Proposed Decision Support Tool Research Articles

A three-stage matheuristic for home blood donation appointment reservation and collection routing

AbstractIn Western countries, the so-called Blood Donation Supply Chain (BDSC) provides blood units to several health services. Its first echelon is the collection of unit from donors, which requires a careful management because an unbalanced supply of units to the rest of the chain could trigger alternating periods of blood shortage and wastage. However, the management of blood collection is only marginally studied in the literature, in comparison to other BDSC echelons. In this work, we propose a new organizational model for blood collection, in which blood is collected at donor’s homes, and provide a decision support tool for its management. This new model provides a novel contribution to the understudied blood collection echelon and, at the same time, it responds to the emerging need of delocalization of health services. The proposed decision support tool consists of an interconnected matheuristic framework with three decision stages: (i) a planning model to create the donation slots that will be assigned to donors, (ii) an online allocation of these slots using a flexible set of criteria, and (iii) a Multi-Trip Vehicle Routing Problem with Time Windows (MTVRP-TW) to route the bloodmobiles that collect blood at donors’ homes. The main goals are to balance the production of blood units between days and to minimize the distance travelled by the bloodmobile fleet, while respecting time windows negotiated with donors. This framework also has the feature of immediately providing a list of slots to choose from when a donor makes a booking request. The decision support tool has been tested on data from a real Italian provider. Results confirm its effectiveness, and the capability of providing good quality and economically sustainable solutions in reasonable timeframes.

SURG-02. SURGICAL OUTCOME OF SUPRATENTORIAL MENINGIOMA PATIENTS AGED ≥80 YEARS – RETROSPECTIVE INTERNATIONAL MULTICENTER STUDY

Abstract BACKGROUND Demographic changes will lead to an increase in old patients, a population with significant risk of postoperative morbidity and mortality, requiring neurosurgery for meningiomas, Objective: To evaluate outcome of supratentorial meningioma patients aged ≥80 years. This multicenter study aims to report neurofunctional status after resection, to identify factors associated with outcome, and to validate a previously proposed decision support tool. METHODS Neurofunctional status was assessed by the Karnofsky Performance Scale (KPS). Patients were categorized in poor (KPS ≤40), intermediate (KPS 50-70), and good (KPS ≥80) preoperative subgroups. Volumetric analyses of tumor and peritumoral brain edema (PTBE) were performed; volumes were scored as small (< 10 cm3), medium (10-50 cm3), and large ( >50 cm3). RESULTS The study population consisted of 262 patients. Median age at surgery was 83.0 years. Median preoperative KPS was 70; 117 (44.7%) patients were allotted to the good, 113 (43.1%) to the intermediate, and 32 (12.2%) to the poor subgroup. Median tumor and PTBE volumes were 30.2 cm3 and 27.3 cm3; large PTBE volume correlated with poor preoperative KPS status (P = .008). The 90-day and 1-year mortality rates were 9.0% and 13.2%. Within the first postoperative year, 101 (38.5%) patients improved, 87 (33.2%) were unchanged, and 74 (28.2%) were functionally worse (including deaths). Each year-increase of age associated with 44% (23–70%) increased risk of 90-day and 1-year mortality. In total, 111 (42.4%) patients suffered from surgery-associated complications. Maximum tumor diameter ≥5cm (odds ratio 1.87 (1.12–3.13)) and large tumor volume (odds ratio 2.35 (1.01–5.50)) associated with an increased risk of complications. Among patients with poor preoperative status and large PTBE, most (58.3%) benefited from surgery. CONCLUSION Patients with poor preoperative status and large PTBE most often showed postoperative improvements. The decision support tool may be of help in identifying cases that most likely benefit from surgery.

Combining urban metabolism and reinforcement learning concepts for sustainable water resources management: A nexus approach

