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ABSTRACTThis article provides a new technique for quantitatively characterizing the progress of recovery operations in the aftermath of a disaster event. The approach extends previous research on measuring dynamic, or adaptive, disaster resilience by developing a robust approach for characterizing nonlinear disaster recovery. In doing so, it enables a more accurate mathematical representation of different categories of recovery behavior and provides support for a much broader application of existing theory. Because the new approach inherits the ability to compare the relative behavior of multiple scenarios simultaneously, it also can serve as the basis for analytically comparing the expected performance of different plans for recovery operations. Practical application of the technique is demonstrated and discussed in the context of recovering electrical power after Hurricane Sandy struck the New York metropolitan area.

AbstractThis paper presents a new approach for quantitatively modeling the resilience of a system that has been disrupted by a sudden‐impact event. It introduces a new theoretical model that explicitly incorporates representations of the enabling and inhibiting forces that are inherent within postdisruption recovery behavior. Based on a new, more comprehensive measure of resilience that is able to capture both negative and positive deviations in performance, a generic mass–spring system is then used to illustrate the applicability of the theoretical model. The interplay between the enabling and inhibiting forces that is revealed by the new model provides a new theoretical basis for understanding the complexity of resilience and disaster recovery. With the addition of the new resilience measure, it lends support for defining and characterizing a new type of resilient behavior: unstable resilience.

ABSTRACTThis study draws from the system resilience literature to propose three different metrics for evaluating the resilience performance of organizations against disruptions: the initial loss due to the disruption, the maximum loss, and the total loss over time. In order to show the usefulness of the developed metrics in practice, we deploy these metrics to study the effectiveness of two resilience strategies: maintaining operational slack and broadening operational scope, by empirically analyzing the performance of manufacturing firms that experienced a disruption during the period from 2005 to the end of 2014. The results show that maintaining certain aspects of operational slack and broadening business scope and geographic scope can affect these different metrics in different ways. Our results help decisionmakers in risk management to gain a better understanding of the conditions under which the recommended strategies actually improve organizations' resilience, as well as the ways in which they may do so.

This article constructs a framework to help a decisionmaker allocate resources to increase his or her organization's resilience to a system disruption, where resilience is measured as a function of the average loss per unit time and the time needed to recover full functionality. Enhancing resilience prior to a disruption involves allocating resources from a fixed budget to reduce the value of one or both of these characteristics. We first look at characterizing the optimal resource allocations associated with several standard allocation functions. Because the resources are being allocated before the disruption, however, the initial loss and recovery time may not be known with certainty. We thus also apply the optimal resource allocation model for resilience to three models of uncertain disruptions: (1) independent probabilities, (2) dependent probabilities, and (3) unknown probabilities. The optimization model is applied to an example of increasing the resilience of an electric power network following Superstorm Sandy.

ABSTRACTThis article introduces a general approach for characterizing cyberinfrastructure resilience in the face of multiple malicious cyberattacks, such as when a sequence of denial‐of‐service attacks progressively target an already weakened information system. Although loss assessment frequently focuses on a single overall measure such as cost or downtime, the proposed technique considers both the timing and the amount of loss associated with each individual attack, as well as whether this loss is incurred suddenly or is "slow‐onset." In support of this, an underlying mathematical model is developed to represent the relative impact of each attack and the corresponding length of time that its effects persist within the system, as well as to illustrate the trade‐offs between these two factors. The model is extended to represent uncertainty in its parameters and thus to support comparative analyses among various security configurations with respect to a baseline estimate of resilience. Monte Carlo simulation is then used to illustrate the model's capabilities and to support a discussion of its ability to provide for more effective decision making in the context of disaster planning and mitigation. [Submitted: March 21, 2011. Revised: July 14, 2011; November 4, 2011. Accepted: December 19, 2011.]

ABSTRACTThe one‐dimensional cutting stock problem (CSP) is a classic combinatorial optimization problem in which a number of parts of various lengths must be cut from an inventory of standard‐size material. The classic CSP ensures that the total demand for a given part size is met but ignores the fact that parts produced by a given cutting pattern may be destined for different jobs. As a result, applying the classic CSP in a dynamic production environment may result in many jobs being open (or partially complete) at any point in time—requiring significant material handling or sorting operations. This paper identifies and discusses a new type of one‐dimensional CSP, called the ordered CSP, which explicitly restricts to one the number of jobs in a production process that can be open, or in process, at any given point in time. Given the growing emphasis on mass customization in the manufacturing industry, this restriction can help lead to a reduction in both in‐process inventory levels and material handling activities. A formal mathematical formulation is provided for the new CSP model, and its applicability is discussed with respect to a production problem in the custom door and window manufacturing industry. A genetic algorithm (GA) solution approach is then presented, which incorporates a customized heuristic for reducing scrap levels. Several different production scenarios are considered, and computational results are provided that illustrate the ability of the GA‐based approach to significantly decrease the amount of scrap generated in the production process.

