基于韧性城市视角的冰雪天气下路网恢复问题研究

城市的快速发展使其愈加依赖于生命线基础设施系统,城市在自然或人为突发事件面前的脆弱性日益凸显,城市面对突发事件后的运行与恢复问题受到广泛关注。冬季极端冰雪天气对城市路网系统带来极大冲击,严重降低路网服务能力。本文基于韧性城市视角,对冰雪天气下城市路网韧性的概念和度量方法进行了分析。以提升路网韧性为目标,建立冰雪天气下路网恢复问题的数学模型,解决极端冰雪天气不确定信息下的城市路网除雪应急物资布局问题及其除雪作业优化问题,并设计了相应的启发式求解算法。最后通过算例验证了模型和算法的有效性,以期为城市冰雪天气应对提供决策支持,提升城市应对极端冰雪天气的韧性。     

山区配电网震后恢复及资源调度集成优化研究

地震的发生往往会对配电网产生较大破坏，影响供电质量。山区路网地形地貌的复杂性及受地震破坏等，增加了资源调度的困难，阻碍了山区配电网的恢复。因此，本文针对山区配电网震后恢复及资源调度集成优化问题，建立以恢复绩效最大为目标的非线性混合整数规划模型，并根据模型特点，引入A*算法求解各节点间的最优路径与所需时间，通过改进具有快速稳健特性的细菌群趋药性算法（BCCOA）对模型进行求解，得到山区配电网恢复及资源调度策略，并采用IEEE30，IEEE57和IEEE118节点系统，验证所述策略的可行性和有效性。结果表明：（1）改进的BCCOA相对于枚举法，在求解精度和计算时间上都有较好优势；（2）当资源调度能力一定，改变抢修能力时，配电网恢复绩效平均提升36.40%。而当抢修能力一定，改变资源调度能力时，由于受抢修能力的影响，配电网恢复绩效平均提升7.99%，但资源调度延迟率下降了61.71%，可提高配电网恢复绩效。而同时改变两者的能力时，配电网恢复绩效平均提升38.23%，提升了209.14%，表明资源调度在山区配电网震后恢复中的重要性，说明山区配电网震后恢复及资源调度集成优化研究符合实际决策需求；（3）改进的BCCOA相较于BCCOA，其算法性能提升了2.68%，各改进策略均产生了效果。     

突发事件后生产运作系统的能力应急管理模型研究

分析了因突发事件而能力受损生产运作系统在应急期间应急管理模式,围绕系统内部能力恢复和外部能力采购,综合考虑高额社会惩罚成本,以应急期间的运作总成本（也即总损失）最小为目标构建了多阶段的能力应急模型。通过随机动态规划算法对模型进行数值仿真后得到4个重要的管理学结论,对生产型运作系统应急预案的制定、能力协作伙伴的选择、能力恢复投资以及应急时间跨度的决策均具有重要的指导意义。     

运力受扰的多车场车辆调度干扰管理问题研究

为解决物流配送系统中因运输车辆毁坏而产生的干扰问题,基于干扰管理思想提出了解决问题的扰动恢复策略与实施方案。在扰动度量的基础上,设计了多车场车辆调度扰动恢复策略,建立相应的干扰管理模型。针对多车场车辆调度干扰管理问题的特有属性,设计了一系列求解简化策略,有效简化了问题的求解空间。结合干扰管理模型的特点,使用改进的遗传算法进行求解。最后给出了一个算例,其结果证明了干扰管理模型与算法的有效性。     

基于混合差分进化算法的联合补货-配送优化模型

本文综合考虑联合补货与配送决策,研究了随机需求、允许缺货环境下多企业多产品联合补货与配送集成优化模型,设计了混合差分进化算法(Hybrid Differential Evolution, HDE)对该模型进行求解,同时通过算例与遗传算法、标准的DE算法进行了比较,证实HDE算法高效且稳定;另外,设计了一个先补货再配送的两阶段优化模型,对比优化结果发现采用供应链协同时补货成本较高,配送成本较低,且总成本较低。最后,对相关参数进行了敏感性分析,发现需求率和库存维持成本的变动对总成本的影响远远大过次要订货成本对总成本的影响。     

