An Integrated Assessment Framework for Water Resources Management: A DSS Tool and a Pilot Study Application

Decision making for the management of water resources is a complex and difficult task. This is due to the complex socio-economic system that involves a large number of interest groups pursuing multiple and conflicting objectives, within an often intricate legislative framework. Several Decision Support Systems have been developed but very few have indeed proved to be effective and truly operational. MULINO (Multisectoral, Integrated and Operational Decision Support System for Sustainable Use of Water Resources at the Catchment Scale) is a project funded under the Fifth Framework Programme of the European Research and the key action line dedicated to operational management schemes and decision support system for sustainable use of water resources. The MULINO DSS (mDSS) integrates hydrological models with multi-criteria decision methods and adopts the DPSIR (Driving Force – Pressure – State – Impact – Response) framework developed by the European Environment Agency. The DPSIR was converted from a static reporting scheme into a dynamic framework for integrated assessment modelling (IAM) and multi-criteria evaluation procedures. This paper presents the methodological framework and the intermediate results of the mDSS tool through its application in a pilot study area located in the Watershed of the Lagoon of Venice.Integrated water resources management, Spatial decision-making, Decision support system, Catchment, Environmental modelling

Decision Engine for SIP Based Dynamic Call Routing

Abstract. Enterprises nowadays are subscribing access to several Internet Service Providers (ISPs) for reliability, redundancy and better revenues underlying the service extension, while providing good Quality of Service (QoS). In this paper, a dynamic decision-making framework is presented for Session Initiation Protocol (SIP) based voice/video call routing in multihomed network. The decision engine takes multiple criteria into account while computing the routing decision (attributes from context of the request, platform's latest conditional parameters, business objectives of the company, etc.). Two Multi-Criteria Decision Making (MCDM) methods, namely Grey Relational Analysis (GRA) and an extended version of Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) are used for decision calculation in outsourcing and provisioning enforcement modes respectively. The proposed solution gives higher throughput and lower call dropping probability while fulfilling the desired goals, taking into account the multiple attributes for choosing the best alternative

Collaborative dynamic decision making: a case study from B2B supplier selection

The problem of supplier selection can be easily modeled as a multiple-criteria decision making (MCDM) problem: businesses express their preferences with respect to suppliers, which can then be ranked and selected. This approach has two major pitfalls: first, it does not consider a dynamic scenario, in which suppliers and their ratings are constantly changing; second, it only addressed the problem from the point of view of a single business, and cannot be easily applied when considering more than one business. To overcome these problems, we introduce a method for supplier selection that builds upon the dynamic MCDM framework of Campanella and Ribeiro [1] and, by means of a linear programming model, can be used in the case of multiple collaborating businesses plan- ning their next batch of orders together.Fundação para a Ciência e a Tecnologia, Portugal, under contract CONT DOUT/49/UNINOVA/0/5902/1/200

Decision making study: methods and applications of evidential reasoning and judgment analysis

Decision making study has been the multi-disciplinary research involving operations researchers, management scientists, statisticians, mathematical psychologists and economists as well as others. This study aims to investigate the theory and methodology of decision making research and apply them to different contexts in real cases. The study has reviewed the literature of Multiple Criteria Decision Making (MCDM), Evidential Reasoning (ER) approach, Naturalistic Decision Making (NDM) movement, Social Judgment Theory (SJT), and Adaptive Toolbox (AT) program. On the basis of these literatures, two methods, Evidence-based Trade-Off (EBTO) and Judgment Analysis with Heuristic Modelling (JA-HM), have been proposed and developed to accomplish decision making problems under different conditions. In the EBTO method, we propose a novel framework to aid people s decision making under uncertainty and imprecise goal. Under the framework, the imprecise goal is objectively modelled through an analytical structure, and is independent of the task requirement; the task requirement is specified by the trade-off strategy among criteria of the analytical structure through an importance weighting process, and is subject to the requirement change of a particular decision making task; the evidence available, that could contribute to the evaluation of general performance of the decision alternatives, are formulated with belief structures which are capable of capturing various format of uncertainties that arise from the absence of data, incomplete information and subjective judgments. The EBTO method was further applied in a case study of Soldier system decision making. The application has demonstrated that EBTO, as a tool, is able to provide a holistic analysis regarding the requirements of Soldier missions, the physical conditions of Soldiers, and the capability of their equipment and weapon systems, which is critical in domain. By drawing the cross-disciplinary literature from NDM and AT, the JA-HM extended the traditional Judgment Analysis (JA) method, through a number of novel methodological procedures, to account for the unique features of decision making tasks under extreme time pressure and dynamic shifting situations. These novel methodological procedures include, the notion of decision point to deconstruct the dynamic shifting situations in a way that decision problem could be identified and formulated; the classification of routine and non-routine problems, and associated data alignment process to enable meaningful decision data analysis across different decision makers (DMs); the notion of composite cue to account for the DMs iterative process of information perception and comprehension in dynamic task environment; the application of computational models of heuristics to account for the time constraints and process dynamics of DMs decision making process; and the application of cross-validation process to enable the methodological principle of competitive testing of decision models. The JA-HM was further applied in a case study of fire emergency decision making. The application has been the first behavioural test of the validity of the computational models of heuristics, in predicting the DMs decision making during fire emergency response. It has also been the first behavioural test of the validity of the non-compensatory heuristics in predicting the DMs decisions on ranking task. The findings identified extend the literature of AT and NDM, and have implications for the fire emergency decision making

