Data-driven Decision Support Tools Research Articles

Forecasting Agricultural Trade Based on TCN-LightGBM Models: A Data-Driven Decision

Facing the increasing complexity and dynamic fluctuations of the global agricultural trade market, accurate forecasting plays a key role in supporting agricultural policy formulation, stabilising the market and optimising resource allocation. In order to increase the precision and stability of agricultural trade predictions, this research suggests a hybrid model built on a temporal convolution network (TCN) and a lightweight gradient boosting tree (LightGBM). The TCN module effectively captures the long-term dependence characteristics of time series data through dilated convolution, which improves the model’s ability to identify seasonal and periodic trends. The LightGBM module, on the other hand, makes use of the characteristics of gradient boosting decision trees and excels at efficiently handling nonlinear relationships and avoiding overfitting. Experimental results show that the TCN-LightGBM model outperforms traditional models in terms of mean square error (MSE), mean absolute error (MAE) and prediction accuracy. Specifically, compared with ARIMA, LSTM, TCN alone or LightGBM alone, TCN-LightGBM achieves a prediction accuracy of 91.3% on the test data, with MSE and MAE of 0.021 and 0.115 respectively, significantly improving prediction accuracy and stability. In addition, parameter sensitivity analysis shows that the TCN-LightGBM model maintains a highly consistent prediction trend under different parameter configurations, which verifies the robustness of the model and its practical application value. This study provides a data-driven decision support tool with high accuracy and strong stability, providing a new solution for agricultural trade forecasting and other complex time series prediction tasks.

412 Awardee Talk: Implementing student-centered teaching and learning strategies in undergraduate animal science

Abstract Student-centered teaching and learning strategies enhance active engagement, foster critical thinking, and promote the practical application of knowledge gained from undergraduate animal science courses. The scholarship of teaching and learning (SOTL) has evolved within the animal science community, with a recent focus on adopting evidence-based teaching activities in college classrooms. We discuss documented SOTL approaches utilized in our program for the development of student-centered teaching and learning. In addition to traditional internships and student research projects, capstone projects utilizing a design thinking approach to solving animal industry problems have proven significantly effective. This problem-based learning program enables students to develop industry networks, understand problems within the industry, and develop creative ways to address the problems. Non-conventional and innovative designs of course assignments can foster student interest and enhance learning experiences. For example, by incorporating engaging and novel resources such as animal industry webinars and podcasts as classroom materials, instructors can reinforce course content and captivate students’ attention to the topic. Specifically, students have appreciated the opportunity to learn from experts not available locally at the university. Developing and utilizing real-world case studies exposes students to farm practices and related decision-making. Involving students in competitive team activities like the Dairy Challenge and Academic Quadrathlon contests develops marketable skills for students while training them to implement the knowledge acquired in the classrooms on a commercial farm. Field trips, short courses, and summer schools provide hands-on training opportunities, enhancing students’ exposure to the industry and opportunities after graduation. Students have identified that animal science topics are relevant beyond the classroom walls in everyday life including grocery store and kitchen, as demonstrated by a significant increase in enrollment of non-animal science major students in the animal science courses. The student-centered programs like Cross-training students in data-driven decision support tools help to develop strong day-one competency of graduates through training and networking within and outside the discipline. With the advent of new technologies and tools, training programs surrounding data management and interpretation should integrate systems knowledge across plant science, animal science, and computer science disciplines. These interdisciplinary and cross-disciplinary training activities promote the exchange of ideas while developing systems-level understanding. Furthermore, initiatives like Collaborative Online International Learning (COIL) provide students with exposure to diverse thoughts and situations, broadening their horizons and perspectives. For students not able to participate in study abroad and international programs due to time and financial constraints, COIL delivers cost-effective virtual opportunities to develop global understanding and cultural competency. By incorporating these student-centered teaching and learning strategies, undergraduate animal science programs can effectively prepare students for real-world challenges for the next generation of agriculture leaders.

Innovative regression model-based decision support tool for optimizing radiotherapy techniques in thoracic esophageal cancer.

Modern radiotherapy exemplified by intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT), has transformed esophageal cancer treatment. Facing challenges in treating thoracic esophageal cancer near vital organs, this study introduces a regression model-based decision support tool for the optimal selection of radiotherapy techniques. We enrolled 106 patients diagnosed with locally advanced thoracic esophageal cancer in this study and designed individualized IMRT and VMAT radiotherapy plans for each patient. Detailed dosimetric analysis was performed to evaluate the differences in dose distribution between the two radiotherapy techniques across various thoracic regions. Single-factor and multifactorial logistic regression analyses were employed to establish predictive models (P1 and P2) and factors such as TLV/PTV ratio. These models were used to predict the compliance and potential advantages of IMRT and VMAT plans. External validation was performed in a validation group of 30 patients. Using predictive models, we developed a data-driven decision support tool. For upper thoracic cases, VMAT plans were recommended; for middle/lower thoracic cases, the tool guided VMAT/IMRT choices based on TLV/PTV ratio. Models P1 and P2 assessed IMRT and VMAT compliance. In validation, the tool showed high specificity (90.91%) and sensitivity (78.95%), differentiating IMRT and VMAT plans. Balanced performance in compliance assessment demonstrated tool reliability. In summary, our regression model-based decision support tool provides practical guidance for selecting optimal radiotherapy techniques for thoracic esophageal cancer patients. Despite a limited sample size, the tool demonstrates potential clinical benefits, alleviating manual planning burdens and ensuring precise, individualized treatment decisions for patients.

