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.     

基于服务效率的自助服务设施选址模型与算法

研究了服务行业中基于服务效率的自助服务设施选址及选址点服务台数量分配的问题.通过确定选址数量,将排队理论与选址理论结合,以减少顾客访问的旅行成本和排队拥塞成本来提高设施的服务效率,对选址点的服务台进行合理分配,建立了非线性整数规划模型.运用下降算法和遗传算法对设施选址与服务台分配进行设计,并通过大量随机算例验证了算法的有效性.     

禁忌搜索算法在单分配多枢纽轴辐式物流网络中的应用

随着经济的发展,轴辐式网络因有提高运输效率,优化资源配置,产生范围经济等作用越来越多的受到各方面学者的关注。本文对轴辐式网络进行优化分析,改进轴辐式网络的多重分配多枢纽中位问题模型,得到无容量限制的单分配多枢纽中位问题模型(USApHLP)的混合整数线性规划模型,并采用改进的禁忌搜索智能算法来求解,通过算例验证了禁忌搜索算法可以有效的求解单分配多枢纽中位问题。     

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。     

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。     

基于不同支付条件的现金流均衡项目调度优化

以现金流均衡为目标,研究不同支付条件下的项目调度问题.在对所研究问题进行界定的基础上,构建了基于里程碑事件支付条件的优化模型,并将其扩展为基于累计时间、累计挣值和累计费用等支付条件的优化模型;针对问题的强NP-hard属性,设计禁忌搜索启发式算法;在随机生成的标准算例集合上对算法进行测试,并与其他两种启发式算法进行比较...     

基于阶梯型衰退效用函数的竞争选址问题

研究了连锁型企业新设施选址的2个重要因素：市场份额的划分和设施的服务半径。依照效用函数为竞争环境下设施市场份额分配的重要依据,结合消费者的空间选择行为提出了阶梯型效用函数,并应用此效用函数建立了一个竞争环境下连锁型企业新设施选址的新模型,此模型既考虑了服务设施覆盖衰退又考虑了设施的服务半径。最后,给出了求解模型的遗传算法和具体案例。结果表明,该模型和算法可以用于连锁型企业新设施选址决策中,并能够有效地得到问题的近似解。     

电动汽车物流配送系统的换电站选址与路径优化问题研究

在能源、环境形势日益严重的今天,电动汽车因其清洁、节能的显著优势,已经逐步成为物流配送公司重要的新能源交通工具,优化物流配送网络成为电动汽车作为物流工具普及的一个重要问题。本文提出了电动汽车物流配送系统的换电站选址与配送路径优化问题,建立了整数规划模型,并设计禁忌搜索-改进Clarke-Wright 节省的两阶段启发式算法来求解该模型,提出了两种不同的禁忌准则,并且通过算例对这两种准则进行了比较。为了证明算法的有效性,还将该算法的结果同CPLEX的计算结果进行了比较,结果表明该算法更加有效和可靠。最后,对车辆的装载容量、电池续航里程和单位建站成本做敏感性分析,发现总成本随着装载容量的增加而显著降低,电池续航里程的提升有助于降低建站成本并降低目标函数值,而单位建站成本的增加可能减少建站个数,增加运输成本,但由于续航里程的限制,建站个数也可能保持不变。     

多级覆盖设施选址问题建模及求解方法研究

针对应急服务网络优化设计中的设施选址决策问题,分析了应急服务的特殊性以及现有的设施选址模型在求解该问题时的局限性,基于传统的最大覆盖模型提出了应急服务设施的多级覆盖选址模型.该模型对"覆盖"概念的定义进行了扩展,考虑了应急服务的多源服务特性以及对多个服务源提供服务时可存在的差异性.之后基于分布估计算法(Estimation of distribution algorilhms,简称EDAs)提出了模型的启发式求解方法,并基于实验结果对算法参数选取给出了参考意见.     

