不对称信息下反恐阻止网络设计

当前，我国面临的恐怖主义威胁日益严峻。为防止境外恐怖分子潜入，政府可设计反恐阻止网络，通过在交通网络中有效地分配例如安检仪器、传感设备等阻断资源，来提前识别和拦截正在潜入的恐怖分子。特别地，考虑信息不对称情形，把阻断资源分为"公开"和"隐蔽"两种类型，并假设恐怖分子观察不到"隐蔽"阻断。主要研究政府应如何同时优化两类阻断方案，才能发挥信息优势，设置"陷阱"并降低袭击分析。首先，将该问题构造为双层规划模型，上层规划是关于政府的阻止网络设计问题，下层规划则是关于恐怖分子的袭击节点选择和入侵路径优化问题。随后，设计一类用改进遗传算法处理上层规划，并结合下层规划直接求解的混合算法。其中，改进体现于杂交算子和变异算子的设计。最后，结合喀什地区进行算例分析，并分析"隐蔽"阻断的作用机理。     

考虑最坏中断损失下的P-中位设施选址问题的模型与算法研究

蓄意突袭以及恐怖袭击会造成设施服务的突然中断成为网络系统的主要危害之一,因此网络设施选址决策应该同时考虑正常和紧急状态下系统的运作成本.本文研究考虑最坏中断损失下的网络设施选址问题,建立了该问题的双层规划模型,上层规划涉及设施选址决策,下层规划研究确定设施位置后,设施中断产生最大损失的问题.本文运用基于拉格朗日松弛的混合遗传算法来求解该双层规划问题.将European150数据集作为研究对象,对比研究了本文研究问题与传统的P-中位选址问题的结果,分析不同选址策略下网络系统的效率被中断影响的程度是不同的.最后通过改变一些关键参数,比如常规运作权重、设施数量、中断设施数量,对相关结果进行了分析.     

基于随机运输路径选择的物流中心选址模型

旨在以物流系统总费用最小化为目标进行物流中心选址规划.政府做出物流中心选址规划后,客户会选择合适的供应商进行交易,并根据已有物流中心与交通流分布进行货物运输路线决策,使其总费用最少.论文提出了物流中心选址双层规划模型,考虑投资费用的约束,保证用户平衡的同时使整个物流系统总费用最低.其中上层规划目标是使物流系统总费用最小化,下层规划建立了一个Logit随机用户均衡模型,并构造了一个等价的凸规划问题.最后针对模型提出了一个算法,并通过算例说明其可行性.     

重大突发事件应急救援设施选址的多目标决策模型

常用的应急服务设施选址模型主要有覆盖模型、p-中心模型和p-中值模型,这些模型并不适合重大突发事件的应急救援设施选址.分析重大突发事件应急救援设施选址问题的特点,建立一个多目标决策模型,该模型考虑了应急救援设施的公平性和效率性,整合了传统选址模型中常用的最大覆盖模型、p-中心模型和p-中值模型,以适应重大突发事件应急救援设施的不同部署策略,通过实例讨论该模型的求解方法和求解策略.     

模糊环境下边境反恐交通网络设计模型及应用

“一带一路”战略加深了我国与邻国的交流，也为境外恐怖分子潜入提供可乘之机。政府可通过在交通网络中有效地分配阻断(或安检)资源，来提前识别和拦截正在潜入的恐怖分子。特别地，考虑各路段阻断资源需求、袭击后果为模糊不确定的情形，结合政府与恐怖分子的互动行为构建双层规划模型，并设计一类改进遗传算法求解。最后，结合南疆交通网络进行仿真分析，结果发现：通过边境反恐交通网络设计可以降低境内恐袭风险，恐怖分子偏好袭击边境口岸城市和境内重要城市，政府应优先针对口岸城市周围路段实施强化安检，还须重视安检资源投入的“边际效用递减规律”，以及安检资源和安检能力间的“替代关系”，合理优化两者比例，提高反恐效率。     

一类应急物资调度的双层规划模型及其算法

应急物资的调度是应急管理中的一个重要分支,衡量其运作效果的两个最常用的指标是应急时间和应急成本,目前的建模大多都以多目标规划为主.但现实告诉我们,面对应急事件,应该在最短的允许时间奔赴现场,而成本与此目标相比要次之.为此,建立了单资源应急物资调度的双层规划模型,上层为时间目标,下层为成本目标,并将此模型的求解转化为若干个有限制的运输问题的求解,利于计算机实现.算例证明了算法的有效性.     