Data-Driven Decision Support Tool Co-Development with a Primary Health Care Practice Based Learning Network

Background The Alliance for Healthier Communities is a learning health system that supports Community Health Centres (CHCs) across Ontario, Canada to provide team-based primary health care to people who otherwise experience barriers to care. This case study describes the ongoing process and lessons learned from the first Alliance for Healthier Communities’ Practice Based Learning Network (PBLN) data-driven decision support tool co-development project. Methods We employ an iterative approach to problem identification and methods development for the decision support tool, moving between discussion sessions and case studies with CHC electronic health record (EHR) data. We summarize our work to date in terms of six stages: population-level descriptive-exploratory study, PBLN team engagement, decision support tool problem selection, sandbox case study 1: individual-level risk predictions, sandbox case study 2: population-level planning predictions, project recap and next steps decision. Results The population-level study provided an initial point of engagement to consider how clients are (not) represented in EHR data and to inform problem selection and methodological decisions thereafter. We identified three initial meaningful types of decision support, with target application areas: risk prediction/screening, triaging specialized program referrals, and identifying care access needs. Based on feasibility and expected impact, we started with the goal to support earlier identification of mental health decline after diabetes diagnosis. As discussions deepened around clinical use cases associated with example prediction task set ups, the target problem evolved towards supporting the upstream task of organizational planning and advocacy for adequate mental health care service capacity to meet incoming needs. Conclusions This case study contributes towards a tool to support diabetes and mental health care, as well as lays groundwork for future CHC EHR-based decision support tool initiatives. We share lessons learned and reflections from our process that other primary health care organizations may use to inform their own co-development initiatives.

Medical Evaluation Readiness Information Toolset (MERIT): Developing a Data-driven Decision Support Tool to Augment Complex Clinical Decisions.

Medical readiness continues to be a significant concern for the military. DoD policy directs medical authorities to refer service members to the Disability Evaluation System (DES) when the course of further recovery is relatively predictable or within 1 year of diagnosis, whichever is sooner. The Medical Evaluation Readiness Information Toolset (MERIT) is an application that leverages artificial intelligence within a clinical decision support tool to provide clinicians with predictions of a service member's likelihood of referral to the DES for the top 24 medical conditions that result in separation from the service, which represent more than 90% of all referral cases to the DES since 2000. Data spanned 19 years and contained records for over 3 million army service members. The MERIT team incorporated a novel approach using a Gamma window function to weight recent medical data more than older medical data in the creation of a "Disease Severity Index" (DSI) that summarized the progression of a health deterioration process per medical condition code. Time-dependent medical encounter data were aggregated into an individual-level DSI. The identified features including the DSI were used in logistic regression and random forest models to predict whether a service member is likely to be referred to the DES. Models were constructed for each of the top 24 unfitting medical conditions. MERIT produced a set of high-performing classification models with area under the receiver operating characteristics curves across all conditions exceeding 0.919 using logistic regression for all conditions. This project demonstrated with a high degree of accuracy that MERIT, using a combination of ICD codes and personnel records, can be used to develop an individual risk profile for each service member.

A data-driven decision support tool to improve hospital bed cleaning logistics using discrete event simulation considering operators’ behaviour

Beds are a critical resource for hospitals, requiring effective management to ensure the quality of care for patients. Beds operate in a closed-loop circuit and must be thoroughly cleaned between patients’ arrivals to prevent infections. Hospitals must implement efficient logistics systems to collect, transport, store, and clean unclean beds from discharged patients. These systems must be robust and efficient to meet the varying bed supply needs, given the available resources such as beds, staff and machines. This study aims to develop a decision support tool to optimise bed cleaning logistics and ensure the availability of sterile beds for incoming patients at all times. The study is based on the bed flow and cleaning organisation of a Danish public hospital. A discrete event simulation model (DES) of the back-end bed flow has been developed. The paper also presents a tension level indicator to reflect the behaviour of cleaning staff when facing variations in demand and bed stock. Using the organisational set-up (staff schedules, policies, and bed fleet size), the DES model: (1) evaluates the ability to provide sterile beds in a reasonable time, (2) measures the stress on cleaning staff, and (3) visualises resource usage. This study illustrates how to incorporate the staff’s perceived workload and resulting behaviour into a DES model to capture the behavioural aspect of staff’s decision-making.