考虑负效应的城市废弃物中转站多目标选址模型及算法

现有研究中废弃物处理设施的负效应测度没有考虑风向、风力等因素的影响,不能很好地反映实际情况。本文提出了一种基于扩散距离的负效应测度方法;以带中转设施的城市废弃物三层物流网络为研究对象,建立以年均总成本和所有居民日人均负效应最小化为目标的中转站选址问题的混合整数规划模型;根据问题特征,设计一个模拟退火算法与贪心算法相结合的混合算法(HSA)同时求解设施选址、中转站容量及废弃物产生点的指派三个决策。算例结果显示,基于扩散距离的负效应测度方法能较好地反映实际情况;HSA算法在求解同类问题上是可行的和有效的。     

基于线性分段恢复函数的基础设施韧性分析模型——以C县电力系统网络为例

针对近年来一系列突发事件冲击和破坏着城市关键基础设施系统的正常运行,并造成了较为严重的社会后果的现实问题,提出了如何保护关键基础设施系统的研究问题,以使基础设施系统能够对灾害情景做出迅速的响应,并迅速地处理以恢复到常态。本研究基于三种典型的恢复函数提出了线性分段恢复函数,构建了关键基础设施系统韧性分析模型,并用蒙特卡洛模拟的方法应用到C县的电力系统网络加以验证,得到了该韧性分析模型不仅可以帮助决策者在灾害情境下权衡预算成本和韧性的关系,也可以识别关键基础设施系统网络中需要保护的关键节点,从而实现对关键基础设施系统的针对性保护的结论。本研究构建的韧性分析模型有为灾害情境下对电力系统采取针对性保护的现实价值,和开拓了对基础设施系统进行保护研究的分析模型的理论价值。     

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.     

基于双层规划的反恐应急设施选址模型及算法

恐怖袭击常以人流密集地区的平民作为袭击目标，并存在突发性和随机性等特点，极易造成严重的袭击后果。通过反恐应急设施的合理布局可以缩短救援人员和物资的到达时间，从而减轻袭击后果。首先，对反恐应急设施选址问题进行描述，并将其构造为一类离散双层规划模型。其中，上层规划是关于政府选址的0-1规划问题，下层规划则是关于恐怖分子袭击目标选择的0-1规划问题。其次，结合模型和问题的特征设计算法，利用分支定界算法实现上层选址变量的隐枚举，同时通过下层问题的求解来确定上下界并判断是否满足分枝或剪枝的条件。最后，结合南疆地区的交通拓扑网络进行算例分析，结果证明有效的选址方案可以大大降低袭击损失。     

Optimization of Cascade‐Resilient Electrical Infrastructures and its Validation by Power Flow Modeling

Large‐scale outages on real‐world critical infrastructures, although infrequent, are increasingly disastrous to our society. In this article, we are primarily concerned with power transmission networks and we consider the problem of allocation of generation to distributors by rewiring links under the objectives of maximizing network resilience to cascading failure and minimizing investment costs. The combinatorial multiobjective optimization is carried out by a nondominated sorting binary differential evolution (NSBDE) algorithm. For each generators–distributors connection pattern considered in the NSBDE search, a computationally cheap, topological model of failure cascading in a complex network (named the Motter‐Lai [ML] model) is used to simulate and quantify network resilience to cascading failures initiated by targeted attacks. The results on the 400 kV French power transmission network case study show that the proposed method allows us to identify optimal patterns of generators–distributors connection that improve cascading resilience at an acceptable cost. To verify the realistic character of the results obtained by the NSBDE with the embedded ML topological model, a more realistic but also more computationally expensive model of cascading failures is adopted, based on optimal power flow (namely, the ORNL‐Pserc‐Alaska) model). The consistent results between the two models provide impetus for the use of topological, complex network theory models for analysis and optimization of large infrastructures against cascading failure with the advantages of simplicity, scalability, and low computational cost.     

