High-Performance Computing for Scheduling Decision Support: A Parallel Depth-First Search Heuristic

Many academic disciplines - including information systems, computer science, and operations management - face scheduling problems as important decision making tasks. Since many scheduling problems are NP-hard in the strong sense, there is a need for developing solution heuristics. For scheduling problems with setup times on unrelated parallel machines, there is limited research on solution methods and to the best of our knowledge, parallel computer architectures have not yet been taken advantage of. We address this gap by proposing and implementing a new solution heuristic and by testing different parallelization strategies. In our computational experiments, we show that our heuristic calculates near-optimal solutions even for large instances and that computing time can be reduced substantially by our parallelization approach

Disruption Response Support For Inland Waterway Transportation

Motivated by the critical role of the inland waterways in the United States\u27 transportation system, this dissertation research focuses on pre- and post- disruption response support when the inland waterway navigation system is disrupted by a natural or manmade event. Following a comprehensive literature review, four research contributions are achieved. The first research contribution formulates and solves a cargo prioritization and terminal allocation problem (CPTAP) that minimizes total value loss of the disrupted barge cargoes on the inland waterway transportation system. It is tailored for maritime transportation stakeholders whose disaster response plans seek to mitigate negative economic and societal impacts. A genetic algorithm (GA)-based heuristic is developed and tested to solve realistically-sized instances of CPTAP. The second research contribution develops and examines a tabu search (TS) heuristic as an improved solution approach to CPTAP. Different from GA\u27s population search approach, the TS heuristic uses the local search to find improved solutions to CPTAP in less computation time. The third research contribution assesses cargo value decreasing rates (CVDRs) through a Value-focused Thinking based methodology. The CVDR is a vital parameter to the general cargo prioritization modeling as well as specifically for the CPTAP model for inland waterways developed here. The fourth research contribution develops a multi-attribute decision model based on the Analytic Hierarchy Process that integrates tangible and intangible factors in prioritizing cargo after an inland waterway disruption. This contribution allows for consideration of subjective, qualitative attributes in addition to the pure quantitative CPTAP approach explored in the first two research contributions

A system dynamics & emergency logistics model for post-disaster relief operations

Emergency teams’ efficiency in responding to disasters is critical in saving lives, reducing suffering, and for damage control. Quality standards for emergency response systems are based on government policies, resources, training, and team readiness and flexibility. This research investigates these matters in regards to Saudi emergency responses to floods in Jeddah in 2009 and again in 2011. The study is relevant to countries who are building emergency response capacity for their populations: analysing the effects of the disaster, communications and data flows for stakeholders, achieving and securing access, finding and rescuing victims, setting up field triage sites, evacuation, and refuges. The research problem in this case was to develop a dynamic systems model capable of managing real time data to allow a team or a decision-maker to optimise their particular response within a rapidly changing situation. The Emergency Logistics Centre capability model responds to this problem by providing a set of nodes relevant to each responsibility centre (Civil Defence, regional/local authority including rescue teams, police and clean-up teams, Red Crescent). These nodes facilitate information on resource use and replenishment, and barriers such as access and weather can be controlled for in the model. The dynamic systems approach builds model capacity and transparency, allowing emergency response decision-makers access to updated instructions and decisions that may affect their capacities. After the event, coordinators and researchers can review data and actions for policy change, resource control, training and communications. In this way, knowledge from the experiences of members of the network is not lost for future position occupants in the emergency response network. The conclusion for this research is that the Saudi emergency response framework is now sufficiently robust to respond to a large scale crisis, such as may occur during the hajj with its three million pilgrims. Researchers are recommended to test their emergency response systems using the Emergency Logistics Centre model, if only to encourage rethinking and flexibility of perhaps stale or formulaic responses from staff. This may lead to benefits in identification of policy change, training, or more appropriate pathways for response teams

