Review of Quantitative Methods for Supply Chain Resilience Analysis

Supply chain resilience (SCR) manifests when the network is capable to withstand, adapt, and recover from disruptions to meet customer demand and ensure performance. This paper conceptualizes and comprehensively presents a systematic review of the recent literature on quantitative modeling the SCR while distinctively pertaining it to the original concept of resilience capacity. Decision-makers and researchers can benefit from our survey since it introduces a structured analysis and recommendations as to which quantitative methods can be used at different levels of capacity resilience. Finally, the gaps and limitations of existing SCR literature are identified and future research opportunities are suggested

Catalogue and Toolbox of Risk Assessment and Management Tools

The ENHANCE project is concerned with analysing and working towards improved public-private partnerships for managing risks from natural hazards. An important issue for such partnerships is the methods, tools and processes available for assessing risk and risk management options. Risk analysis has long provided useful input to decision-making. At the same time, the field of risk analysis is in motion and an enhanced framing of risk analysis and risk management is being embraced following an iterative cycle organized around notions of learning, innovation and transformation. This broadened vision on risk analysis is a key issue for the ENHANCE project as well, which takes many and different perspectives on analysing, understanding, communicating and managing risk. This report lays out the status quo at the outset of the project regarding risk analytical tools, methods and data that are currently used by project partners in ENHANCE. The task overall develops a catalogue of existing risk assessment and management tools and methods to describe the concepts of iterative risk management and further sets up a toolbox, containing individual models and tools to be used by the case studies in their analyses. While work in the cases study, including methodological development, is in process, we find that ENHANCE partners and cases employ a multitude of models, tools and data ranging from impact analysis, different risk modelling techniques to various decision-support methods. A number of tools that encapsulate these methods are also available with the consortium. We suggest the tools and methods in use can be useful starting points for working towards a broader vision of iterative risk management. While the work so far, and this deliverable, have focussed on populating the technical stages of the risk analytical cycle (visually identified as the inner circle), we suggest in the next phase of ENHANCE, additional efforts should be dispensed to better understand adaptive management aspects associated with using these methods and tools, such as learning, innovation and transformation, which we exhibit visually in an outer circle. This report proceeds as follows: We start with laying out key elements of risk analysis and management in section 2, which also describes the new framing organized around the iterative risk-management concept. Methods for assessing risk and evaluating risk management are discussed in section 3. Then we consider methods, models and datasets that are in use in the ENHANCE case studies at the moment (section 4), before section 5 concludes. Finally and importantly, the annex lists more information on cases studies, for which detailed information was received from the project partners

Computational environment for modeling and enhancing community resilience: Introducing the center for risk-based community resilience planning

The resilience of a community is defined as its ability to prepare for, withstand, recover from and adapt to the effects of natural or human-caused disasters, and depends on the performance of the built environment and on supporting social, economic and public institutions that are essential for immediate response and long-term recovery and adaptation. The performance of the built environment generally is governed by codes, standards, and regulations, which are applicable to individual facilities and residences, are based on different performance criteria, and do not account for the interdependence of buildings, transportation, utilities and other infrastructure sectors. The National Institute of Standards and Technology recently awarded a new Center of Excellence (NIST-CoE) for Risk-Based Community Resilience Planning, which is headquartered at Colorado State University and involves nine additional universities. Research in this Center is focusing on three major research thrusts: (1) developing the NIST-Community Resilience Modeling Environment known as NIST-CORE, thereby enabling alternative strategies to enhance community resilience to be measured quantitatively; (2) developing a standardized data ontology, robust data architecture and data management tools in support of NIST-CORE; and (3) performing a comprehensive set of hindcasts on disasters to validate the data architecture and NIST-CORE

A review of lean and agile management in humanitarian supply chains: analysing the pre-disaster and post-disaster phases and future directions

Disasters have quadrupled over the last two decades leading to unprecedented loss of life. The objective of disaster-focussed humanitarian supply chains (HSCs) is to ensure saving maximum lives with limited resources; despite severe uncertainties. Therefore, significant research has investigated lean and agile in HSCs; to effectively source and speedily deploy resources, with minimum wastage; in each disaster life-cycle phase. However, the literature and research findings are currently highly disjointed regarding how lean and agile principles may be aligned with different HSC activities in the disaster management lifecycle; and do not provide a collective understanding for practitioners and researchers. This paper reviews and organises the literature on HSCs in relation to lean and agile paradigms, focussing on the pre-disaster (mitigation and preparedness) and post-disaster (response and recovery) phases. Findings reveal, all phases benefit from both lean and agile, with agile benefitting the response phase most. The phases are inter-dependent and identifying optimum decoupling points for lean and agile principles are crucial. Majority research has focussed on individual or a couple of phases. Therefore, authors recommend research on integrating the functions of the different phases by employing lean and agile principles, to generate rapid response, economies of scale and cost minimisation