服务水平保证下应急抢修点选址模型及求解算法研究

本文研究了一类故障率低但重要性较高设备的应急抢修点选址问题。设备的故障发生过程和从应急抢修点到故障设备的通行时间是随机的,每个设备被分配给一个应急抢修点进行抢修,并且整个应急抢修系统的服务水平要大于给定标准。本文以应急抢修点总开设成本最小作为目标,同时考虑了设备覆盖约束、抢修分配关系约束和抢修系统服务水平约束,在合理的假设下证明设备发生故障且应急抢修小组迟到的总次数服从泊松分布,最终将应急抢修点选址问题描述为一个0-1整数规划模型。通过对模型中的覆盖约束和抢修系统服务水平约束进行松弛,设计了相应的拉格朗日启发式算法。最后通过对大量随机算例进行计算,证明了该模型和算法的有效性。     

基于持续运营机会约束的竞争设施点选址研究——一种有效的实数编码遗传求解算法

竞争设施点选址是空间经济、区域发展、组合优化和系统工程的重要课题之一。本文以市场份额最大化为目标,研究了基于持续运营机会约束的竞争设施点选址问题,并给出了一种有效的实数编码遗传求解算法。在求解模型方面,首先假定运营成本是竞争设施点规模大小的函数,并对设施点持续运营概率进行机会约束,借鉴引力模型建立竞争设施点选址-设计问题的非线性混合整数规划模型。其次,考虑到选址变量和规模变量的数值类型,以及编码变换问题,设计了一种实数编码遗传求解算法。通过数值实验表明,对不同规模问题的实际计算结果,该算法可以在较短时间内获得最优解,可行解和精确解之间误差小于0.5%,相关比较分析也讨论了该算法的优越性和实用性,为竞争设施点选址问题的研究提供了不同的视角和实用求解算法。     

基于场景分析的应急资源布局模型研究

突发事件越来越频繁地发生,对应急物资保障提出了更高的要求,好的资源布局方案对地区需求的保障程度至关重要。本文利用场景分析的方法,给出一个适于多点需求,多点救助的应急资源布局多目标规划模型,并根据资源的具体配置情况决定选址,以期为选址决策者提供选址的依据。求解该问题时,针对模型设计了算法,并进行了算例分析。     

基于非概率可靠性的铁路应急设施选址-路径鲁棒优化

鉴于铁路应急设施选址研究中很难合理估计参数的概率分布或确定其隶属函数的问题,将选址-路径问题与区间非概率可靠性方法结合起来,以复杂网络理论为基础,提出网络节点区间权重的确定方法,同时考虑节点权重、边权及径权区间不确定性的共同作用,构建铁路应急设施选址节点加权网络。基于区间非概率可靠性理论及区间运算规则,提出路径的非概率可靠性度量及最优时间可靠度路径选择方法,建立节点权重、边权及径权均为区间数的非概率可靠性铁路应急设施选址-路径鲁棒优化模型,并给出了求解算法,确定了基于区间模型的铁路应急设施鲁棒选址的最优方案。算例表明,本文的优化方案能更好地保证救援的时间鲁棒性,能有效地规避不确定因素波动对设施选址的长期风险,具有很好的实际应用价值。     

A general space-time model for combinatorial optimization problems (and not only)

We consider the problem of defining a strategy consisting of a set of facilities taking into account also the location where they have to be assigned and the time in which they have to be activated. The facilities are evaluated with respect to a set of criteria. The plan has to be devised respecting some constraints related to different aspects of the problem such as precedence restrictions due to the nature of the facilities. Among the constraints, there are some related to the available budget. We consider also the uncertainty related to the performances of the facilities with respect to considered criteria and plurality of stakeholders participating to the decision. The considered problem can be seen as the combination of some prototypical operations research problems: knapsack problem, location problem and project scheduling. Indeed, the basic brick of our model is a variable x_ilt which takes value 1 if facility i is activated in location l at time t, and 0 otherwise. Due to the conjoint consideration of a location and a time in the decision variables, what we propose can be seen as a general space-time model for operations research problems. We discuss how such a model permits to handle complex problems using several methodologies including multiple attribute value theory and multiobjective optimization. With respect to the latter point, without any loss of the generality, we consider the compromise programming and an interactive methodology based on the Dominance-based Rough Set Approach. We illustrate the application of our model with a simple didactic example.     

Adaptive Management: A Paradigm for Remediation of Public Facilities Following a Terrorist Attack

Terrorist actions are aimed at maximizing harm (health, psychological, economical, and political) through the combined physical impacts of the act and fear. Immediate and effective response to a terrorist act is critical to limit human and environmental harm, effectively restore facility function, and maintain public confidence. Though there have been terrorist attacks in public facilities that we have learned from, overall our experiences in restoration of public facilities following a terrorist attack are limited. Restoration of public facilities following a release of a hazardous material is inherently far more complex than in industrial settings and has many unique technical, economic, social, and political challenges. For example, there may be a great need to quickly restore the facility to full operation and allow public access even though it was not designed for easy or rapid restoration, and critical information is needed for quantitative risk assessment and effective restoration must be anticipated to be incomplete and uncertain. Whereas present planning documents have substantial linearity in their organization, the “adaptive management” paradigm provides a constructive parallel paradigm for restoration of public facilities that anticipates and plans for uncertainty, inefficiencies, and stakeholder participation. Adaptive management grew out of the need to manage and restore natural resources in highly complex and changing environments with limited knowledge about causal relationships and responses to restoration actions. Similarities between natural resource management and restoration of a public facility after a terrorist attack suggest that integration of adaptive management principles explicitly into restoration processes will result in substantially enhanced and flexible responses necessary to meet the uncertainties of potential terrorist attacks.     