OPTIMIZATION MODELS AND METHODOLOGIES TO SUPPORT EMERGENCY PREPAREDNESS AND POST-DISASTER RESPONSE

This dissertation addresses three important optimization problems arising during the phases of pre-disaster emergency preparedness and post-disaster response in time-dependent, stochastic and dynamic environments. The first problem studied is the building evacuation problem with shared information (BEPSI), which seeks a set of evacuation routes and the assignment of evacuees to these routes with the minimum total evacuation time. The BEPSI incorporates the constraints of shared information in providing on-line instructions to evacuees and ensures that evacuees departing from an intermediate or source location at a mutual point in time receive common instructions. A mixed-integer linear program is formulated for the BEPSI and an exact technique based on Benders decomposition is proposed for its solution. Numerical experiments conducted on a mid-sized real-world example demonstrate the effectiveness of the proposed algorithm. The second problem addressed is the network resilience problem (NRP), involving an indicator of network resilience proposed to quantify the ability of a network to recover from randomly arising disruptions resulting from a disaster event. A stochastic, mixed integer program is proposed for quantifying network resilience and identifying the optimal post-event course of action to take. A solution technique based on concepts of Benders decomposition, column generation and Monte Carlo simulation is proposed. Experiments were conducted to illustrate the resilience concept and procedure for its measurement, and to assess the role of network topology in its magnitude. The last problem addressed is the urban search and rescue team deployment problem (USAR-TDP). The USAR-TDP seeks an optimal deployment of USAR teams to disaster sites, including the order of site visits, with the ultimate goal of maximizing the expected number of saved lives over the search and rescue period. A multistage stochastic program is proposed to capture problem uncertainty and dynamics. The solution technique involves the solution of a sequence of interrelated two-stage stochastic programs with recourse. A column generation-based technique is proposed for the solution of each problem instance arising as the start of each decision epoch over a time horizon. Numerical experiments conducted on an example of the 2010 Haiti earthquake are presented to illustrate the effectiveness of the proposed approach

Softwarization of Large-Scale IoT-based Disasters Management Systems

The Internet of Things (IoT) enables objects to interact and cooperate with each other for reaching common objectives. It is very useful in large-scale disaster management systems where humans are likely to fail when they attempt to perform search and rescue operations in high-risk sites. IoT can indeed play a critical role in all phases of large-scale disasters (i.e. preparedness, relief, and recovery). Network softwarization aims at designing, architecting, deploying, and managing network components primarily based on software programmability properties. It relies on key technologies, such as cloud computing, Network Functions Virtualization (NFV), and Software Defined Networking (SDN). The key benefits are agility and cost efficiency. This thesis proposes softwarization approaches to tackle the key challenges related to large-scale IoT based disaster management systems. A first challenge faced by large-scale IoT disaster management systems is the dynamic formation of an optimal coalition of IoT devices for the tasks at hand. Meeting this challenge is critical for cost efficiency. A second challenge is an interoperability. IoT environments remain highly heterogeneous. However, the IoT devices need to interact. Yet another challenge is Quality of Service (QoS). Disaster management applications are known to be very QoS sensitive, especially when it comes to delay. To tackle the first challenge, we propose a cloud-based architecture that enables the formation of efficient coalitions of IoT devices for search and rescue tasks. The proposed architecture enables the publication and discovery of IoT devices belonging to different cloud providers. It also comes with a coalition formation algorithm. For the second challenge, we propose an NFV and SDN based - architecture for on-the-fly IoT gateway provisioning. The gateway functions are provisioned as Virtual Network Functions (VNFs) that are chained on-the-fly in the IoT domain using SDN. When it comes to the third challenge, we rely on fog computing to meet the QoS and propose algorithms that provision IoT applications components in hybrid NFV based - cloud/fogs. Both stationary and mobile fog nodes are considered. In the case of mobile fog nodes, a Tabu Search-based heuristic is proposed. It finds a near-optimal solution and we numerically show that it is faster than the Integer Linear Programming (ILP) solution by several orders of magnitude

Mathematical Methods and Operation Research in Logistics, Project Planning, and Scheduling