Optimization-based decision-making models for disaster recovery and reconstruction planning of transportation networks

The purpose of this study is to analyze optimization-based decision-making models for the problem of Disaster Recovery Planning of Transportation Networks (DRPTN). In the past three decades, seminal optimization problems have been structured and solved for the critical and sensitive problem of DRPTN. The extent of our knowledge on the practicality of the methods and performance of results is however limited. To evaluate the applicability of those context-sensitive models in real-world situations, there is a need to examine the conceptual and technical structure behind the existing body of work. To this end, this paper performs a systematic search targeting DRPTN publications. Thereafter, we review the identified literature based on the four phases of the optimization-based decision-making modeling process as problem definition, problem formulation, problem-solving, and model validation. Then, through content analysis and descriptive statistics, we investigate the methodology of studies within each of these phases. Eventually, we detect and discuss four research improvement areas as [1] developing conceptual or systematic decision support in the selection of decision attributes and problem structuring, [2] integrating recovery problems with traffic management models, [3] avoiding uncertainty due to the type of solving algorithms, and [4] reducing subjectivity in the validation process of disaster recovery models. Finally, we provide suggestions as well as possible directions for future research.TU Berlin, Open-Access-Mittel - 202

The State-of-the-Art Survey on Optimization Methods for Cyber-physical Networks

Cyber-Physical Systems (CPS) are increasingly complex and frequently integrated into modern societies via critical infrastructure systems, products, and services. Consequently, there is a need for reliable functionality of these complex systems under various scenarios, from physical failures due to aging, through to cyber attacks. Indeed, the development of effective strategies to restore disrupted infrastructure systems continues to be a major challenge. Hitherto, there have been an increasing number of papers evaluating cyber-physical infrastructures, yet a comprehensive review focusing on mathematical modeling and different optimization methods is still lacking. Thus, this review paper appraises the literature on optimization techniques for CPS facing disruption, to synthesize key findings on the current methods in this domain. A total of 108 relevant research papers are reviewed following an extensive assessment of all major scientific databases. The main mathematical modeling practices and optimization methods are identified for both deterministic and stochastic formulations, categorizing them based on the solution approach (exact, heuristic, meta-heuristic), objective function, and network size. We also perform keyword clustering and bibliographic coupling analyses to summarize the current research trends. Future research needs in terms of the scalability of optimization algorithms are discussed. Overall, there is a need to shift towards more scalable optimization solution algorithms, empowered by data-driven methods and machine learning, to provide reliable decision-support systems for decision-makers and practitioners

Multi-level Analytic Network Process Model to Mitigate Supply Chain Disruptions in Disaster Recovery Planning

Over the past few decades, environmental changes have led to more frequent occurrences and greater intensities of natural disasters worldwide. In terms of globally connected supply chains, this has resulted in an enormous economical loss for corporations. Therefore, Business Continuity and Disaster Recovery (BC/DR) planning and management has become essential for businesses in order to protect their critical business flow. Yet there is a lack of systematic and transparent methodologies for companies to handle this problem. Hence, this thesis introduces a novel approach to combine consecutive steps of the Disaster Recovery Planning (DRP) process within one application. The multi-criteria decision-making (MCDM) tool called the Analytic Network Process (ANP) is employed to identify critical products of a business and match them with optimal disruption mitigation strategies based on an evaluation of benefits, opportunities, costs, and risks (BOCR). To validate the method developed in this thesis, a case study using historical data of a U.S. company (Company XYZ) is introduced. The results of the ANP mathematical modeling demonstrate that the developed methodology provides a valuable approach to analyze and confirm BC/DR planning decisions. Moreover, an expert of Company XYZ confirmed that the suggested solution established through this case study is in agreement with the preferable choice based on his expertise and professional decision-making. Further research could extend the proposed methodology to other fields of BC/DR planning, such as IT Disaster Recovery Planning or Human Disaster Relief

Nuclear emergency decision support : a behavioural OR perspective

Operational researchers, risk and decision analysts need consider many behavioural issues. Despite many OR applications in nuclear emergency decision support, the literature has not paid sufficient attention to behavioural matters. In working on designing decision support processes for nuclear emergency management, we have encountered many behavioural issues. In this paper we synthesise the findings in the literature with our experience and identify a number of behavioural challenges to nuclear emergency decision support. In addition to challenges in model-building and interaction, we pay attention to a behavioural issue that is often neglected: the analysis itself and the communication of its implications may have behavioural consequences. We introduce proposals to address these challenges. First, we propose the use of models relying on incomplete preference information, outlining a framework and illustrating it with data from a previous decision analysis for the Chernobyl Project. Moreover, we reflect on the responsibility that rests on the analyst in addressing behavioural issues sensitively in order to lessen the effects on public stress. In doing so we make a distinction between System 1 Societal Deliberation and System 2 Societal Deliberation and discuss how this can help structure societal deliberation in the context of nuclear emergencies