EURO Journal on Decision Processes最新文献

Regulatory health, safety, and environment (HSE) interventions have an impact on both costs and benefits for the industry. It is common for the regulators to evaluate such interventions by providing a comparison of costs and benefits as a basis for decision-making. Fulfilling an assignment for the Norwegian government, two consulting companies proposed a methodology for regulatory evaluation in the petroleum industry. This methodology acknowledges that uncertainty must have a higher weight than given through traditional cost–benefit analyses, but it is still to a great extent based on the use of expected values. We question this use of modified cost–benefit analyses for providing decision support in contexts where uncertainty is the dominating attribute. Furthermore, we argue that the decision-makers should be able to take a dynamic approach, where the chosen method should fit its context. As an example, we present a framework in line with such a dynamic approach. The article is an extended version of an ESREL conference article.

Reaching a decision when multiple, possibly conflicting, criteria are taken into account is often a difficult task. This normally requires the intervention of an analyst to aid the decision maker in following a clear methodology with respect to the steps that need to be taken, as well as the use of different algorithms and software tools. Most of these tools focus on one or a small number of algorithms, some are difficult to adapt and interface with other tools, while only a few belong to dynamic communities of contributors allowing them to expand in use and functionality. In this paper, we address these issues by proposing to use the R statistical environment and the MCDA package of decision aiding algorithms and tools. This package is meant to provide a wide range of MCDA algorithms that may be used by an analyst to tailor a decision aiding process to their needs, while the choice of R takes advantage of the yet poorly explored opportunity to interface data analysis and decision aiding. We additionally demonstrate the use of this tool on a practical application following a well-defined decision aiding process.

The risk caused by DP vessels in offshore marine operations is not negligible, due to wide applications of DP vessels in complex marine operations, and the sharp increase of DP vessel population. The DP accidents/incidents on the Norwegian Continental Shelf (NCS) that have occurred after 2000 indicate a need for improving safety of DP operations, which calls for new risk reduction measures. The focus of this paper is particularly on the offshore loading operations with DP shuttle tanker in offloading from floating production storage and offloading (FPSO) vessels on the NCS, but the results may be relevant also for other types of DP vessels in offshore oil and gas operations. In the paper, Man, Technology and Organization (MTO) analysis is applied to investigate the cause and barrier failures of nine reported accidents/incidents occurring over a 16-year period (2000–2015). MTO is based on three methods, including structured analysis by use of an event- and cause-diagram, change analysis by describing how events have deviated from earlier events or common practice, and barrier analysis by identifying technological and administrative barriers which have failed or are missing. The results are categorized into technical failures, human failures, organizational failures, as well as a combination of failures. The main finding is that the majority of the accidents are caused by the combination of technical, human and organizational failures. Critical root causes, results of change analysis and barrier analysis, and combination of failures are focused in the discussion. Recommendations of potential safety improvements are made on the aspects of the assessment of the actual system function, barrier management for marine systems, risk information to support different decision-makings, and the development of an on-line risk monitoring and decision supporting system.

Many important decision and risk analysis problems are complicated by dependencies between input variables. In such cases, standard one-variable-at-a-time sensitivity analysis methods are typically eschewed in favor of fully probabilistic, or n-way, analysis techniques which simultaneously vary all n input variables and capture their interdependencies. Unfortunately, much of the intuition provided by one-way sensitivity analysis may not be available in fully probabilistic methods because it is difficult or impossible to isolate the marginal effects of the individual variables. In this paper, we present a dependence-adjusted approach for identifying and analyzing the impact of the input variables in a model through the use of probabilistic sensitivity analysis based on copulas. This approach provides insights about the influence of the input variables and the dependence relationships between the input variables. One contribution of this approach is that it facilitates assessment of the relative marginal influence of variables for the purpose of determining which variables should be modeled in applications where computational efficiency is a concern, such as in decision tree analysis of large-scale problems. In addition, we also investigate the sensitivity of a model to the magnitude of correlations in the inputs.

The present work is devoted to the problem of city recovery management after a large scale disaster. A modern city is represented as a complex urban socio-technical system consisting of three interdependent parts: a physical lifeline system, citizens’ daily life demand, and service systems. The functioning of the last two ones directly depends on the connectivity of the physical lifelines. In cases when all the consequences of a large scale disaster are known and the system damage can be evaluated withing high accuracy researches in the field of recovery of socio-technical systems mainly use different optimization methods of recovery plan generation using various objective functions. However, the recovery of urban socio-technical system in the case of non-reliable information requires another approach. At the initial time moment collecting the information about the system state can be hindered by many causes and possible cascading failures in the infrastructure system lead to the reassessment of the city damage. The applicability of an optimization technique to the recovery management in such cases is rather troubled. The process of recovery planning should be very adaptive to such changing conditions. We developed a method of recovery management based on the reactive decision-making. It uses the step-by-step realization logic which can be applied to the recovery management under significant information uncertainties. In the present paper, we summarize the basic logic of the novel method, results of numerical simulation that demonstrate its applicability to the recovery management in cases of initial lack of information. Besides, the comparison of this approach with the result obtained by the optimization technique demonstrates the quasi-optimality of the reactive decision-making based approach of the city recovery planning.

