Decision Aid Framework Research Articles

COVID-19 Bed Management Using a Two-Step Process Mining and Discrete-Event Simulation Approach

The sudden admission of many patients with similar needs caused by the COVID-19 (SARS-CoV-2) pandemic forced health care centers to temporarily transform units to respond to the crisis. This process greatly impacted the daily activities of the hospitals. In this paper, we propose a two-step approach based on process mining and discrete-event simulation for sizing a recovery unit dedicated to COVID-19 patients inside a hospital. A decision aid framework is proposed to help hospital managers make crucial decisions, such as hospitalization cancellation and resource sizing, taking into account all units of the hospital. Three sources of patients are considered: (i) planned admissions, (ii) emergent admissions representing day-to-day activities, and (iii) COVID-19 admissions. Hospitalization pathways have been modeled using process mining based on synthetic medico-administrative data, and a generic model of bed transfers between units is proposed as a basis to evaluate the impact of those moves using discrete-event simulation. A practical case study in collaboration with a local hospital is presented to assess the robustness of the approach. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In this paper we develop and test a new decision-aid tool dedicated to bed management, taking into account exceptional hospitalization pathways such as COVID-19 patients. The tool enables the creation of a dedicated COVID-19 intensive care unit with specific management rules that are fine-tuned by considering the characteristics of the pandemic. Health practitioners can automatically use medico-administrative data extracted from the information system of the hospital to feed the model. Two execution modes are proposed: (i) fine-tuning of the staffed beds assignment policies through a design of experiment and (ii) simulation of user-defined scenarios. A practical case study in collaboration with a local hospital is presented. The results show that our model was able to find the strategy to minimize the number of transfers and the number of cancellations while maximizing the number of COVID-19 patients taken into care was to transfer beds to the COVID-19 ICU in batches of 12 and to cancel appointed patients using ICU when the department hit a 90% occupation rate.

An Embedded Tactical Decision Aid Framework for Environmentally Adaptive Autonomous Underwater Vehicle Communication and Navigation

Motivated by a changing acoustic environment in the Arctic Beaufort Sea, in this article, we present a tactical decision aid framework for a human decision-maker collaborating with an autonomous underwater vehicle (AUV) to integrate the vertical sound-speed profile for underwater navigation and communication. In a predeployment phase, using modeled and real oceanographic data, we load basis function representations of the sound-speed perturbations onto one or more AUVs on deck, where a handful of weights can estimate a sound-speed profile. During deployment, these weights are updated on an AUV through a digital acoustic message to improve navigation and reciprocal communication throughout the duration of an under-ice mission. Field work applying this framework in the Beaufort Sea is presented, highlighting key decisions regarding predeployment oceanographic data assimilation, CTD cast collection, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> weight choice. Selected examples evaluate the framework’s ability to adapt to a depth-limited CTD cast and the appearance of an anomalous microlens feature in the profile. We show that the framework effectively balances the need to adapt in a changing acoustic environment in real time while maintaining operator trust in an AUV’s embedded intelligence.

A Novel Approach to Multi-Provider Network Slice Selector for 5G and Future Communication Systems.

The Network Slice Selection Function (NSSF) in heterogeneous technology environments is a complex problem, which still does not have a fully acceptable solution. Thus, the implementation of new network selection strategies represents an important issue in development, mainly due to the growing demand for applications and scenarios involving 5G and future networks. This work presents an integrated solution for the NSSF problem, called the Network Slice Selection Function Decision-Aid Framework (NSSF DAF), which consists of a distributed solution in which a part is executed on the user’s equipment (for example, smartphones, Unmanned Aerial Vehicles, IoT brokers) functioning as a transparent service, and another at the Edge of the operator or service provider. It requires a low consumption of computing resources from mobile devices and offers complete independence from the network operator. For this purpose, protocols and software tools are used to classify slices, employing the following four multicriteria methods to aid decision making: VIKOR (Visekriterijumska Optimizacija i Kompromisno Resenje), COPRAS (Complex Proportional Assessment), TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and Promethee II (Preference Ranking Organization Method for Enrichment Evaluations). The general objective is to verify the similarity among these methods and applications to the slice classification and selection process, considering a specific scenario in the framework. It also uses machine learning through the K-means clustering algorithm, adopting a hybrid solution in the implementation and operation of the NSSF service in multi-domain slicing environments of heterogeneous mobile networks. Testbeds were conducted to validate the proposed framework, mapping the adequate quality of service requirements. The results indicate a real possibility of offering a complete solution to the NSSF problem that can be implemented in Edge, in Core, or even in the 5G Radio Base Station itself, without the incremental computational cost of the end user’s equipment, allowing for an adequate quality of experience.