突发事件下的医院应急群决策模型研究*

针对突发事件下的医院应急管理问题,进行了医院应急群决策模型研究。考虑到医院应急决策者心理行为特征的偏向性,将病人应急方案的选择设计成一个以医院决策者信息与病人集结信息之间距离最小为目标的非线性规划模型。根据病人的伤重程度和应急资源使用度的损益值,获得医院应急情景下的综合价值,根据累积前景理论,通过计算区间概率,获得医院应急情景下不同应急方式的累积前景值。使用模拟植物生长算法求解,莸得病人的最优集结信息。案例分析表明,该决策过程可以使得突发事件下医院应急管理方案达到最优。     

政府奖励机制下闭环供应链的利他关切性决策

考虑政府奖励机制的影响,文章对闭环供应链中的利他关切性决策进行研究。文章首先构建了制造商是否利他关切的两种分散决策和集中决策模型,并求解给出最优回收决策和政府最优奖励决策;然后,对各模型的最优决策进行比较分析,讨论制造商的利他关切对决策的影响。在此基础上,设计“数量折扣联合固定费用-成本共担”契约,对闭环供应链系统进行协调。最后,通过数值算例验证模型结论。研究表明：(1)政府奖励强度的设置存在阈值。只有当奖励强度大于阈值时,奖励机制才有效,才能促进废旧品的回收。(2)制造商的利他关切行为有利于系统获利,但不利于制造商利润的增长,但是这种利他行为扩大了制造商和零售商的谈判空间,有利于协调机制的实施;(3)政府最优奖励强度与WEEE回收价格和价格敏感系数正相关,与利他系数正相关。并且,政府奖励强度的增加有利于“数量折扣联合固定费用-成本共担”契约协调机制的达成。     

基于差分进化算法的运输容量约束下有数量折扣的模糊联合补货模型研究

针对采购管理中广泛存在的不确定性,将单位库存成本和可变订货成本视为模糊变量,构建了运输容量约束下有数量折扣的模糊联合补货模型,此模型属于NP-hard问题,目前缺乏可靠的全局优化求解算法。在选取梯级平均综合表示法对总成本去模糊的基础上,设计了基于自适应混合差分进化算法的求解方法,并通过算例验证了此模糊联合补货模型的有效性和求解算法的全局优化能力。     

考虑灾民痛苦感知的应急避难所选址与物资分配优化

大规模灾害发生初期，应急物资往往不能及时供应，受灾群众因缺乏物资和服务产生痛苦感知。针对该问题，设计数值评定量表（NRS）构建痛苦函数，刻画灾民痛苦感知成本，将灾民感知因素纳入应急总成本的决策考量。提出以总社会成本（物流成本和痛苦心理成本之和）最小为应急救援目标，构建了一个考虑灾民疏散与重新安置、临时避难所选址和物资分配的集成优化模型。设计经典混合整数规划方法和改进的遗传算法对所构模型进行求解，并应用于海南威马逊台风案例。案例分析表明，模型和算法能有效解决考虑灾民痛苦的避难所选址与物资分配问题，揭示了随着受灾规模的增加和疏散时间的减少，遗传算法的求解结果优于经典求解方法。     

基于极大团模型的多星多站测控资源调度方法研究

针对实际应用中大量卫星测控需求不能得到满足这一瓶颈问题，为缓解地面站测控资源有限与不断增长的测控需求数量之间的矛盾，研究多星多站测控调度问题。考虑卫星-地面站可见测控弧段间的冲突及地面测控设备转换时间约束，同时考虑同一测控设备上可能存在多个满足需求的测控弧段，以测控需求是否分配到某一测控弧段为决策变量，建立了多星多站测控调度0-1整数规划模型。针对传统0-1整数规划模型约束众多、规模庞大、难以求解的不足，本文提出了测控弧段区间图描述，基于区间图描述构建了测控调度极大团模型，大幅减少了约束数量，有效提高问题求解效率。为了获取区间图的所有极大团，设计了一个多项式时间(O(nlogn))的极大团获取算法，并从理论上证明了算法的复杂度和最优性。此外，针对存在升降轨特定类型测控数量要求的调度问题，本文对极大团模型进行了拓展，建立了相应的线性整数规划模型。实验结果表明，对于满足实际应用需求的周计划调度问题(测控需求数量≥9000),极大团模型能够在数分钟内得到单次测控需求调度问题最优解。对于多次测控需求调度问题，拓展的极大团模型也能高效求解，得到问题最优解或近似最优解，满足实际应用需求。相对于目前...     