ABSTRACTThis article continues a recent trend of exploring the linkages between supply chain risk, resilience and decision‐making at an individual level. Specifically, the article reports the results of a behavioral study that explores whether and how the perceptions of supply chain risk and resilience influence the decisions made regarding the selection of a new source of critical components. The study uses a full factorial design for a scenario‐based role‐playing experiment involving over 1,000 valid responses drawn from multiple sampling pools including supply chain managers, students, and crowd‐sourced respondents. The results indicate that the perception of supply chain resilience—whether it is by systemic resilience communication, such as training or corporate pronouncements, or through personal exposure—significantly influences decision‐making, although personal exposure appears to have a stronger impact on the outcome. This relationship is significantly moderated by the risk propensity of the individual decision‐makers. The article concludes with a discussion of the results and their implication for both theory and practice. The discussion provides a framework for integrating the macro and micro levels by arguing that the micro issues can potentially moderate and mediate the relationships and findings observed at the macro level. Failure to integrate the micro effects can result in variance that the macro‐level analysis is unable to explain.

ABSTRACTClassification is often a critical task for business managers in their decision‐making processes. It is generally more difficult for a classification scheme to produce accurate results when the input domains of the various output classes coincide, to some degree, with one another. In an attempt to address this issue, this article discusses a data‐driven algorithm that identifies the region of coincidence, or overlap, for two‐group classification problems by empirically determining the convex boundary for each group. The results are extendable to multigroup classification. The class membership of a new observation is then determined by its relative position with respect to each of these boundaries. Due to minimal data storage requirements, this boundary‐point classification technique can adapt to changing conditions far more easily than other approaches. Test results demonstrate that the new classification technique has similar performance to a back‐propagation neural network under static conditions and significantly outperforms a back‐propagation neural network under dynamic conditions.

Intro -- Contents -- Contributors -- About the Authors -- About the Editors -- Chapter-1 -- Humanitarian Research and Managing Humanitarian Operations -- 1.1 Purpose of Volume -- 1.2 Overview Section -- 1.3 Theory Section -- 1.4 Application Section -- 1.5 Summary -- References -- Chapter-2 -- Modeling Approaches and Metrics to Evaluate Nonprofit Operations -- 2.1 Introduction -- 2.2 Modeling Methods and Strategies -- Modeling the Nonprofit Operation -- Managerial Strategies -- 2.3 Performance Metrics -- Input Metrics -- Output Metrics -- Efficiency Metrics -- 2.4 Conclusion -- References -- Chapter-3 -- Disaster Response Logistics: Chief Findings of Fieldwork Research -- 3.1 Introduction -- 3.2 Disaster Response Logistics (DRL) -- 3.3 Chief Findings from Fieldwork Research -- All Facets of Disaster Response Logistics are Socio-Technical Processes -- Humanitarian Logistics and Commercial Logistics are Very Different -- Size and Complexity Matters: Catastrophes and Disasters Require Radically Different Responses -- It is Crucial to Integrate the Civic Society in the Response Effort -- It is Important to Design Plans for Easy Integration of Outside Help -- Prepositioning Critical Supplies is Required for Effective Response to Catastrophes -- Control of Precautionary/Opportunistic Buying Could Help Provide a First Wave of Resources -- Control of Material Convergence will Enhance Disaster Response -- New Supporting Technologies and Systems Could Foster Effectiveness -- 3.4 Conclusions -- References -- Chapter-4 -- Cash as a Form of Relief -- 4.1 Introduction -- 4.2 Changing Landscape -- 4.3 Types of Cash Transfer Programmes (CTPs) -- 4.4 CTPs-Change to Humanitarian Supply Chains -- 4.5 Impact of CTPs on Humanitarian Logisticians (HL) -- 4.6 CTP-Lean or Agile? -- 4.7 Delivery Mechanisms of Cash in Humanitarian Operations -- 4.8 Conclusion -- References.

AbstractThe worldwide healthcare and economic crisis caused by the COVID‐19 pandemic highlights the need for a deeper understanding of investing in the mitigation of epidemic risks. To address this, we built a mathematical model to optimize investments into two types of measures for mitigating the risks of epidemic propagation: prevention/containment measures and treatment/recovery measures. The new model explicitly accounts for the characteristics of networks of individuals, as a critical element of epidemic propagation. Subsequent analysis shows that, to combat an epidemic that can cause significant negative impact, optimal investment in either category increases with a higher level of connectivity and intrinsic loss, but it is limited to a fraction of that total potential loss. However, when a fixed and limited mitigation investment is to be apportioned among the two types of measures, the optimal proportion of investment for prevention and containment increases when the investment limit goes up, and when the network connectivity decreases. Our results are consistent with existing studies and can be used to properly interpret what happened in past pandemics as well as to shed light on future and ongoing events such as COVID‐19.