设施失灵风险下不确定需求应急定位-路径鲁棒优化研究

为抵御突发灾害对路网造成的破坏性和设施失灵风险,降低系统成本,并快速完成应急救援任务,本文考虑到受灾点物资需求量的不确定和风险对救援系统的影响,采用直升机进行物资运送以规避路径风险。建立了最小化应急物流系统总成本和物资到达需求点总救援时间为双目标的应急物流定位-路径鲁棒优化模型,基于相对鲁棒优化方法处理需求不确定,采用偏差鲁棒优化思想描述设施失灵风险损失,采用遗传算法进行求解。通过对三个算例进行数据仿真实验,证明了相对鲁棒优化方法在处理需求不确定和偏差鲁棒优化方法在处理设施失灵风险方面的有效性,进而为解决应急设施点的开设和救援物资的安全及时准确配送,增强应急物流系统的风险应对能力提供了有效的方法。     

商品交易市场网络节点选址的双层规划模型——以安徽省砀山县为例

优化商品交易市场选址方法有利于提升城市规划布局模式,促进城市有序发展。在分析现有选址模型在应用时存在不足的基础上,采用双层规划模型构建了商品交易市场选址模型。上层规划从决策者的角度出发,通过考虑修建商品交易市场的总费用与购买者需求量之间的关系,以使总的收益最大。下层规划描述使用者的选择行为,对应于最大效用值选择商品交易市场。同时,针对所建模型,设计了启发式求解算法,并以安徽省砀山县为例进行初步应用验证。     

Risk Analysis of Terrorist Attacks

A quantitative probabilistic/systems analysis model is described which is useful for allocating resources to safeguard valuable documents or materials in either a fixed-site facility or a moving convoy against an overt terrorist attack. The model is also useful for ranking the sensitive areas at a site according to their survivability of a given hypothesized terrorist attempt. To compare various defense strategies and security configurations, the probability of a successful terrorist activity is computed based on event tree models of the site/security configuration. This calculation incorporates a realistic engagement model (in the event a guard force engages the terrorists prior to completion of their objective) and information on barrier penetration times (for example, distribution of the time to defeat a chain link fence or vault door, traverse an open area, and so forth). Two security analyses are described to illustrate the methodology. One example considers a terrorist attack on a convoy transporting a missile from a storage to a launch facility. The second example involves an attack on a munitions storage facility.     

Designing time-of-use tariffs in electricity retail markets using a bi-level model – Estimating bounds when the lower level problem cannot be exactly solved

Time-of-use tariffs are a pricing strategy for a product or service in which the supplier establishes time-differentiated prices. Dynamic (e.g., day-ahead) time-differentiated electricity prices can contribute to increase the retailer's profit, allow end-users to reduce the consumption costs and enhance grid efficiency.The electricity retailer and the consumer are hierarchically related. The interaction between them can be modeled by a bi-level (BL) optimization model – the retailer is the upper level decision maker and the consumer is the lower level decision maker. The retailer and the consumer have different and conflicting goals: the retailer establishes the pricing scheme to sell electricity to consumers to maximize his profit; the consumer reacts to these prices by determining the operation of the controllable loads in order to minimize the discomfort and the electricity bill.In this work, a BL optimization model incorporating shiftable, interruptible and thermostatic loads is proposed. The upper level problem is tackled by a particle swarm optimization algorithm while the lower level problem is solved by an exact mixed-integer programming solver. The inclusion of the thermostatic load in the lower level problem imposes a much higher computational burden. Therefore, it may not be possible to find the optimal lower level solution, and a sub-optimal lower level solution is infeasible to the BL problem. Considering a computational budget, this work proposes an approach to compute good quality estimates of bounds for the upper level objective function, providing the leader further information and allowing him to make sounder decisions in an adequate time frame.     

基于总量控制的工业领域能源分配双层规划模型

在工业领域实施能源消费总量控制是节能管理工作的趋势,可以在约束能源消费总量的同时带动工业能耗强度下降,并进一步促进社会综合能耗强度的下降。在此背景下,能源的科学分配尤其重要,并具有明显的层次性。本文针对此问题构建了工业领域能源分配双层规划模型,上层为多目标规划,描述上级政府行为,以社会的经济性、协调性和可持续发展性为目标,以在下级之间的合理调配为约束条件,下层为目标规划,描述能源在下级行业之间的配置优先权。根据该模型的决策特点,给出了基于决策者偏好的迭代算法。算例说明该分配方法可以有效倒逼各级地方政府"转方式、调结构",科学引导高耗能行业的发展。