Modeling Water-Energy-Food (WEF) nexus is necessary for integrated water resources management (IWRM), especially in urban areas. This paper presents a new urban water metabolism-based methodology for WEF nexus modeling and management. A behavioral simulation model is used to incorporate the characteristics of stakeholders in an urban area. Modified versions of the Borda count, Copeland rule, and fallback bargaining procedures are implemented to choose the socially acceptable management scenarios. Finally, the selected scenarios’ effectiveness is evaluated using the fairness and total utility indices.The applicability of the proposed methodology is evaluated by applying it to the Kan River basin, Tehran, Iran, which is suffering from some water and environmental issues. The considered management scenarios include adding new water sources, leakage control plans, using rubber dams for enhancing groundwater recharge, revising water allocation priorities, and developing semi-centralized or decentralized reuse strategies for reclaimed wastewater. Results illustrate that considering different fluxes (i.e., water quantity, pollutants, energy, greenhouse gases (GHG), and materials) is as important as incorporating the social characteristics of stakeholders. Simulating the socially acceptable scenario shows that the aquifer's average water level improves by 3 (m), and its average nitrate concentration reduces by 16 (mg/L) in comparison with the business as usual (BAU) scenario. In addition, by implementing different water reuse strategies, which are energy-intensive, total energy consumption is reduced by 5% due to less groundwater pumping. Also, the selected scenario decreases GHG emissions by 18% and increases the sequestrated carbon dioxide by 20%. In conclusion, the proposed decision support tool can provide policies for sustainable water resources management considering water quality and quantity issues, energy usage, and GHG emission.

Risk-based decision-making related to preprocedural coronavirus disease 2019 testing in the setting of GI endoscopy: management of risks, evidence, and behavioral health economics

Background and AimsControversies exist regarding the benefits and most appropriate approach for preprocedural coronavirus disease 2019 (COVID-19) testing (eg, rapid antigen test, polymerase chain reaction, or real-time polymerase chain reaction) for outpatients undergoing diagnostic and therapeutic procedures, such as GI endoscopy, to prevent COVID-19 infections among staff. Guidelines for protecting healthcare workers (HCWs) from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection from outpatient procedures varies across medical professional organizations. This study provides an evidence-based decision support tool for key decision-makers (eg, clinicians) to respond to COVID-19 transmission risks and reduce the effect of personal biases.MethodsA scoping review was used to identify relevant factors influencing COVID-19 transmission risk relevant for GI endoscopy. From 12 relevant publications, 8 factors were applicable: test sensitivity, prevalence of SARS-CoV-2 in the population, age-adjusted SARS-CoV-2 prevalence in the patient cohort, proportion of asymptomatic patients, risk of transmission from asymptomatic carriers, risk reduction by personal protective equipment (PPE), vaccination rates of HCWs, and risk reduction of SAE by vaccination. The probability of a serious adverse event (SAE), such as workplace-acquired infection resulting in HCW death, under various scenarios with preprocedural testing was determined to inform decision-makers of expected costs of reductions in SAEs.ResultsIn a setting of high community transmission, without testing and PPE, 117.5 SAEs per million procedures were estimated to occur, and this was reduced to between .079 and 2.35 SAEs per million procedures with the use of PPE and preprocedural testing. When these variables are used and a range of scenarios are tested, the probability of an SAE was low even without testing but was reduced by preprocedural testing.ConclusionsUnder all scenarios tested, preprocedural testing reduced the SAE risk for HCWs regardless of the SARS-CoV-2 variant. Benefits of preprocedural testing are marginal when community transmission is low (eg, below 10 infections a day per 100,000 population). The proposed decision support tool can assist in developing rational preprocedural testing policies.

Transportation data visualization with a focus on freight: a literature review

ABSTRACT Road-based freight movements are a critical component of the supply chain and transportation networks. With the drive to invest in big data collection capabilities, most of the collected freight movement data remain underutilized. To improve the efficiency and resiliency of the supply chain, it is essential to enhance the visibility of goods movements on road networks. To this end, we provide in this paper a comprehensive literature review on this topic and analyze the previous research from different perspectives, such as data levels of abstraction and existing visualization techniques. In addition, we provide a taxonomy of freight transportation visualization according to the underlying analytic objective. Furthermore, we propose a decision support tool to aid freight data analysts in selecting the right visualization tools. Finally, we identify research gaps in the field of freight transportation visualization.

Assessing the Risks and Success Factors of Telehealth Development Projects in an Academic Setting

The development of a telehealth technology in an academic setting is a complex project that faces several obstacles. The early assessment of the project risks plays a critical role in the translation of promising telehealth innovations into healthcare practice. This paper presents a decision support tool based on Failure Mode and Effects Analysis (FMEA) and Quality Function Deployment (QFD) techniques to associate the project risks to relevant success factors. Certain modifications in both techniques are applied to deploy them for project risk assessment. The project risks and success factors used in the tool are identified from the literature. The proposed decision support tool enables researchers to manage the risks in their telehealth development projects and identify action items to overcome such risks. The application of the proposed tool is illustrated with a telehealth development project for virtual physical therapy.