A Multifunctional Solution for Wicked Problems: Value-Chain Wide Facilitation of Legumes Cultivated at Bioregional Scales Is Necessary to Address the Climate-Biodiversity-Nutrition Nexus

Well-managed legume-based food systems are uniquely positioned to curtail the existential challenge posed by climate change through the significant contribution that legumes can make toward limiting Green House Gas (GHG) emissions. This potential is enabled by the specific functional attributes offered only by legumes, which deliver multiple co-benefits through improved ecosystem functions, including reduced farmland biodiversity loss, and better human-health and -nutrition provisioning. These three critical societal challenges are referred to collectively here as the “climate-biodiversity-nutrition nexus.” Despite the unparalleled potential of the provisions offered by legumes, this diverse crop group remains characterized as underutilized throughout Europe, and in many regions world-wide. This commentary highlights that integrated, diverse, legume-based, regenerative agricultural practices should be allied with more-concerted action on ex-farm gate factors at appropriate bioregional scales. Also, that this can be achieved whilst optimizing production, safeguarding food-security, and minimizing additional land-use requirements. To help avoid forfeiting the benefits of legume cultivation for system function, a specific and practical methodological and decision-aid framework is offered. This is based upon the identification and management of sustainable-development indicators for legume-based value chains, to help manage the key facilitative capacities and dependencies. Solving the wicked problems of the climate-biodiversity-nutrition nexus demands complex solutions and multiple benefits and this legume-focus must be allied with more-concerted policy action, including improved facilitation of the catalytic provisions provided by collaborative capacity builders—to ensure that the knowledge networks are established, that there is unhindered information flow, and that new transformative value-chain capacities and business models are established.

Towards a complex investment evaluation framework for renewable energy systems: A 2-level heuristic approach

Renewable energy technologies still endure critical technical and financial challenges. These challenges demand for specially tailored decision support tools for planning the (renewable-based) strategical expansion of power systems. However, existing tools not always adequately integrate the tactical and operational dimensions of power systems into the evaluation process; hence, the designed investment strategies might fail to foresee how new infrastructure integrates with the existing power system and also fail to capture the volatile energy market dynamics. In this paper, this methodological gap is addressed by presenting a decision aid framework, based on mixed integer programming, for supporting long-term decision-making when planning (the expansion of existing) energy systems. The proposed framework relies on heuristically solving an optimization problem coined as the Generation, transmission and storage location and sizing of operation-aware sustainable power system design problem, which encodes two nested problems: a novel strategic renewable power system expansion problem, and a Unit Commitment problem. Using a case study from the Chilean power system, it is shown that the proposed tool ensures a more realistic and accurate economic evaluation, as it takes into account the influence of the evaluated project on the grid where it will be installed. In the considered case study, the devised framework designs a sustainable power system expansion investment strategy ensuring an investment rate of return of at least 7% and an annualized profit of more than 277 MM USD considering a 30 years evaluation horizon; furthermore, this is accomplished along with an equalization of the spot price resulting in a 13% reduction with respect to the current spot price of the system. This shows the impact of renewable sources and energy storage systems on the market operation. Furthermore, the proposed approach is used to investigate how high (public) subsidies on renewable technologies should be in order to increase the penetration of these technologies.

Towards a Goal Programming Methodology for Constructing Equity Mutual Fund Portfolios

The evaluation of the performance of mutual fund [MF] portfolios has been a very interesting research topic not only for researchers, but also for managers of financial, banking and investment institutions. In this paper, a multicriteria decision aid framework is proposed for the construction of MF portfolios. The proposed methodology is based on a Goal Programming approach to determine the proportion of each MF in the constructed portfolios. This methodology is applied on a sample of Indian MFs over the period 2007-2006 with encouraging results.

Utilising design for Six Sigma to implement soft skills in construction education

The construction industry demands a competitive pipeline of construction graduates who possess a high level of soft skills coupled with technical skills. The existence of a soft skills gap among construction schools graduates was previously discussed by the authors. To reduce the gap, construction schools were challenged to implement new learning innovations to increase the soft skills level among their graduates. This paper presents the authors' work to develop a theoretical framework that can be used as a decision aid framework to help in designing a reliable holistic soft skills curriculum models. Design for Six Sigma (DFSS) has been identified as the skeleton of the framework. The paper focuses on presenting all tasks and activities that encompass the framework. It is hoped that this framework will offer a continuous collaboration system between the industry and academia so that the teaching activities of construction will improve and broaden the soft skills taught in the classrooms to correlate with those needed in the marketplace. Consequently, it is hoped that the framework will help in bridging the gaps between construction graduates and their employers and ultimately facilitate the recruitment of entry-level construction graduates.