In the last decade, the Industrial Revolution 4.0 brought flexible supply chains and flexible design projects to the forefront. Nevertheless, the recent pandemic, the accompanying economic problems, and the resulting supply problems have further increased the role of logistics and supply chains. Therefore, planning and scheduling procedures that can respond flexibly to changed circumstances have become more valuable both in logistics and projects. There are already several competing criteria of project and logistic process planning and scheduling that need to be reconciled. At the same time, the COVID-19 pandemic has shown that even more emphasis needs to be placed on taking potential risks into account. Flexibility and resilience are emphasized in all decision-making processes, including the scheduling of logistic processes, activities, and projects

AGENT-BASED DISCRETE EVENT SIMULATION MODELING AND EVOLUTIONARY REAL-TIME DECISION MAKING FOR LARGE-SCALE SYSTEMS

Computer simulations are routines programmed to imitate detailed system operations. They are utilized to evaluate system performance and/or predict future behaviors under certain settings. In complex cases where system operations cannot be formulated explicitly by analytical models, simulations become the dominant mode of analysis as they can model systems without relying on unrealistic or limiting assumptions and represent actual systems more faithfully. Two main streams exist in current simulation research and practice: discrete event simulation and agent-based simulation. This dissertation facilitates the marriage of the two. By integrating the agent-based modeling concepts into the discrete event simulation framework, we can take advantage of and eliminate the disadvantages of both methods.Although simulation can represent complex systems realistically, it is a descriptive tool without the capability of making decisions. However, it can be complemented by incorporating optimization routines. The most challenging problem is that large-scale simulation models normally take a considerable amount of computer time to execute so that the number of solution evaluations needed by most optimization algorithms is not feasible within a reasonable time frame. This research develops a highly efficient evolutionary simulation-based decision making procedure which can be applied in real-time management situations. It basically divides the entire process time horizon into a series of small time intervals and operates simulation optimization algorithms for those small intervals separately and iteratively. This method improves computational tractability by decomposing long simulation runs; it also enhances system dynamics by incorporating changing information/data as the event unfolds. With respect to simulation optimization, this procedure solves efficient analytical models which can approximate the simulation and guide the search procedure to approach near optimality quickly.The methods of agent-based discrete event simulation modeling and evolutionary simulation-based decision making developed in this dissertation are implemented to solve a set of disaster response planning problems. This research also investigates a unique approach to validating low-probability, high-impact simulation systems based on a concrete example problem. The experimental results demonstrate the feasibility and effectiveness of our model compared to other existing systems