Interactive multiobjective optimization for finding the most preferred exercise therapy modality in knee osteoarthritis

Background There are no explicit guidelines or tools available to support clinicians in selecting exercise therapy modalities according to the characteristics of individual patients despite the apparent need. Objective This study develops a methodology based on a novel multiobjective optimization model and examines its feasibility as a decision support tool to support healthcare professionals in comparing different modalities and identifying the most preferred one based on a patient’s needs. Methods Thirty-one exercise therapy modalities were considered from 21 randomized controlled trials. A novel interactive multiobjective optimization model was designed to characterize the efficacy of an exercise therapy modality based on five objectives: minimizing cost, maximizing pain reduction, maximizing disability improvement, minimizing the number of supervised sessions, and minimizing the length of the treatment period. An interactive model incorporates clinicians’ preferences in finding the most preferred exercise therapy modality for each need. Multiobjective optimization methods are mathematical algorithms designed to identify the optimal balance between multiple conflicting objectives among available solutions/alternatives. They explicitly evaluate the conflicting objectives and support decision-makers in identifying the best balance. An experienced research-oriented physiotherapist was involved as a decision-maker in the interactive solution process testing the proposed decision support tool. Results The proposed methodology design and interactive process of the tool, including preference information, graphs, and exercise suggestions following the preferences, can help clinicians to find the most preferred exercise therapy modality based on a patient’s needs and health status; paving the way to individualize recommendations. Conclusions We examined the feasibility of our decision support tool using an interactive multiobjective optimization method designed to help clinicians balance between conflicting objectives to find the most preferred exercise therapy modality for patients with knee osteoarthritis. The proposed methodology is generic enough to be applied in any field of medical and healthcare settings, where several alternative treatment options exist. KEY MESSAGES We demonstrate the potential of applying Interactive multiobjective optimization methods in a decision support tool to help clinicians compare different exercise therapy modalities and identify the most preferred one based on a patient’s needs. The usability of the proposed decision support tool is tested and demonstrated in prescribing exercise therapy modalities to treat knee osteoarthritis patients.

Integrating Fuzzy-AHP and Fuzzy-GIS method in zoning wind power development in Bac Lieu province towards sustainability

From the perspective of economic, environmental, and social point of view , Wind project site selection is a difficult problem with many criteria to consider. In this study, a proposed decision support tool was developed by the integration of the Fuzzy Analytical Hierarchy Process Method (Fuzzy-AHP) and Fuzzy Geographic Information System (Fuzzy–GIS) methods. Fuzzy-AHP was used to determine the weights of the criteria and the Fuzzy-GIS was used to normalize the data and develop the maps for optimal option determination. The study has proposed two sets of evaluation criteria, including a set of exclusion criteria and a set of criteria for ranking priority of wind power plant locations. The set of evaluation criteria was divided into 3 criteria and 10 sub-criteria. Besides, the research results showing the suitability with the wind power development planning in Bac Lieu province, an outstanding result of the study is: While local decision-makers considered that the rice fields, which account for 83.86% of the total area of the province, were unsuitable for wind power development, we found that the rice fields should be included in the planning consideration area because of the high wind speed as well as the positive effects of wind farms on crop yields and minimizing negative impacts on the natural environment.

Decision support tool for enabling resiliency in an underground power distribution system

Natural disasters cause substantial damage to power distribution infrastructure with long duration of power outages. A resilient Power Distribution System (PDS) should be able to quickly absorb, adapt and recover from the catastrophic events. Enabling resiliency in the PDS is a new imperative for utilities to reduce financial losses, enhance customer satisfaction and supply critical loads in the network. Modern underground PDS comprise of underground feeders and overhead components. Though, these PDS are considered to be highly reliable; however, they still remain vulnerable to catastrophic events by virtue of their locations, age, maintenance and other factors. These factors have a direct impact on resiliency assessment of the system. Quick and accurate decision making by the network operator during and immediately after a natural disaster can make significant impact on the restoration of the system, particularly critical loads. This paper proposes a set of metrics for evaluation of resiliency for underground power distribution networks and a decision support tool for the network operator to enable resiliency in a PDS during network planning and operational contingency scenarios. It facilitates the decision making process to supply all critical loads in the network under all possible scenarios. The proposed decision support tool to enable resiliency is tested through simulation study on a real 110 Bus, 11 kV, underground PDS of CESC Limited, in Patuli area of Kolkata, India.

