Scenario-based Methods Research Articles

The scenario-based generalization of radiation therapy margins

We give a scenario-based treatment plan optimization formulation that is equivalent to planning with geometric margins if the scenario doses are calculated using the static dose cloud approximation. If the scenario doses are instead calculated more accurately, then our formulation provides a novel robust planning method that overcomes many of the difficulties associated with previous scenario-based robust planning methods. In particular, our method protects only against uncertainties that can occur in practice, it gives a sharp dose fall-off outside high dose regions, and it avoids underdosage of the target in ‘easy’ scenarios. The method shares the benefits of the previous scenario-based robust planning methods over geometric margins for applications where the static dose cloud approximation is inaccurate, such as irradiation with few fields and irradiation with ion beams. These properties are demonstrated on a suite of phantom cases planned for treatment with scanned proton beams subject to systematic setup uncertainty.

Stochastic network constrained hydro-thermal scheduling using a linearized progressive hedging algorithm

A method for solving the stochastic network constrained hydro-thermal scheduling problem based on the progressive hedging algorithm (PHA) is presented. The quadratic penalty terms in the PHA are dynamically linearized to retain a linear problem structure. Furthermore, a technique for efficient treatment of linear power flow constraints in scenario-based decomposition methods is proposed and tested. The method is applied to a case study, emphasizing on computational performance. It is demonstrated how the PHA convergence properties can be compared with those of the multi-stage Benders decomposition method by using bounds.

Combining Climate Scenarios and Risk Management Approach—A Finnish Case Study

Climate change impacts on nature and the environment have been widely discussed and studied. Traditionally, a company’s continuity management is based on risk analysis. There are also attempts to implement scenario-based methods in the risk management procedures of companies. For industrial decision makers, it is vital to acknowledge the impacts of climate change with regards to their adaptation strategies. However, a scenario-based approach is not always the most effective way to analyze these risks. This paper investigates the integration of scenario and risk-based methods for a company’s adaptation planning. It considers the uncertainties of the climate change scenarios and the recognized risks as well as suitable adaptation strategies. The paper presents the results of climate risk analysis prepared for two Finnish hydropower plants. The introduced method was first piloted in 2008 and then again in 2015. The update of the analysis pointed out that at the company level, the climate risks and other risks originating from governmental or political decisions form an intertwined wholeness where the origin of the risk is difficult to outline. It seems that, from the business point of view, the main adaptation strategies suggested by the integrated risk and scenarios approach are those that support buying “safety margins” in new investments and reducing decision time horizons. Both of these adaptation strategies provide an advantage in the circumstances where also political decisions and societal changes have a great effect on decision making.

Supply Chain Vulnerability Analysis Using Scenario-Based Input-Output Modeling: Application to Port Operations.

Disruptive events such as natural disasters, loss or reduction of resources, work stoppages, and emergent conditions have potential to propagate economic losses across trade networks. In particular, disruptions to the operation of container port activity can be detrimental for international trade and commerce. Risk assessment should anticipate the impact of port operation disruptions with consideration of how priorities change due to uncertain scenarios and guide investments that are effective and feasible for implementation. Priorities for protective measures and continuity of operations planning must consider the economic impact of such disruptions across a variety of scenarios. This article introduces new performance metrics to characterize resiliency in interdependency modeling and also integrates scenario-based methods to measure economic sensitivity to sudden-onset disruptions. The methods will be demonstrated on a U.S. port responsible for handling $36.1 billion of cargo annually. The methods will be useful to port management, private industry supply chain planning, and transportation infrastructure management.

Combining sampling-based and scenario-based nested Benders decomposition methods: application to stochastic dual dynamic programming

Nested Benders decomposition is a widely used and accepted solution methodology for multi-stage stochastic linear programming problems. Motivated by large-scale applications in the context of hydro-thermal scheduling, in 1991, Pereira and Pinto introduced a sampling-based variant of the Benders decomposition method, known as stochastic dual dynamic programming (SDDP). In this paper, we embed the SDDP algorithm into the scenario tree framework, essentially combining the nested Benders decomposition method on trees with the sampling procedure of SDDP. This allows for the incorporation of different types of uncertainties in multi-stage stochastic optimization while still maintaining an efficient solution algorithm. We provide an illustration of the applicability of our method towards a least-cost hydro-thermal scheduling problem by examining an illustrative example combining both fuel cost with inflow uncertainty and by studying the Panama power system incorporating both electricity demand and inflow uncertainties.