Analysis and operational challenges of dynamic ride sharing demand responsive transportation models

There is a wide body of evidence that suggests sustainable mobility is not only a technological question, but that automotive technology will be a part of the solution in becoming a necessary albeit insufficient condition. Sufficiency is emerging as a paradigm shift from car ownership to vehicle usage, which is a consequence of socio-economic changes. Information and Communication Technologies (ICT) now make it possible for a user to access a mobility service to go anywhere at any time. Among the many emerging mobility services, Multiple Passenger Ridesharing and its variants look the most promising. However, challenges arise in implementing these systems while accounting specifically for time dependencies and time windows that reflect users’ needs, specifically in terms of real-time fleet dispatching and dynamic route calculation. On the other hand, we must consider the feasibility and impact analysis of the many factors influencing the behavior of the system – as, for example, service demand, the size of the service fleet, the capacity of the shared vehicles and whether the time window requirements are soft or tight. This paper analyzes - a Decision Support System that computes solutions with ad hoc heuristics applied to variants of Pick Up and Delivery Problems with Time Windows, as well as to Feasibility and Profitability criteria rooted in Dynamic Insertion Heuristics. To evaluate the applications, a Simulation Framework is proposed. It is based on a microscopic simulation model that emulates real-time traffic conditions and a real traffic information system. It also interacts with the Decision Support System by feeding it with the required data for making decisions in the simulation that emulate the behavior of the shared fleet. The proposed simulation framework has been implemented in a model of Barcelona’s Central Business District. The obtained results prove the potential feasibility of the mobility concept.Postprint (published version

The adequacy of institutional frameworks and practice for climate change adaptation decision making

The ability of decision makers to respond to climate change impacts such as sea-level rise and increased flood frequency is challenged by uncertainty about scale, timing, dynamic changes that could lead to regime shifts, and by societal changes. Climate change adaptation decision making needs to be robust and flexible across a range of possible futures, to provide sufficient certainty for investment decisions in the present, without creating undue risks and liabilities for the near and long-term futures. A country’s governance and regulatory institutions set parameters for such decisions. The decision-making challenge is, therefore, a function of the uncertainty and dynamic characteristics of climate change, a country’s institutional framework, and the ways in which actual decision-making practice delivers on the intention of the framework. My research asks if the current decision-making framework, at national and sub-national scales, and practices under it are adequate to enable decision makers to make climate change adaptation decisions that sufficiently address the constraints posed by climate change uncertainty and dynamic change. The focus is on New Zealand’s multi-scale governance and institutional framework with its high level of devolution to the local level, the level assumed as the most appropriate for climate change adaptation decisions. Empirical information was collected from a sample of agencies and actors, at multiple governance scales reflecting the range of geographical characteristics, governance types, organisational functions and actor disciplines. Data were collected using a mix of workshops, interviews and document analyses. The adequacy of the institutional framework and practice was examined using 12 criteria derived from the risk-based concepts of precaution, risk management, adaptive management and transformational change, with respect to; a) understanding and representing uncertainty and dynamic climate change; b) governance and regulations; and c) organisations and actors. The research found that the current decision-making framework has many elements that could, in principle, address uncertainty and dynamic climate change. It enables long-term considerations and emphasises precaution and risk-based decision making. However, adaptive and transformational objectives are largely absent, coordination across multiple levels of government is constrained and timeframes are inconsistent across statutes. Practice shows that climate risk has been entrenched by misrepresentation of climate change characteristics. The resulting ambiguity is compounded at different governance scales, by gaps in the use of national and regional instruments and consequent differences in judicial decisions. Practitioners rely heavily upon static, time-bound treatments of risk, which reinforce unrealistic community expectations of ongoing protections, even as the climate continues to change, and makes it difficult to introduce transformational measures. Some efforts to reflect changing risk were observed but are, at best, transitional measures. Some experimentation was found in local government practice and boundary organisations were used as change-agents. Any potential improvements to both the institutional framework and to practices that could enable flexible and robust adaptation to climate change, would require supporting policies and adaptive governance to leverage them and to sustain decision making through time. This thesis contributes to understanding how uncertainty and dynamic climate change characteristics matter for adaptation decision making by examining both a country-level institutional framework and practice under it. The adequacy analysis offers a new way of identifying institutional barriers, enablers and entry points for change in the context of decision making under conditions of uncertainty and dynamic climate change