Sequential Hazards Resilience of Interdependent Infrastructure System: A Case Study of Greater Toronto Area Energy Infrastructure System

Coupled infrastructure systems and complicated multihazards result in a high level of complexity and make it difficult to assess and improve the infrastructure system resilience. With a case study of the Greater Toronto Area energy system (including electric, gas, and oil transmission networks), an approach to analysis of multihazard resilience of an interdependent infrastructure system is presented in the article. Integrating network theory, spatial and numerical analysis methods, the new approach deals with the complicated multihazard relations and complex infrastructure interdependencies as spatiotemporal impacts on infrastructure systems in order to assess the dynamic system resilience. The results confirm that the effects of sequential hazards on resilience of infrastructure (network) are more complicated than the sum of single hazards. The resilience depends on the magnitude of the hazards, their spatiotemporal relationship and dynamic combined impacts, and infrastructure interdependencies. The article presents a comparison between physical and functional resilience of an electric transmission network, and finds functional resilience is always higher than physical resilience. The multiple hazards resilience evaluation approach is applicable to any type of infrastructure and hazard and it can contribute to the improvement of infrastructure planning, design, and maintenance decision making.     

An interdependent layered network model for a resilient supply chain

This paper addresses the problem of designing supply chains that are resilient to natural or human-induced extreme events. It focuses on the development of efficient restoration strategies that aid the supply chain in recovering from a disruption, thereby limiting the impact on its customers. The proposed restoration model takes into account possible disruptions to infrastructures, e.g., transportation and communications, by explicitly formulating their logical relationships with supply chains. A problem solving process is proposed that provides for cooperation between the managers of the infrastructures and the managers of the supply chains disrupted by an extreme event. Both the model and the problem solving process are exercised with a realistic industry problem.     

Probabilistic Multiple Hazard Resilience Model of an Interdependent Infrastructure System

Multiple hazard resilience is of significant practical value because most regions of the world are subject to multiple natural and technological hazards. An analysis and assessment approach for multiple hazard spatiotemporal resilience of interdependent infrastructure systems is developed using network theory and a numerical analysis. First, we define multiple hazard resilience and present a quantitative probabilistic metric based on the expansion of a single hazard deterministic resilience model. Second, we define a multiple hazard relationship analysis model with a focus on the impact of hazards on an infrastructure. Subsequently, a relationship matrix is constructed with temporal and spatial dimensions. Further, a general method for the evaluation of direct impacts on an individual infrastructure under multiple hazards is proposed. Third, we present an analysis of indirect multiple hazard impacts on interdependent infrastructures and a joint restoration model of an infrastructure system. Finally, a simplified two‐layer interdependent infrastructure network is used as a case study for illustrating the proposed methodology. The results show that temporal and spatial relationships of multiple hazards significantly influence system resilience. Moreover, the interdependence among infrastructures further magnifies the impact on resilience value. The main contribution of the article is a new multiple hazard resilience evaluation approach that is capable of integrating the impacts of multiple hazard interactions, interdependence of network components (layers), and restoration strategy.     

A Screening Methodology for the Identification and Ranking of Infrastructure Vulnerabilities Due to Terrorism

The extreme importance of critical infrastructures to modern society is widely recognized. These infrastructures are complex and interdependent. Protecting the critical infrastructures from terrorism presents an enormous challenge. Recognizing that society cannot afford the costs associated with absolute protection, it is necessary to identify and prioritize the vulnerabilities in these infrastructures. This article presents a methodology for the identification and prioritization of vulnerabilities in infrastructures. We model the infrastructures as interconnected digraphs and employ graph theory to identify the candidate vulnerable scenarios. These scenarios are screened for the susceptibility of their elements to a terrorist attack, and a prioritized list of vulnerabilities is produced. The prioritization methodology is based on multiattribute utility theory. The impact of losing infrastructure services is evaluated using a value tree that reflects the perceptions and values of the decisionmaker and the relevant stakeholders. These results, which are conditional on a specified threat, are provided to the decisionmaker for use in risk management. The methodology is illustrated through the presentation of a portion of the analysis conducted on the campus of the Massachusetts Institute of Technology.     