Telecare and self-management: a guideline for anticipating future care in scenario-based design

An important aim of telecare technologies for chronic patients is supporting self-management. Although patient involvement is crucial for successful implementation, any adaptation of telecare systems to needs of users requires explicit reflection regarding which form of self-management it should support. Scenario-based methods (SBDs) are proposed to involve users in the earlier phases of development. This paper aims to extend SBDs by incorporating explicit exploration of self-management forms. We first analyzed what self-management forms were inscribed in the design of a telecare system for COPD patients. These were mainly based on compliance to medical treatment. However, our study shows that many patients thrive better on self-management based on cooperation and concordance with healthcare professionals. To overcome this discrepancy between design and use practices we developed a guideline enabling designers to anticipate and reflect on which form of self-management is desirable to incorporate in the design of telecare technologies for chronic patients.

Robust supply chain network design: an optimization model with real world application

This paper presents a robust optimization model for the design of a supply chain facing uncertainty in demand, supply capacity and major cost data including transportation and shortage cost parameters. We first present a base model that aims to determine the strategic ‘location’ and tactical ‘allocation’ decisions for a deterministic four-tier supply chain. The model is then extended to incorporate uncertainty in key input parameters using a robust optimization approach that can overcome the limitations of scenario-based solution methods in a tractable way, i.e. without excessive changes in complexity of the underlying base deterministic model. The application of the approach is investigated in an actual case study where real data is utilized to design a bread supply chain network. Numerical results obtained from model implementation and sensitivity analysis experiments arrive at important managerial insights and practical implications.

On the Road Between Robust Optimization and the Scenario Approach for Chance Constrained Optimization Problems

We propose a new method for solving chance constrained optimization problems that lies between robust optimization and scenario-based methods. Our method does not require prior knowledge of the underlying probability distribution as in robust optimization methods, nor is it based entirely on randomization as in the scenario approach. It instead involves solving a robust optimization problem with bounded uncertainty, where the uncertainty bounds are randomized and are computed using the scenario approach. To guarantee that the resulting robust problem is solvable we impose certain assumptions on the dependency of the constraint functions with respect to the uncertainty and show that tractability is ensured for a wide class of systems. Our results lead immediately to guidelines under which the proposed methodology or the scenario approach is preferable in terms of providing less conservative guarantees or reducing the computational cost.

Tailored Scenarios: A Low-Cost Online Method to Elicit Perceptions of Home Technologies Using Participant-Specific Contextual Information

The introduction of new ubiquitous computing technologies has the capacity to greatly affect the home, a space that is core to human experience, yet remains understudied owing to its sensitive, semi-private nature. Scenario-based methods have previously been used to collect home technology design responses but they have tended to over-simplify the social contexts involved, and have not taken into account the unique circumstances of the respondents. Technology probes have also been used but suffer from increased costs in research time and effort and a corresponding reduction in participation, as well as decreased acceptance owing to their invasiveness. We present 'Tailored Scenarios', a new method for eliciting participant responses involving home-based technologies that dynamically presents participants with scenarios, based on their home contexts, into which new technologies are installed. 'Tailored Scenarios' use self-reported contextual data to customize each scenario. We present the method, and report findings from a case study in which we compared responses to 'Tailored Scenarios' with those from traditional scenarios and technology probes. Our findings suggest that 'Tailored Scenarios' is an improvement over standard scenario methods in terms of eliciting attitudes towards technology and are easier to implement at scale than technology probes.

Morningness–Eveningness and Risk Taking

ABSTRACT. Depending on individual differences in biological rhythms and diurnal preferences, people have long been described as either “larks” or “owls.” Larks and owls differ greatly in personality aspects, but from the behavioral perspective it is unclear whether they have significant differences in terms of risky behaviors. Whether morning types or evening types are consistently more risk-taking or risk-averse in different domains remains unknown. This study adopted a general American adult sample to systematically investigate the relationship between chronotype and individuals’ risky behaviors in different domains. By using different methods to measure risky behaviors in different domains, the current research obtained convergent results that morningness was negatively related only to financially risky behaviors for American adults. More specifically, by using the composite scale of morningness and the domain-specific risk attitude scale, Study 1 showed that for American adults, morning types were less likely than evening types to engage in financially risky behaviors (N = 212). In Study 2, after scenario-based methods were used to measure risky behavior, results showed that that participants engaged less in risky behaviors in the domains of gambling and investment (N = 187). A mediator test showed that the negative relationship between morningness and financially risky behaviors was partly mediated by individuals’ self-control ability (self-control scale, Study 1).