Developing a User-Based Decision-Aid Framework for Water Storage Systems in Sub-Saharan Africa: The Case of Blue Nile Basin in Ethiopia

The development of water storage schemes in Sub-Saharan Africa (SSA) is considered a major aid for those regions with unequal water distribution, limited accessibility and anticipated climate change impacts. Great attention is given by many SSA countries to set up different water storage schemes that may improve rural and urban development on a national level. The funding for the water storage schemes is often derived from foreign agencies which conduct feasibility studies for the financing of potential investments. Often however, the feasibility studies rely on a single monetary criterion which may not identify the most appropriate water storage in each case. In addition, limited data availability in many SSA countries increases the difficulty of identifying the most suitable storage option. This paper develops a multicriteria framework for the integrated evaluation of water storage strategies in Sub-Saharan African countries. A set of economic, agronomic and opinion-based criteria are assessed through the PROMETHEE II outranking approach. The introduction of crop modeling complements the limited field data available in agronomic criteria and enhances the scientific rigor of the method. Ethiopia is adopted as a representative case of SSA countries where a diverse set of water storage options is currently under construction, often financed by foreign agencies.

A probabilistic Bayesian framework for progressively updating site-specific recommendations

The goal of this research was to create an agricultural adaptive management framework that enables the probabilistic optimization of N fertilizer to achieve maximized net returns under multiple uncertainties. These uncertainties come in the form of bioclimatic variables that drive crop yield, and economic variables that determine profitability. Taking advantage of variable rate application (VRA), spatial monitoring technologies, and historical datasets, we demonstrate a comprehensive spatiotemporal modeling approach that can achieve optimal efficiency for the producer under such uncertainties. The utility of VRA fertilizer research for producers is dependent upon a localized accurate understanding of crop responses under a range of possible climatic regimes. We propose an optimization framework that continuously updates by integrating annual on-site experiments, VRA prescriptions, crop prices received, input prices, and climatic conditions observed each year under a dryland spring wheat (Triticum aestivum) cropping system. The spatio-temporal Bayesian framework used to assimilate these data sources also enables calculation of the probabilities of economic returns and the risks associated with different VRA strategies. The results from our simulation experiments indicated that our framework can successfully arrive at optimum N management within 6–8 years using sequential Bayesian analysis, given complete uncertainty in water as a driver of crop yield. Once optimized, the spatial N management approach increased net returns by $23–25 ha−1 over that of uniform N management. By identifying small-scale targeted treatments that can be merged with VRA prescriptions, our framework ensures continuous reductions in parameter uncertainty. Thus we have demonstrated a useful decision aid framework that can empower agricultural producers with site-specific management that fully accounts for the range of possible conditions farmers must face.

P5-15-03: Development of a Patient Decision Aid for Women 70 Years and Older with Stage I, Hormonally Sensitive, Breast Cancer Considering Adjuvant Treatment Post-Lumpectomy.

Abstract Background: Decision Aids (DA) are developed with the intent to support people in making specific and deliberate choices by improving information transfer about different outcomes. Previous research has shown that DAs can increase patient knowledge regarding treatment options, reduce decisional conflict, and increase patient satisfaction with the decision-making process. However, no DAs have been developed to help older breast cancer patients decide whether or not to undergo adjuvant RT. We developed and tested a DA for older women with stage I,ER/PR positive breast cancer considering adjuvant treatment post-lumpectomy and we examined its impact on treatment decision-making process. Methods and Materials: A DA was developed and evaluated in three steps following the Ottawa Decision Aid Framework: 1) Needs assessment (N=16); 2) Pilot I, to examine the DA's acceptability (N=12); and 3) Pilot II, a pre-test post-test (N=38) with older women with ER/PR responsive breast cancer post-lumpectomy who were receiving adjuvant RT. Measures included questionnaires to assess patient's satisfaction with the DA, patients’ self-reported decisional conflict (DC), level of distress, treatment-related knowledge, and choice predisposition Results: The DA is a booklet that details each adjuvant treatment option's benefits, risks and side-effects tailored to their clinical profile; includes a value clarification exercise; and steps to guide them towards their own treatment decision. All women felt the DA was helpful and informative. Compared with baseline scores, patients had a statistically significant (p &amp;lt; .05) reduction in DC (adjusted mean difference [AMD], −7.18; 95% confidence interval [CI], −13.50 to 12.59); increased clarity of the treatment benefits and risks (AMD, −10.86, CI, −20.33 to 21.49; and improved general treatment knowledge (AMD, 8.99, CI, 2.88 to 10.28) after using the DA. General trends were also reported in patient's choice predisposition scores suggesting potential differences in treatment decision after DA use. Discussion: This study provides evidence that this DA may be a helpful educational tool for this group of women. The quality of care for older breast cancer patients may be enhanced by using a tailored DA to help the patient be informed of their treatment options and to prepare for decision-making. Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr P5-15-03.