Multistage Stochastic Optimization for Production‐Inventory Planning with Intermittent Renewable Energy

A growing number of companies install wind and solar generators in their energy‐intensive facilities to attain low‐carbon manufacturing operations. However, there is a lack of methodological studies on operating large manufacturing facilities with intermittent power. This study presents a multi‐period, production‐inventory planning model in a multi‐plant manufacturing system powered with onsite and grid renewable energy. Our goal is to determine the production quantity, the stock level, and the renewable energy supply in each period such that the aggregate production cost (including energy) is minimized. We tackle this complex decision problem in three steps. First, we present a deterministic planning model to attain the desired green energy penetration level. Next, the deterministic model is extended to a multistage stochastic optimization model taking into account the uncertainties of renewables. Finally, we develop an efficient modified Benders decomposition algorithm to search for the optimal production schedule using a scenario tree. Numerical experiments are carried out to verify and validate the model integrity, and the potential of realizing high‐level renewables penetration in large manufacturing system is discussed and justified.     

Resource Distribution in Multiple Attacks with Imperfect Detection of the Attack Outcome

This article extends the previous research of consecutive attacks strategy by assuming that an attacker observes the outcome of each attack imperfectly. With given probabilities it may wrongly identify a destroyed target as undestroyed, and wrongly identify an undestroyed target as destroyed. The outcome of each attack is determined by a contest success function that depends on the amount of resources allocated by the defender and the attacker to each attack. The article suggests a probabilistic model of the multiple attacks and analyzes how the target destruction probability and the attacker's relative resource expenditure are impacted by the two probabilities of incorrect observation, the attacker's and defender's resource ratio, the contest intensity, the number of attacks, and the resource distribution across attacks. We analyze how the attacker chooses the number of attacks, the attack stopping rule, and the optimal resource distribution across attacks to maximize its utility.     

Hedging against disruptions with ripple effects in location analysis

Supply systems are subject to disruptions whose impact may not remain confined, but might actually propagate across the network. We consider the problem of optimally protecting a capacitated median system with a limited amount of protective resources subject to disruptions. Specifically, the type of disruption studied is characterized by correlation effects between the facilities, and may result in partial or complete disruption of the facilities involved. The model optimizes protection plans in the face of large area disruptions; i.e., disruptions that affect regions rather than single elements of the system. Examples may be earthquakes, storms, floods, fires, hurricanes, droughts, the spread of diseases, the spread of chemical agents, and cascading failures. The model is also a general framework for the family of fortification problems in the context of location analysis, as it includes uncapacitated facilities and single-target disruptions as special cases. We provide a tri-level formulation of the problem, and we propose an exact solution algorithm which makes use of a tree-search procedure to identify which facilities to protect. The procedure is enhanced by a dual-based pruning rule. The underlying disruption problem is reformulated as a single-level mixed-integer program. The algorithm has been tested on a dataset based on the 2009 L’Aquila earthquake. We verify empirically the efficiency of the pruning rule, and we provide an evaluation of the importance of considering propagation effects in the disruptions.     

A critical review of end-user information system satisfaction research and a new research framework

This paper presents a critical review of research in end-user information system satisfaction (EUISS). An extensive literature search is conducted from which over 50 EUISS related papers are identified. It is found that the past research is dominated by the expectation disconfirmation approach. To provide more insights into the psychological processing of the information system performance construct and its impact upon EUISS, we propose an integrated conceptual model based on the equity and needs theories. The implications of the proposed model for EUISS are discussed, and suggestions are made for testing the model.     

Resilience of Cyber Systems with Over‐ and Underregulation

Recent cyber attacks provide evidence of increased threats to our critical systems and infrastructure. A common reaction to a new threat is to harden the system by adding new rules and regulations. As federal and state governments request new procedures to follow, each of their organizations implements their own cyber defense strategies. This unintentionally increases time and effort that employees spend on training and policy implementation and decreases the time and latitude to perform critical job functions, thus raising overall levels of stress. People's performance under stress, coupled with an overabundance of information, results in even more vulnerabilities for adversaries to exploit. In this article, we embed a simple regulatory model that accounts for cybersecurity human factors and an organization's regulatory environment in a model of a corporate cyber network under attack. The resulting model demonstrates the effect of under‐ and overregulation on an organization's resilience with respect to insider threats. Currently, there is a tendency to use ad‐hoc approaches to account for human factors rather than to incorporate them into cyber resilience modeling. It is clear that using a systematic approach utilizing behavioral science, which already exists in cyber resilience assessment, would provide a more holistic view for decisionmakers.