Active shooter in the emergency department: A scenario-based training approach for healthcare workers

An active shooter in the emergency department (ED) presents a significant danger to employees, patients, and visitors. Very little education on this topic exists for healthcare workers. Using didactic and scenario-based training methods, the authors constructed a comprehensive training experience to better prepare healthcare workers for an active shooter. Thirty-two residents, nurses, and medical students participated in a disaster drill onboard a US military base. All were blinded to the scenarios. The study was approved by the institutional review board, and written consent was obtained from all participants. Each participant completed a 10-item pretest developed from the Department of Homeland Security's IS:907 Active Shooter course. Participants were exposed to a single active shooter scenario followed by a didactic lecture on hostage recovery and crisis negotiation. Participants were then exposed to a scenario involving multiple shooters. Many of the participants were held hostage for several hours. The training concluded with a post-test and debrief. Paired Student's t-test determined statistical significance between the pretest and post-test questionnaire scores. Paired Student's t-tests confirmed a statistically significant difference between the pretest and post-test scores for the subjects, as a whole (p < 0.002 [-0.177, -0.041]). There was no difference in scores for nurses (p = 1 [-1.779, 1.779]). The scores for resident physicians (p < 0.01 [-0.192, -0.032]) and medical students (p < 0.01 [-0.334, -0.044]) were found to be significant. Didactic lectures, combined with case-based scenarios, are an effective method to teach healthcare workers how to best manage an active shooter incident.

Participatory Design of an Integrated Information System Design to Support Public Health Nurses and Nurse Managers

The objectives of the study were to use persona-driven and scenario-based design methods to create a conceptual information system design to support public health nursing. We enrolled 19 participants from two local health departments to conduct an information needs assessment, create a conceptual design, and conduct a preliminary design validation. Interviews and thematic analysis were used to characterize information needs and solicit design recommendations from participants. Personas were constructed from participant background information, and scenario-based design was used to create a conceptual information system design. Two focus groups were conducted as a first iteration validation of information needs, personas, and scenarios. Eighty-nine information needs were identified. Two personas and 89 scenarios were created. Public health nurses and nurse managers confirmed the accuracy of information needs, personas, scenarios, and the perceived usefulness of proposed features of the conceptual design. Design artifacts were modified based on focus group results. Persona-driven design and scenario-based design are feasible methods to design for common work activities in different local health departments. Public health nurses and nurse managers should be engaged in the design of systems that support their work.

Exploring the impact of evaluating strategic options in a scenario-based multi-criteria framework

One of the least explored aspects of scenario planning is how to assess systematically the value and robustness of strategic options after scenario development. In this context, there is growing research interest on the use of multi-criteria decision analysis (MCDA) to evaluate such options, but with very limited evidence about its performance in practice. This paper examines effects of applying in practice one of those recently proposed scenario-based MCDA methods for identifying robust options. Three public sector decision-making instances in Trinidad and Tobago are examined within an action research framework to provide insights on differences in decision-making behaviour and areas for improvement of the method. Findings from these in-depth case studies indicate that the method's main benefit was that it stimulated curiosity on how options might be improved in order to mitigate negative consequences and capitalise on opportunities across scenarios. We conclude the paper by discussing these findings and their implications to the development of the method and the evaluation of strategic options under deep uncertainty.

Industrial architectural assessment using TARA

Scenario based architectural assessment is a well-established approach for assessing architectural designs. However scenario-based methods are not always usable in an industrial context, where in our experience, they can be perceived as complicated and expensive to use. In this paper we explore why this may be the case and define a simpler technique called TARA, which has been designed for use in situations where scenario based methods are unlikely to be successful. The method is illustrated through an experience report that explains how it was applied to the assessment of two quantitative financial analysis systems, and its strengths, weaknesses and relationship to other methods are briefly discussed.

Developing an Empirical and Risk-Based Revenue Forecasting Model for NAV CANADA

Accurate forecasting of aviation activity is necessary for NAV CANADA, because the act that created the company stipulates that revenues must equal cost commitments related to the provision of air navigation services. Accurate forecasting of revenue is an important early step in determining the cost commitments that can be met in upcoming fiscal years. Traditional econometric forecasting techniques are of a long-term nature and do not consider the seasonality of traffic, nor do they provide the required detail at the level of the individual month. For these and other reasons, empirical forecasting methods were developed as the primary forecasting approach for the company. This paper describes the selection and development of the empirical model, which is based on the Holt–Winters multiplicative method, for the air traffic that overflies Canadian airspace on routes between North America and Europe. A second part of the modeling process is the application of risk analysis not only to replace current scenario-based methods but also to improve and augment them. The methodology will then be applied to the two other markets, Asia and the Far East and Alaska, with eventual expansion to Canadian air traffic, though they are not discussed in this paper.