A heuristic approach for green vehicle routing

This paper addresses a green capacitated vehicle routing problem that accounts for transportation emissions. A Dynamic Programming approach has been used to formulate the problem. Although small-sized problems can be solved by Dynamic Programming, this approach is infeasible for larger problems due to the curse of dimensionality. Therefore, we propose a Dynamic Programming based solution approach that involves the ideas of restriction, simulation and online control of parameters to solve large-sized problems. The added values of the proposed decision support tool have been shown on a small-sized base case and relatively larger problems. Performance comparisons of the proposed heuristic against other existing Dynamic Programming based solution approaches reveal its effectiveness, as in most of the instance-setting pairs, the proposed heuristic outperforms the existing ones. Accordingly, the proposed heuristic can be used as an alternative decision support tool to tackle real routing problems confronted in sustainable logistics management.

Decision support system for flood risk reduction policies: The case of a flood protection measure in the area of Vicenza

Abstract Climate change is one of the most significant and pressing issues faced by humanity; it frequently results in major natural disasters, such as catastrophic floods, which require the establishment of effective management policies by local and national authorities. These policies involve complex multistep decision-making processes that require combined assessment of various sources of data by different stakeholders. Even though an abundance of data is being collected to monitor climate change and estimate its consequences on the society, the environment, and the economy, policy-making is still largely based on intuition rather than evidence due to lack of a structured approach for modeling the decision-making process and considering the appropriate use of data in every step of the process. The goal of this work is to introduce a novel decision support system that can guide policy makers through a structured data-driven decision-making process aiming to create policies for flood risk management. The proposed system is a multifacet platform that guides policy makers through five phases—inform, advise, monitor, evaluate, and revise—of the policy cycle. For each phase, different dashboards provide relevant information regarding the environmental, social, and economic conditions. To demonstrate the potential of the proposed system, we use it to assess a flood protection policy in the city of Vicenza, Italy. The results reveal the benefits and challenges of the proposed decision support tool for public administrations involved in flood risk management.

A Simulation-Based Optimization Method for Warehouse Worker Assignment

The general assignment problem is a classical NP-hard (non-deterministic polynomial-time) problem. In a warehouse, the constraints on the equipment and the characteristics of consecutive processes make it even more complicated. To overcome the difficulty in calculating the benefit of an assignment and in finding the optimal assignment plan, a simulation-based optimization method is introduced. We first built a simulation model of the warehouse with the object-oriented discrete-event simulation (O2DES) framework, and then implemented a random neighborhood search method utilizing the simulation output. With this method, the throughput and service level of the warehouse can be improved, while keeping the number of workers constant. Numerical results with real data demonstrate the reduction of discrepancy between inbound and outbound service level performance. With a less than 10% reduction in inbound service level, we can achieve an over 30% increase in outbound service level. The proposed decision support tool assists the warehouse manager in dealing with warehouse worker allocation problem under conditions of random daily workload.

Hybrid fuzzy-Bayesian decision support tool for dynamic project scheduling and control under uncertainty

Construction projects are complex and highly uncertain due to their special characteristics. Considering the incapability of current project scheduling methods in addressing the associated uncertainty, there is an emerging need for a dynamic scheduling and monitoring tool during project implementation. This paper addresses this need by proposing a dynamic decision support tool which employs fuzzy set theory to assess the combinatory effect of multiple risk factors on activities duration and Bayesian network to control and predict productivity under uncertainty. Advantages of proposed decision support tool comparing to existing approaches are: (1) calculating the fuzzy weighted average through an exact analytical solution which avoids creating incorrect fuzzy membership functions; (2) employing Type-2 interval-valued fuzzy numbers to model the uncertainty in expert evaluations, and (3) utilizing the advantages of Bayesian network in updating prior probability distributions of duration and predict productivity with actual activity data. Proposed model contributes to knowledge that could help minimize the schedule overrun and improve overall project performance. To elaborate the methodology, modelling and updating procedures of a group of activities in a simulated bridge project is presented as a case study.

