Christopher W. Zobel

Representing perceived tradeoffs in defining disaster resilience

Two of the primary measures that characterize the concept of disaster resilience are the initial impact of a disaster event and the subsequent time to recovery. This paper presents a new analytic approach to representing the relationship between these two characteristics by extending a multi-dimensional approach for predicting resilience into a technique for fitting the resilience function to the preferences and priorities of a given decision maker. This allows for a more accurate representation of the perceived value of different resilience scenarios to that individual, and thus makes the concept more relevant in the context of strategic decision making.

Characterizing multi-event disaster resilience

This paper presents an approach for providing a quantitative measure of resilience in the presence of multiple related disaster events. It extends the concepts of the resilience triangle and predicted disaster resilience by considering the tradeoffs between multiple criteria for each individual sub-event, as well as for an entire multi-event situation. The focus of the research is on sudden-onset disasters, and on the initial impact of each sub-event as well as the amount of time available to work towards recovery of the system before the next sub-event occurs. A mathematical model is developed for the new resilience measure, along with an approach for graphically representing the relationships between the different criteria. An example is then provided of using the new approach to compare the relative resilience of different scenarios under a representative multi-event disaster situation. The results demonstrate that characterizing multi-event resilience analytically can ultimately provide a great depth of information and thus support better disaster planning and mitigation.

Static and dynamic metrics of economic resilience for interdependent infrastructure and industry sectors

Abstract Infrastructures are needed for maintaining functionality and stability of society, while being put under substantial stresses from natural or man-made shocks. Since avoiding shock is impossible, increased focus is given to infrastructure resilience, which denotes the ability to recover and operate under new stable regimes. This paper addresses the problem of estimating, quantifying and planning for economic resilience of interdependent infrastructures, where interconnectedness adds to problem complexity. The risk-based economic input–output model enterprise, a useful tool for measuring the cascading effects of interdependent failures, is employed to introduce a framework for economic resilience estimation. We propose static and dynamic measures for resilience that confirm to well-known resilience concepts of robustness, rapidity, redundancy, and resourcefulness. The quantitative metrics proposed here (static resilience metric, time averaged level of operability, maximum loss of functionality, time to recovery) guide a preparedness decision making framework to promote interdependent economic resilience estimation. Using the metrics we introduce new multi-dimensional resilience functions that allow multiple resource allocation scenarios. Through an example problem we demonstrate the usefulness of these functions in guiding resource planning for building resilience.

A two?stage procurement model for humanitarian relief supply chains

Purpose – The purpose of this paper is to discuss and to help address the need for quantitative models to support and improve procurement in the context of humanitarian relief efforts.Design/methodology/approach – This research presents a two‐stage stochastic decision model with recourse for procurement in humanitarian relief supply chains, and compares its effectiveness on an illustrative example with respect to a standard solution approach.Findings – Results show the ability of the new model to capture and model both the procurement process and the uncertainty inherent in a disaster relief situation, in support of more efficient and effective procurement plans.Research limitations/implications – The research focus is on sudden onset disasters and it does not differentiate between local and international suppliers. A number of extensions of the base model could be implemented, however, so as to address the specific needs of a given organization and their procurement process.Practical implications – Despi...

Utilization of neural networks for the recognition of variance shifts in correlated manufacturing process parameters

Traditional statistical process control (SPC) charting techniques were developed for use in discrete industries where independence exists between process parameters over time. Process parameters from many manufacturing industries are not independent, however, but they are serially correlated. Consequently, the power of traditional SPC charts was greatly weakened. The paper discusses the development of neural network models to identify successfully shifts in the variance of correlated process parameters. These neural network models can be used to monitor manufacturing process parameters and signal when process adjustments are needed.

An augmented neural network classification approach to detecting mean shifts in correlated manufacturing process parameters

Statistical process control (SPC) techniques have traditionally been used to identify when the mean of a manufacturing process has shifted out of control. In situations where there is correlation among the observed outputs of the process, however, the underlying assumptions of SPC are violated and alternative approaches such as neural networks become necessary in order to characterize the process behaviour. This paper discusses the development of a neural network technique that provides a significantly improved capability for recognizing these process shifts as compared to the current techniques in the literature. The procedure in question is an augmented neural-network based approach, which incorporates a data preprocessing classification algorithm that provides information to facilitate early detection of out of control operating conditions. This approach is shown to improve significantly upon the performance of previous neural network techniques for identifying process shifts in the presence of correlation.

Neural network-based simulation metamodels for predicting probability distributions

Simulation is an important tool for supporting decision-making under uncertainty, particularly when the system under consideration is too complex to evaluate analytically. The amount of time required to generate large numbers of simulation replications can be prohibitive, however, necessitating the use of a simulation metamodel in order to describe the behavior of the system under new conditions. The purpose of this study is to examine the use of neural network metamodels for representing output distributions from a stochastic simulation model. A series of tests on a well-known simulation problem demonstrate the ability of the neural networks to capture the behavior of the underlying systems and to represent the inherent uncertainty with a reasonable degree of accuracy.

The Ordered Cutting Stock Problem

The 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.

Quantitatively Representing Nonlinear Disaster Recovery

This 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 di?erent 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.

Evaluation of neural network variable influence measures for process control

Decision-making frequently involves identifying how to change input parameters in a given process in order to effect a directed change in the process output. Artificial neural networks have been used extensively to model business and manufacturing processes and there are several existing neural network-based influence measures that allow a decision-maker to assess the relative impact of each variable on process performance. The purpose of this paper is to review those neural network-based measures of variable influence, and to identify the combination of those measures that results in a comprehensive approach to characterizing variable influence within a trained neural network model. We then demonstrate how this comprehensive approach can be used as a tool to guide decision makers in dynamic process control.