A Hybrid Modelling Framework for Real-time Decision-support for Urgent and Emergency Healthcare

In healthcare, opportunities to use real-time data to support quick and effective decision-making are expanding rapidly, as data increases in volume, velocity and variety. In parallel, the need for short-term decision-support to improve system resilience is increasingly relevant, with the recent COVID-19 crisis underlining the pressure that our healthcare services are under to deliver safe, effective, quality care in the face of rapidly-shifting parameters. A real-time hybrid model (HM) which combines real-time data, predictions, and simulation, has the potential to support short-term decision-making in healthcare. Considering decision-making as a consequence of situation awareness focuses the HM on what information is needed where, when, how, and by whom with a view toward sustained implementation. However the articulation between real-time decision-support tools and a sociotechnical approach to their development and implementation is currently lacking in the literature. Having identified the need for a conceptual framework to support the development of real-time HMs for short-term decision-support, this research proposed and tested the Integrated Hybrid Analytics Framework (IHAF) through an examination of the stages of a Design Science methodology and insights from the literature examining decision-making in dynamic, sociotechnical systems, data analytics, and simulation. Informed by IHAF, a HM was developed using real-time Emergency Department data, time-series forecasting, and discrete-event simulation. The application started with patient questionnaires to support problem definition and to act as a formative evaluation, and was subsequently evaluated using staff interviews. Evaluation of the application found multiple examples where the objectives of people or sub-systems are not aligned, resulting in inefficiencies and other quality problems, which are characteristic of complex adaptive sociotechnical systems. Synthesis of the literature, the formative evaluation, and the final evaluation found significant themes which can act as antecedents or evaluation criteria for future real-time HM studies in sociotechnical systems, in particular in healthcare. The generic utility of IHAF is emphasised for supporting future applications in similar domains

Addressing real-time control problems in complex environments using dynamic multi-objective evolutionary approaches

The demand for increased automation of industrial processes generates control problems that are dynamic, multi-objective and noisy at the same time. The primary hypothesis underlying this research is that dynamic evolutionary methods could be used to address dynamic control problems where con icting control criteria are necessary. The aim of this research is to develop a framework for on-line optimisation of dynamic problems that is capable of a) representing problems in a quantitative way, b) identifying optimal solutions using multi-objective evolutionary algorithms, and c) automatically selecting an optimal solution among alternatives. A literature review identi es key problems in the area of dynamic multi-objective optimisation, discusses the on-line decision making aspect, analyses existing Multi- Objective Evolutionary Algorithms (MOEA) applications and identi es research gap. Dynamic evolutionary multi-objective search and on-line a posteriori decision maker are integrated into an evolutionary multi-objective controller that uses an internal process model to evaluate the tness of solutions. Using a benchmark multi-objective optimisation problem, the MOEA ability to track the moving optima is examined with di erent parameter values, namely, length of pre-execution, frequency of change, length of prediction interval and static mutation rate. A dynamic MOEA with restricted elitism is suggested for noisy environments.To address the on-line decision making aspect of the dynamic multi-objective optimisation, a novel method for constructing game trees for real-valued multiobjective problems is presented. A novel decision making algorithm based on game trees is proposed along with a baseline random decision maker. The proposed evolutionary multi-objective controller is systematically analysed using an inverted pendulum problem and its performance is compared to Proportional{ Integral{Derivative (PID) and nonlinear Model Predictive Control (MPC) approaches. Finally, the proposed control approach is integrated into a multi-agent framework for coordinated control of multiple entities and validated using a case study of a tra c scheduling problem.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Decision Support for Dynamic Barrier Management for Offshore Operations

PresentationEffective safety barrier management is a fundamental principle for prevention and mitigation of major accidents in offshore drilling and production operations. Barrier management methods such as bow tie diagrams are commonly used for identifying safety barriers in the development of safety case documentation and the performance of major accident risk assessments. In addition to such applications for establishing design baselines for offshore installations, some organizations are taking safety barrier management into the operational regime by establishing measures for assessing barrier health and assigning barrier owners to ensure that barriers are continuously maintained. The next step in effective safety barrier management is to develop and implement methods to continuously monitor barriers in real time and provide decision guidance for operations, maintenance, and management personnel regarding actions to be taken when barriers are degraded or failed. A systematic approach has been developed by DNV GL for identifying information requirements for dynamic barrier management, instrumentation or other sources of data for providing that information, decision criteria for determining when barriers are degraded or failed, and guidance for actions to be taken to restore degraded barriers and to prevent major accidents and mitigate their consequences. The resulting information framework can be used to support communication, consensus, decision making and action across technical disciplines and organizational boundaries. This paper summarizes the approach for the development of decision support tools for dynamic barrier management, and insights gained from application of the approach to offshore production and drilling operations with multiple industry partners. In addition, the paper summarizes industry research and development activities that are needed for effective implementation of dynamic barrier management in the offshore oil and gas industry