Decision Support Tool for Planning Neighborhood-Scale Deployment of Low-Speed Shared Automated Shuttles

Increasing interest and investment in connected, automated, and electric vehicles as well as mobility-as-a-service (MaaS) concepts are paving the way for the next major transformation in transportation through automated and shared mobility. The initial excitement toward rapid deployment and adoption of automated vehicles (AVs) has subsided, and low-speed automated shuttles are emerging as a more pragmatic pathway for introducing automated mobility in geofenced districts. Such shuttles hold the promise to provide a viable alternative for serving short trips in urban districts with high travel densities. As interest in low-speed automated shuttle systems (to improve urban mobility) increases, the need for tools that can inform communities in relation to benefits or disadvantages of automated shuttle deployments is imminent. However, most of the existing transportation planning and simulation tools are not capable of handling emerging shared automated mobility options. This paper presents a microscopic simulation toolkit that can be used by cities and communities to plan for the deployment of low-speed automated shuttles systems, as well as other shared mobility options. Labeled as the Automated Mobility District modeling and simulation toolkit, the proposed decision support tool intends to help cities evaluate the mobility and sustainability impacts of deploying shared automated vehicles (SAVs) in geofenced regions. This paper describes the toolkit, as well as a sample scenario analysis for the deployment of low-speed automated shuttles in Greenville, South Carolina, U.S. Results from the scenario study demonstrate the effectiveness of the proposed simulation toolkit in planning for advanced mobility systems.

Designing of an agility control system: a case of an Indian manufacturing organization

PurposeAgility is the ability of an organization to adjust its supply chain tactics and operations to respond quickly against altering business environments such as fluctuating demand pattern, supply chain disruption and global competition. An agile organization must possess a promising capability of swiftly responding to dynamic conditions while being cost-effective without compromising the efficiency. Such high-performance adaptability necessitates the role of supply chain managers to maximize the agility of the supply chain through the efficient use of input resources. Therefore, the purpose of this study to reveal a new decision support tool that would allow the key decision-makers to maximize the agility of the supply chain while deploying the input resources more effectively.Design/methodology/approachIn present study, an integrated approach of popular analytic hierarchy process (AHP) and goal programming (GP) has been adopted as a potential solution methodology. AHP has been implemented to allocate the local and global weights to decision variables, whereas GP incorporates the AHP weights into the desired model.FindingsIt was found that the proposed decision support tool restricts the value of the decision variables for maximizing the agility and optimizing the usage of input resources. The results obtained from the model validate the objective of achieving targeted agility level within the available resource limitations.Research limitations/implicationsThe decision support tool developed in the proposed study offers a systematic and effectively simple approach to supply chain managers with a goal of identifying the degree of focus under each decision variable in the respective manufacturing organizations.Originality/valueA novel decision support tool has been developed known as an agility control system), which helps the decision-maker to achieve the required agility in the supply chain by controlling the decision variables.

Stochastic data-driven optimization for multi-class dynamic pricing and capacity allocation in the passenger railroad transportation

As for any passenger transportation service provider, pricing and capacity management are two critical tools for the profitability of a passenger railroad service provider: pricing affects the demand for the services and the capacity management sets the availability of the services in advance. In this study, an expert system is developed as a decision support tool for a passenger railroad service provider’s integrated pricing and capacity management problem, which has great significance for the success of the service provider. Considering the demand uncertainty, we first formulate the integrated pricing and capacity management problem as a stochastic nonlinear integer programming (SNLIP) model. This model includes dynamic pricing and dynamic capacity allocation decisions for multiple service classes over a planning horizon in order to maximize profit. Also, several key characteristics of the passenger railroad service operations are captured in the model. Due to inherent demand uncertainty as well as the dynamic nature of the problem, a fast and efficient solution approach is needed. Therefore, a simulation-based procedure embedded in a simulated annealing method is proposed to solve the model. Several real-life cases from Fadak Five-Star Trains (an Iranian luxurious passenger railroad service provider) are presented to demonstrate the model and the solution approach. The results of the case studies show the operational and profitability impacts of using the proposed decision support tool as well as its potential capabilities for practical use by other service providers.

Development of a Toileting and Containment Decision Support Tool.

