the packet arrives, the routing information, and possibly the destination. The problem we consider seeks to find a minimum cardinality subset of nodes to filter so that the prescribed level of security is achieved. We formulate a mixed-integer programming model for the problem and derive valid inequalities for this model by identifying polynomially-solvable subgraphs of the communication network. We also present three heuristics for solving the filter placement problem and evaluate their performance against the optimal solution provided by the mixed-integer programming model. The authors gratefully acknowledge the comments of two anonymous referees, whose input led to an improved version of this paper. Dr. Smith gratefully acknowledges the support of the Office of Naval Research under Grant #N00014-03-1-0510 and the Defense Advanced Research Projects Agency under Grant #N66001-01-1-8925.     

雪灾救援物资车辆-直升机联合运送的调度问题研究

文章研究了雪灾救援中车辆与直升飞机联合运输应急物资的调度问题。在道路疏通能力与整体救援时间有限的情况下,首先决策需要疏通的道路,及其疏通顺序,对疏通后的需求点采用汽车运输补给,该阶段决策目标为汽车运输量最大化;然后调度直升飞机对各点疏通前的物资短缺以及未疏通的需求点进行物资运输,设计每架直升机的飞行路线与各点投放量,使得两个阶段运输完成后,最大化所有需求点中最小物资可维持时间。文章对两个阶段分别建立混合整数规划模型,并设计启发式算法,最后以西北某区域的路网结构为例,设计了数值算例。     

城市道路交通网络容量的建模

交通网络承载着具有意志行为的个体组成的交通流,交通网络容量表示道路网络的交通承载能力,与交通网络的OD结构密切相关.本文在分析城市道路交通网络OD结构特征的基础上,提出表达道路网络容量的基本思想和模型,阐明交通网络容量受到交通流特性、网络要素容量、废气排放量、服务水平和系统效率等因素的影响,基于不同的考虑方法可以建立不同的网络容量模型.基于路段容量约束的网络容量描述了道路系统承载交通流的物理极限;基于路段容量约束和交通环境约束的网络容量则描述了道路系统承载交通流的环境极限;基于服务水平的网络容量描述了系统提供某种服务水平条件下的最大交通承载能力;基于效率的网络容量描述了网络资源使用效率和用户效益同时最大化条件下的道路网络承载能力.文章深入分析了所提出的各种模型之间的关系,在实际应用中可根据交通规划和管理所追求的具体目标,采取相应的模型.     

A Genetic Algorithm for the Weight Setting Problem in OSPF Routing

With the growth of the Internet, Internet Service Providers (ISPs) try to meet the increasing traffic demand with new technology and improved utilization of existing resources. Routing of data packets can affect network utilization. Packets are sent along network paths from source to destination following a protocol. Open Shortest Path First (OSPF) is the most commonly used intra-domain Internet routing protocol (IRP). Traffic flow is routed along shortest paths, splitting flow at nodes with several outgoing links on a shortest path to the destination IP address. Link weights are assigned by the network operator. A path length is the sum of the weights of the links in the path. The OSPF weight setting (OSPFWS) problem seeks a set of weights that optimizes network performance. We study the problem of optimizing OSPF weights, given a set of projected demands, with the objective of minimizing network congestion. The weight assignment problem is NP-hard. We present a genetic algorithm (GA) to solve the OSPFWS problem. We compare our results with the best known and commonly used heuristics for OSPF weight setting, as well as with a lower bound of the optimal multi-commodity flow routing, which is a linear programming relaxation of the OSPFWS problem. Computational experiments are made on the AT&T Worldnet backbone with projected demands, and on twelve instances of synthetic networks.     

The P-Hub maximal covering problem and extensions for gradual decay functions

The p-hub maximal covering problem aims to find the best locations for hubs so as to maximize demands within a coverage distance with a predetermined number of hubs. Classically, the problem is defined in the framework of binary coverage only; an origin–destination pair is covered if the cost (time, etc.) is lower than the critical value, and not covered at all if the cost is greater than the critical value. In this paper, we extend the definition of coverage, introducing “partial coverage”, which changes with distance. We present new and efficient mixed-integer programming models that are also valid for partial coverage for single and multiple allocations. We present and discuss the computational results with different data sets.     

An application of mathematical programming to soybean processing

In this paper, a mathematical programming methodology is applied to a production planning problem involving a soybean processing plant which can purchase its raw materials from multiple origins and must ship its finished products to multiple destinations. A time horizon production planning model is developed, with the objective of maximizing the net income produced by this plant. This model is tested for a five origin, three destination, processing network, over a thirteen month time horizon. Test results, in terms of a production plan and associated purchasing-allocation decisions, are presented and discussed.     

Using branch-and-price approach to solve the directed network design problem with relays

We present node-arc and arc-path formulations, and develop a branch-and-price approach for the directed network design problem with relays (DNDR). The DNDR problem can be used to model many network design problems in transportation, service, and telecommunication system, where relay points are necessary. The DNDR problem consists of introducing a subset of arcs and locating relays on a subset of nodes such that in the resulting network, the total cost (arc cost plus relay cost) is minimized, and there exists a directed path linking the origin and destination of each commodity, in which the distances between the origin and the first relay, any two consecutive relays, and the last relay and the destination do not exceed a predefined distance limit. With the node-arc formulation, we can directly solve small DNDR instances using mixed integer programming solver. With the arc-path formulation, we design a branch-and-price approach, which is a variant of branch-and-bound with bounds provided by solving linear programs using column generation at each node of the branch-and-bound tree. We design two methods to efficiently price out columns and present computational results on a set of 290 generated instances. Results demonstrate that our proposed branch-and-price approach is a computationally efficient procedure for solving the DNDR problem.     

Optimization of loss-balanced multicast in all-optical WDM networks

In wavelength-division multiplexing (WDM) optical networks, multicast is implemented by constructing a light-forest, which is a set of light-trees with each light-tree rooted from the multicast source and terminated at a partition subset of the destination nodes. Multicast routing scenario has considerable impact on the quality of optical signal received at each destination. To guarantee the fairness of signal quality at different destinations in a multicast session, it is desirable to construct a loss-balanced light-forest to deliver the multicast traffic. A loss-balanced light-forest is composed of a set of light-trees bounded in size (number of destinations per multicast tree), in size variation (difference in the number of destinations among different multicast trees), and in dimension (maximum source-to-destination distance on each multicast tree). This paper investigates the multicast routing and wavelength assignment (MC-RWA) problem under the loss-balance constraint. The problem is formulated as an optimization model using integer linear programming (ILP). Numerical solutions to the optimization model can supply useful performance benchmarks for loss-balance-constrained optical multicast in WDM networks. This work was supported by the NSF under Grant OCI-0225642 and by the U.S. DoE under Grant DE-FG02–03ER25566.