A new application of ELECTRE III and revised Simos’ procedure for group material selection under weighting uncertainty

Towards the end of a design process designers may face a number of candidate materials with different attributes that are difficult to distinguish with the aid of available databases. In such situations material selection of sensitive components is perhaps one of the most challenging problems in the design of structural elements in some industries such as aerospace. The selection process is often realized as a team-work task to enhance reliability of the chosen material. During group decision making, however, separations in design preferences can be encountered. Furthermore, there may be uncertainties in each designer’s mind with regards to expressing his/her preferences over design criteria. This paper using a revised Simos’ method with the ELECTRE III optimization model is an attempt to provide a decision aid framework that account for both of these effects. A post-aggregation strategy with an overall rank loss function is proposed to arrive at candidate materials that show the most stable and the highest ranks in a list of given candidates. To show the applicability of the approach, a sample case study in the material selection of a thermal loaded conductor cover sheet is conducted and validated by an available database. It is also illustrated that, using ELECTRE III, a non-compensatory aspect of material selection can be assumed while attaining a reasonable sensitivity to weight fluctuations.

Agricultural countermeasures in nuclear emergency management: a stakeholders' survey for multi-criteria model development

In order to accommodate the use of multi-criteria decision-aid tools for nuclear crisis management in the Belgian context, a stakeholder process was carried out, aiming to add in stakeholders' values and practical knowledge in the models developed for decision support. The main steps are described in this paper, following a case study concerning the management of contaminated milk. We argue in favour of a systematic interaction with relevant stakeholders in all phases of the decision analysis. Some difficulties arising from the complexity of the problem itself, as well as from the different points of view of relevant stakeholders are illustrated and discussed. Suggestions are given for resolving this in a decision-aid framework and an example is provided throughout the paper for a better understanding of the issues involved.

A Decision-Aid Framework to Provide Guidance for the Enhanced Use of Best Available Techniques in Industry

Best Available Techniques (BATs) contribute significantly to the reduction of industrial environmental burdens with respect to air pollution, wastewater, and solid wastes. In Europe, the application of BATs is prescribed by Directive 96/61/EC, which, however, leaves the selection of specific BATs to plant operators. In making their choices, installations have to consider not only the environmental benefits of BATs, but also all relevant cost components. In assessing the economic attractiveness of potential BATs and their combinations, as well as incentives and disincentives to be instituted, the cost of environmental externalities, usually not reflected in market prices, should be taken into account. In this paper, a decision-aid framework combining an assessment of environmental externalities and the utilization of multicriteria methods and, more specifically, Multiobjective Mathematical Programming, capable of addressing all these issues in a comprehensive and coherent manner, is presented. This is illustrated by its application for the region of Attica in Greece, where over 50% of the industrial basis and Athens, with its 4 million inhabitants, are located. The implementation of the framework and its associated tools to 800 installations led to the identification of the specific BATs, alone or in combination, that provide the most cost-effective reductions of four air pollutants (PM10, SO(2), NO(x), VOC) and CO(2). The results also clearly demonstrate the increased pollution reductions that would result from the adoption of BATs made economically attractive by the inclusion of externalities. Estimates of investments and net present values with and without incentives/disincentives are also provided.

Towards a goal programming methodology for constructing equity mutual fund portfolios

The evaluation of the performance of mutual fund (MF) portfolios has been a very interesting research topic not only for researchers, but also for managers of financial, banking and investment institutions. In this paper, a multicriteria decision aid framework is proposed for the construction of MF portfolios. The proposed methodology is based on a goal programming approach to determine the proportion of each MF in the constructed portfolios. This methodology is applied on a sample of Greek MFs over the period 1999–2001 with encouraging results.