A multidisciplinary international expert panel was convened to provide input for a proposed decision support tool. This tool will assist health care professionals who are not specialized in incontinence care to assess individuals with urinary and/or fecal incontinence and recommend appropriate person-centered management options for the home care and ambulatory community settings. A targeted literature review was complemented by a series of interviews with experts in continence management, followed by a practitioner survey and rounds of expert opinion. A set of factors for assessment were defined, along with questions created to identify and quantify the factors. In addition, a range of lifestyle intervention, toileting and containment strategies were identified that were appropriate for the decision support tool. Future steps required to progress this work to a functioning tool are described.

Seismic-Resilient Bulk Power Grids: Hazard Characterization, Modeling, and Mitigation

The operation of the electricity delivery infrastructure is environmentally driven and vulnerable to a wide range of high-impact low-probability (HILP) hazards. Among different classes of HILP disasters, earthquakes are one of the most unpredictable hazards which may lead to widespread disruptions of mission-critical services and infrastructures. This article introduces a comprehensive framework for modeling and characterization of seismic hazards, vulnerability assessment of electric systems to the earthquake, and corrective actions and mitigation strategies ensuring operational resilience. The Monte Carlo simulation is employed to produce a realistically large set of possible earthquake scenarios to capture the stochastic nature of seismic hazards. An inclusive approach is then introduced based on the fundamental principles of fragility curves to assess the vulnerability of power generation facilities in the face of HILP earthquakes. A new seismic risk metric is suggested that takes into account both hazard and vulnerability probabilities, as well as the financial consequences due to postquake disruptions in power generation stations. Along with the generation redispatch strategy as a conventional mitigation solution following a nontrivial contingency, a new mitigation strategy centered on corrective network topology control is formulated to maximize the load outage recovery following HILP disruptions. The proposed decision support tool enables a swift restoration and improved resilience in dealing with the aftermath of the HILP earthquakes. Efficacy of the proposed framework is numerically analyzed and verified on both the IEEE 57-bus and IEEE 118-bus test systems.

Predictive Risk Analytics for Weather-Resilient Operation of Electric Power Systems

Day-to-day operation of the electricity grid generation, transmission, and distribution is environmentally driven and closely dependent on evolving weather patterns. This paper introduces several new weather-driven analytics for accurate spatial–temporal electricity generation forecasts, asset health and reliability assessment, probabilistic load forecasts, and electricity market simulations. A new risk metric is suggested, which accounts for the weather hazards, grid vulnerability, and financial consequences in the face of changing weather patterns and associated meteorological predictions over time. New mitigation formulations for power system topology control through transmission line switching for fast and timely recovery of the weather-caused electricity outages are suggested. The proposed decision support tool enables the operators to predictively evaluate the high-risk weather threats and consequently plan on how to safeguard the grid when exposed to forecasted weather-driven incidents. The efficiency of the proposed toolset is illustrated by application to a part of the IEEE 73-Bus test system.

A proposed decision support tool for prioritising conservation planning of Southeast Asian seagrass meadows: combined approaches based on ecosystem services and vulnerability analyses

AbstractSeagrass habitats provide a range of goods and benefits to coastal communities by supporting ecosystem functioning, food provisioning, and cultural values. However, they are at risk worldwide from anthropogenic activities, climate change impacts and limited resources, which hamper efforts to protect them. Effective conservation planning requires prioritisation of sites based on multiple factors, including their relative value and vulnerability (VU). The current study examines the efficacy of combining two well-established approaches – ecosystem service assessments and habitat VU analyses – to test a method for determining conservation priority for selected seagrass meadows in Southeast Asia. Seven seagrass meadows around Southeast Asia were selected and scored by the authors based on (1) the cumulative contribution of 12 ecosystem services (ES) and the condition of the meadows, (2) VU from 16 anthropogenic threats, which were each ranked against five factors (spatial scale, frequency, functional impact, resistance and recovery time) and (3) the combination of the two approaches to create a single numerical index. Prioritisation of sites differed when meadows were scored solely for the ES they provide, compared to how vulnerable they are to anthropogenic threats. The new combined analysis brought the two metrics together to give conservation priority to sites where management intervention will yield maximum conservation benefits from conservation effort, and resulted in the prioritisation of three sites: Barrang Lompo (Indonesia), Cape Bolinao (Philippines) and Beting Tanjung Kupang (Malaysia). This method is easy to use and does not require great technical expertise. It is also auditable, allowing for clear and transparent understanding of decision-making process.