SHADD: A scenario-based approach to software architectural defects detection

Although a good architecture is not sufficient to guarantee the success of a software product, undoubtedly it is essential to support the product quality. Evaluating software architecture provides early insight into product capabilities and limitations. The earlier in the life cycle the problems are detected, the cheaper it is to fix them. In this paper, at first, a review on well-known scenario-based methods to evaluate software architectures is presented, and their advantages and limitations are discussed. Then, a method named SHADD with different characteristics is introduced to detect architectural defects. Using a scenario-based approach, the proposed method finds out the critical aspects and potential problems threatening the system from the stakeholder's point of view. Scenarios are then used as a basis for the process of architectural defects detection. SHADD and its elements are specified in a systematic form and an illustration of its application on the architecture of a real system is also presented. The results show that SHADD can be used to detect those architectural defects which may be uncovered during the application of conventional evaluation methods.

Participatory Methods for Planning the Future in Forest Communities

Forest devolution and government decentralization have increased community control over forests. Remoteness, low literacy, and lack of formal planning experience often leave forest communities unprepared for their new responsibilities. Forest communities need to develop skills that allow them to establish goals and make decisions transparently and democratically and to negotiate effectively with other local actors if they are to become more proactive participants in local governance processes. In Bolivia and Vietnam we tested four adaptations of scenario-based methods to assist forest communities to develop these skills. This article reflects on the strengths, limitations, and new applications of these methods. The methods encourage participation by members who have little experience with structured planning, including the most marginalized: women, elderly, and illiterate participants. The methods are useful as planning tools, for generating records of decision-making processes, and for preparing for negotiations between communities and local governments.

An inquiry into the potential of scenario analysis for dealing with uncertainty in strategic environmental assessment in China

Strategic environmental assessment (SEA) inherently needs to address greater levels of uncertainty in the formulation and implementation processes of strategic decisions, compared with project environmental impact assessment. The range of uncertainties includes internal and external factors of the complex system that is concerned in the strategy. Scenario analysis is increasingly being used to cope with uncertainty in SEA. Following a brief introduction of scenarios and scenario analysis, this paper examines the rationale for scenario analysis in SEA in the context of China. The state of the art associated with scenario analysis applied to SEA in China was reviewed through four SEA case analyses. Lessons learned from these cases indicated the word “scenario” appears to be abused and the scenario-based methods appear to be misused due to the lack of understanding of an uncertain future and scenario analysis. However, good experiences were also drawn on, regarding how to integrate scenario analysis into the SEA process in China, how to cope with driving forces including uncertainties, how to combine qualitative scenario storylines with quantitative impact predictions, and how to conduct assessments and propose recommendations based on scenarios. Additionally, the ways to improve the application of this tool in SEA were suggested. We concluded by calling for further methodological research on this issue and more practices.

A comparison of sample-based Stochastic Optimal Control methods

In this paper, we compare the performance of two scenario-based numerical methods to solve stochastic optimal control problems: scenario trees and particles. The problem consists in finding strategies to control a dynamical system perturbed by exogenous noises so as to minimize some expected cost along a discrete and finite time horizon. We introduce the Mean Squared Error (MSE) which is the expected $L^2$-distance between the strategy given by the algorithm and the optimal strategy, as a performance indicator for the two models. We study the behaviour of the MSE with respect to the number of scenarios used for discretization. The first model, widely studied in the Stochastic Programming community, consists in approximating the noise diffusion using a scenario tree representation. On a numerical example, we observe that the number of scenarios needed to obtain a given precision grows exponentially with the time horizon. In that sense, our conclusion on scenario trees is equivalent to the one in the work by Shapiro (2006) and has been widely noticed by practitioners. However, in the second part, we show using the same example that, by mixing Stochastic Programming and Dynamic Programming ideas, the particle method described by Carpentier et al (2009) copes with this numerical difficulty: the number of scenarios needed to obtain a given precision now does not depend on the time horizon. Unfortunately, we also observe that serious obstacles still arise from the system state space dimension.

Towards Analyzing the Architectural Support Level of Usability

The user acceptance of a software product especially depends on its usability. Therefore, it is a matter that needs to be addressed early on in the software development process. In existing scenario-based software architecture analysis methods that focus on usability, the usage context is not employed to select scenarios used for analysis, although it is known that understanding a specific usage context is important to carefully design for usability. In order to address this, we propose the method Software ArchitecTural analysis of Usability Requirements realizatioN'' SATURN in this paper. It is set in the requirements context, incorporates a usage context analysis, and a simple walkthrough of a user interface prototype. The method furthermore provides a knowledge base of standardized analysis scenarios and analysis templates that are derived from general usability requirements and design patterns. By arriving at an Architectural Support Level (ASL) based on the architectural impact of analyzed scenarios, the proposed procedure model shall guide evaluators to accomplish a repeated analysis of the current architectural design for usability in a traceable way.