Inherent Costs and Interdependent Impacts of Infrastructure Network Resilience

Recent studies in system resilience have proposed metrics to understand the ability of systems to recover from a disruptive event, often offering a qualitative treatment of resilience. This work provides a quantitative treatment of resilience and focuses specifically on measuring resilience in infrastructure networks. Inherent cost metrics are introduced: loss of service cost and total network restoration cost. Further, “costs” of network resilience are often shared across multiple infrastructures and industries that rely upon those networks, particularly when such networks become inoperable in the face of disruptive events. As such, this work integrates the quantitative resilience approach with a model describing the regional, multi‐industry impacts of a disruptive event to measure the interdependent impacts of network resilience. The approaches discussed in this article are deployed in a case study of an inland waterway transportation network, the Mississippi River Navigation System.     

Control of interrelated research and development projects

The purpose of this paper is to examine a type of resource allocation problem arising in the context of research and development activities. The particular problem analyzed involves scheduling a group of projects in such a way that total cost is minimized while several precedence relations are satisfied and specific completion times are maintained. The primary difficulty results from the existence of commonalities that allow the successful completion of a particular project to be applied to several different purposes. A solution approach is developed which combines a one-pass network algorithm to deal with the precedence and time restrictions and a dynamic programming procedure to allocate the resources to each project.     

Integration of Critical Infrastructure and Societal Consequence Models: Impact on Swedish Power System Mitigation Decisions

Critical infrastructures provide society with services essential to its functioning, and extensive disruptions give rise to large societal consequences. Risk and vulnerability analyses of critical infrastructures generally focus narrowly on the infrastructure of interest and describe the consequences as nonsupplied commodities or the cost of unsupplied commodities; they rarely holistically consider the larger impact with respect to higher‐order consequences for the society. From a societal perspective, this narrow focus may lead to severe underestimation of the negative effects of infrastructure disruptions. To explore this theory, an integrated modeling approach, combining models of critical infrastructures and economic input–output models, is proposed and applied in a case study. In the case study, a representative model of the Swedish power transmission system and a regionalized economic input–output model are utilized. This enables exploration of how a narrow infrastructure or a more holistic societal consequence perspective affects vulnerability‐related mitigation decisions regarding critical infrastructures. Two decision contexts related to prioritization of different vulnerability‐reducing measures are considered—identifying critical components and adding system components to increase robustness. It is concluded that higher‐order societal consequences due to power supply disruptions can be up to twice as large as first‐order consequences, which in turn has a significant effect on the identification of which critical components are to be protected or strengthened and a smaller effect on the ranking of improvement measures in terms of adding system components to increase system redundancy.     

Decision analysis and risk models for land development affecting infrastructure systems

Coordination and layering of models to identify risks in complex systems such as large-scale infrastructure of energy, water, and transportation is of current interest across application domains. Such infrastructures are increasingly vulnerable to adjacent commercial and residential land development. Land development can compromise the performance of essential infrastructure systems and increase the costs of maintaining or increasing performance. A risk-informed approach to this topic would be useful to avoid surprise, regret, and the need for costly remedies. This article develops a layering and coordination of models for risk management of land development affecting infrastructure systems. The layers are: system identification, expert elicitation, predictive modeling, comparison of investment alternatives, and implications of current decisions for future options. The modeling layers share a focus on observable factors that most contribute to volatility of land development and land use. The relevant data and expert evidence include current and forecasted growth in population and employment, conservation and preservation rules, land topography and geometries, real estate assessments, market and economic conditions, and other factors. The approach integrates to a decision framework of strategic considerations based on assessing risk, cost, and opportunity in order to prioritize needs and potential remedies that mitigate impacts of land development to the infrastructure systems. The approach is demonstrated for a 5,700-mile multimodal transportation system adjacent to 60,000 tracts of potential land development.     

Strategies for Appointment Policy Design with Patient Unpunctuality

Appointment policy design is complicated by patients who arrive earlier or later than their scheduled appointment time. This article considers the design of scheduling rules in the presence of patient unpunctuality and how they are impacted by various environmental factors. A simulation optimization framework is used to determine how to improve performance by adjusting the schedule of appointments. Prior studies (that did not include patient unpunctuality) have found that a scheduling policy with relatively consistent appointment interval lengths in the form of a dome or plateau dome rule to perform well in a variety of clinic environments. These rules still perform reasonably well here, but it is shown that a combination of variable‐length intervals and block scheduling are better at mitigating the effects of patient unpunctuality. In addition, performance improves if the use of this policy increases toward the end of the scheduling session. Survey and observational data collected at multiple outpatient clinics are used to add realism to the input parameters and develop practical guidelines for appointment policy decision making.