Disaster management and its economic implications

Das Ziel dieser Arbeit ist es, aktuelle Forschungsschwerpunkte im Bereich des Katastrophenmanagements in der Operational Research Literatur aufzuzeigen. Katastrophenmanagement umfasst in diesem Zusammenhang einerseits Naturkatastrophen wie geophysikalische und hydro-meteorologische Katastrophen, technologische Katastrophen wie industrielle Unfälle, Transportunfälle und sonstige Unfälle, und andererseits die verschiedenen Formen des Terrorismus, allgemeinen Terrorismus sowie Bioterrorismus. Da die Anzahl und das Ausmaß von Katastrophen immer weiter zunehmen ist auch eine immer größere Notwendigkeit für die Entwicklung, den Einsatz und die wirtschaftliche Beurteilung der jeweiligen Strategien gegeben. Der erste Teil dieser Arbeit gibt einen Überblick über die Literatur im Bereich des Katastrophenmanagements und umfasst Simulation, Katastrophenmanagement in Krankenhäusern und die Rolle von Versicherungen im Katastrophenmanagementprozess. Im zweiten Teil wird eine Taxonomie entwickelt, deren Kategorien auf den Modellen und Ergebnissen der Literatur beruhen. Einerseits werden allgemeine Modelleigenschaften wie die Ebene im Katastrophenmanagementprozess, der Modelltyp und die Anwendungsgebiete der Modelle untersucht. Andererseits stellen die Art der Intervention und die Anwendbarkeit für die unterschiedlichen Katastrophenklassen weitere Kategorien der Taxonomie dar. Es wurden 90 Artikel, die beispielhaft für die Forschungsrichtungen im Bereich des Katastrophenmanagements der letzten 25 Jahre stehen, ausgewählt, und entsprechend den jeweiligen Kategorien der Taxonomie zugeordnet. Das Hauptaugenmerk der Taxonomie liegt auf der wirtschaftlichen Analyse, die wirksamkeitsbezogene, ressourcenbezogene und kostenbezogene Parameter umfasst. Es wird gezeigt ob und welche wirtschaftliche Analyse wie beispielsweise die Kosten-Nutzwert- Analyse, die Kosten-Wirksamkeits-Analyse und die Kosten-Nutzen-Analyse angewendet wird um die in den Artikeln beschriebenen Interventionen zu evaluieren. Es wird gezeigt, dass erhebliche Verbesserungen für die verschiedenen Katastrophentypen und in den verschiedenen Situationen erzielt werden können. Eingeschränkte Datenverfügbarkeit schränkt in vielen Fällen die Einsetzbarkeit der Modelle in realen Situationen ein. Im Allgemeinen ist erkennbar, dass Kooperation und Koordination zwischen den beteiligten Einheiten ausschlaggebend für den zeitgerechten und effizienten Einsatz der knappen Ressourcen sind. Oftmals erzielt der gemeinsame Einsatz mehrerer Maßnahme ein deutlich besseres Ergebnis als der Einsatz von lediglich einem einzigen Instrument. Die Taxonomie unterstreicht dass trotz der großen Fülle an Literatur im Bereich des Katastrophenmanagements nur wenige Autoren auf die Kosten-Nutzwert-Analyse, die Kosten-Wirksamkeits-Analyse und die Kosten-Nutzen-Analyse als Hilfsmittel zur wirtschaftlichen Analyse zurückgreifen. In Zukunft, um Interventionen erfolgreich evaluieren zu können oder die beste aus mehreren Interventionen bestimmen zu können wird es immer wichtiger werden, diese Art von wirtschaftlichen Analysen anzuwenden.This thesis intends to demonstrate current research directions in the field of disaster management in the Operational Research literature. Disaster management in this context comprises the management of natural, such as geophysical and hydro-meteorological, and technological disasters, such as industrial accidents, transportation accidents, and miscellaneous accidents, as well as the management of the different terrorism forms, general terrorism and bioterrorism. As the occurrence of disasters is getting more and more frequent and the accumulated loss of these events is getting higher and higher, there is a strong need for the development, implication and economic evaluation of strategies to counter these disasters. In the first part of the thesis, a general overview of the literature is given, including a focus on simulation, disaster management in hospitals, and the role of insurances in the disaster management process. The second part encompasses the taxonomy which focuses on models and outcomes presented in the literature. As a result of the review of the literature, appropriate categories for the disaster management taxonomy are derived. On the one hand, an overview of general model features, i.e., the level of disaster management, model type and methods of application is given. On the other hand, the type of intervention used and the practicability for different disaster types are discussed. 90 papers, illustrative main examples of the research directions of the last 25 years, were selected for deeper investigation and classified according to the main criteria analyzed in the articles. The main focus of the taxonomy lies on the economic analysis, which encompasses effectiveness-related, resource-related, and cost-related parameters and shows the type of economic analysis used in the literature. We analyze whether economic analysis, i.e., costutility, cost-effectiveness, and cost-benefit are used to investigate different interventions and what type of analysis has been chosen by the authors. Policy implications and results show that considerable improvements can be achieved for different disastrous events and in different situations. Limited data availability constrains the outcomes of the models and their applicability to real-world situations. In general, cooperation and coordination of the entities involved are crucial to guarantee timely and efficient assignment of scarce resources. Furthermore, different authors confirm that a combination of various measures often achieves a better outcome than if tools are used autonomously. The taxonomy has underlined that although there exists a vast disaster management literature dealing with various problems related to mitigation, preparedness, response and recovery from disasters, there are only a few authors evaluating the actions taken through economic analyses such cost-utility, cost-effectiveness, or cost-benefit analysis. In the future, to be able to evaluate interventions, or to figure out the most effective intervention among several interventions, it is crucial to stronger rely on the abovementioned economic analyses