Organizations exercise to strengthen their crisis management capability, to identify possible improvements to plans, and to develop necessary skills. Taking part in exercises is one way leaders can test their decision-making abilities under emergencies and crises. Crisis management exercises provide arenas for learning and knowledge sharing. This paper discusses how exercises can be performed better and more efficiently. We draw upon practical experiences from 12 table top and functional exercises, involving a variety of military and civilian actors, and we argue that it is possible to improve the planning and conduct of many exercises, leading to more relevant results and greater benefits for the participants. We want to demonstrate that exercises are important tools for decision-making and strategic planning processes within units or organizations. The target audience for the paper is anyone involved in crisis management exercises, from strategic planners and decision-makers to practitioners. The paper identifies practical recommendations for successful crisis management exercises, both discussion-based and operations-based. Learning points are extracted and analyzed with the use of problem structuring methods, resulting in a list of success criteria for crisis management exercises with examples of what works well and what does not.

The efficient use of resources in health systems is important due to the increasing demand and limited funding. Large health systems often have fixed input resources (such as budget and staffing) to be allocated among individual hospitals/clinics with particular target output levels. We propose an optimization model with fuzzy constraints that can be used for automatic resource re-allocation with respect to different levels of risk preferences. We illustrate its applicability using data from a U.S. Army hospital network. The implications of the proposed fuzzy decision-making model for healthcare decision makers and its relevance to healthcare policy and management are also discussed.

Financial incentives offered by insurers to health care providers have been identified as a key mechanism to lower costs while improving quality of care. How to effectively design incentive programs that can align the varying objectives of health care stakeholders, as well as predict program performance and stakeholders’ decision response is an unresolved research challenge. The objective of this paper was to establish the foundation for a novel approach based on multiscale decision theory (MSDT) that can effectively model and efficiently analyze such incentive programs, and the complex health care system in general. The MSDT model captures the interdependencies of stakeholders, their decision processes, uncertainties, and how incentives impact decisions and outcomes at the payer, hospital, physician and patient level. We illustrate the modeling approach by applying it to a specific incentive program, the Medicare Shared Savings Program (MSSP) for Accountable Care Organizations (ACOs), which was introduced by the Centers for Medicare and Medicaid Services (CMS) in the United States in 2012. We focus our analysis on computed tomography (CT) use by physicians, and CT scanner investment decisions by hospitals. We determine the conditions under which the incentive program leads to the desired outcomes of cost reduction and quality of care improvements. The results have policy and managerial implications for CMS, ACOs and their members, specifically hospitals and physicians.

Smoking is the single biggest cause of preventable death in the Uited Kingdom (UK) and is a major cause of coronary heart disease, some cancers, and respiratory disease, including chronic obstructive pulmonary disease. At the time of initiating the project, smoking prevalence had not changed across four local government areas in South Yorkshire for some years. Most spending had been focussed on helping people quit, an intervention where there was clear evidence of effectiveness. A number of changes occurred in public health structures and targets, requiring a reappraisal of the range of interventions offered. This was challenging due to a lack of clear evidence for some of the areas’ alternative interventions. The aim of this paper is to describe the use of a multi-criteria decision analysis (MCDA) approach to support the health priority setting in local authorities to reduce smoking prevalence. There were three phases to this process: (1) problem structuring; (2) the multiple criteria decision analysis; (3) and using the MCDA results to influence decision making at the local government level. The MCDA approach was used to collate information in a consistent and transparent manner, using expert, stakeholder and public opinion to fill known gaps in evidence. Fifteen interventions (such as stop smoking support services, smoke-free spaces, communication and marketing exercises, and increased investment in enforcement) were ranked across eight criteria (relating to reductions in prevalence across relevant groups, as well as aspects relating to equity and feasibility), allowing a range of relevant concerns to be incorporated. Subsequent steps were taken to translate the results of this stage into workable policy options. The results differed significantly from current practice. Sensitivity analysis showed that the findings were robust to changes in preference weights. These results informed subsequent changes to the interventions offered across the four boroughs. The ability of MCDA techniques to incorporate data and both qualitative and quantitative judgements in a formal manner mean that they are well suited to support public health decision making, where evidence is often only partially available and many policies are value driven. MCDA methods, if used, should be chosen carefully based on their resource/time constraints, scientific validity, and the significance and broader context of the decision problem.