A shared decision-making tool to support the decision of patients with Lumbar Disc Herniation on whether to undergo surgery: a pilot study

This study aims to develop a decision support tool for patients and surgeons dealing with the uncertainty of surgical outcomes and the expectations of both parties for treating symptomatic lumbar disc herniation (LDH). The study uses a mixed-methods approach with Markov Chains (MC) and Analytic Hierarchy Process (AHP) to predict future health states after surgery for LDH, based on patient-reported outcomes measures (PROMs) and custom weights to each individual PROM and elicited priorities. A case-based analysis of two patients is presented to demonstrate the utility of the model in providing a likely trajectory that priority PROMs will follow over time. The study was conducted under the STROBE guidelines as a post hoc analysis of a large spine outcomes research study conducted in southern Brazil. Data were collected from patients operated between 2006 and 2017 to assess pain, disability, mood, and general health status from the preoperative time point to 1 year after surgery using patient-reported outcome questionnaires. The output of the algorithm represented the chances of surgery fulfilling the expectations of Patients A and B. The results presented by the tool suggest that Patient A will have a considerably higher probability of satisfaction and/or not meeting expectations with surgery than Patient B. The study demonstrates the feasibility and utility of a data-driven decision support tool that takes into account patient preferences and beliefs in generating high-quality decision-making and more utility for long-term outcomes in treating symptomatic lumbar disc herniation.

A data-driven decision support tool for public transport service analysis and provision

Public transport service (PTS) analysis and provision is an important and challenging issue for public transport agencies. The results of the PTS analysis help transport planners to identify the areas in need of PTS improvement. Furthermore, relevant policy actions need to be determined for service provision to reach the desired level of PTS improvement in the identified areas. Without an appropriate decision support tool, planners need to apply several blind trials to find a policy action which improves the PTS in the examined areas. This paper introduces a data-driven decision support tool for PTS analysis and provision. The proposed framework combines a potentially large number of PTS measures while taking the correlation among the investigated measures into account and develops high-dimensional supervised classification models that predict the PTS levels for different policy actions. With this approach, planners can identify and prioritize the areas in need of PTS improvement, determine what policy actions should be targeted to improve the PTS in the identified areas, and predict the PTS impacts of these policy actions in the examined areas. The application of the proposed framework is demonstrated in detail through a case study of Budapest, Hungary, which is followed by a hypothetical policy implementation. The results show that mostly outskirts are in need of PTS improvement. Furthermore, the underlying reasons behind the areas with poor overall PTS are studied to target the relevant policy actions that improve the PTS in the identified areas. The PTS impacts of the targeted policy actions are studied by using the developed high-dimensional supervised classification models.

Predicting CTS Diagnosis and Prognosis Based on Machine Learning Techniques.

Carpal tunnel syndrome (CTS) is a clinical disease that occurs due to compression of the median nerve in the carpal tunnel. The determination of the severity of carpal tunnel syndrome is essential to provide appropriate therapeutic interventions. Machine learning (ML)-based modeling can be used to classify diseases, make decisions, and create new therapeutic interventions. It is also used in medical research to implement predictive models. However, despite the growth in medical research based on ML and Deep Learning (DL), CTS research is still relatively scarce. While a few studies have developed models to predict diagnosis of CTS, no ML model has been presented to classify the severity of CTS based on comprehensive clinical data. Therefore, this study developed new classification models for determining CTS severity using ML algorithms. This study included 80 patients with other diseases that have an overlap in symptoms with CTS, such as cervical radiculopathysasas, de quervian tendinopathy, and peripheral neuropathy, and 80 CTS patients who underwent ultrasonography (US)-guided median nerve hydrodissection. CTS severity was classified into mild, moderate, and severe grades. In our study, we aggregated the data from CTS patients and patients with other diseases that have an overlap in symptoms with CTS, such as cervical radiculopathysasas, de quervian tendinopathy, and peripheral neuropathy. The dataset was randomly split into training and test data, at 70% and 30%, respectively. The proposed model achieved promising results of 0.955%, 0.963%, and 0.919% in terms of classification accuracy, precision, and recall, respectively. In addition, we developed a machine learning model that predicts the probability of a patient improving after the hydro-dissection injection process based on the aggregated data after three different months (one, three, and six). The proposed model achieved accuracy after six months of 0.912%, after three months of 0.901%, and after one month 0.877%. The overall performance for predicting the prognosis after six months outperforms the prediction after one and three months. We utilized statistics tests (significance test, Spearman's correlation test, and two-way ANOVA test) to determine the effect of injection process in CTS treatment. Our data-driven decision support tools can be used to help determine which patients to operate on in order to avoid the associated risks and expenses of surgery.

Utilizing big data from electronic health records in pediatric clinical care.

Big data has the capacity to transform both pediatric healthcare delivery and research, but its potential has yet to be fully realized. Curation of large multi-institutional datasets of high-quality data has allowed for significant advances in the timeliness of quality improvement efforts. Improved access to large datasets and computational power have also paved the way for the development of high-performing, data-driven decision support tools and precision medicine approaches. However, implementation of these approaches and tools into pediatric practice has been hindered by challenges in our ability to adequately capture the heterogeneity of the pediatric population as well as the nuanced complexities of pediatric diseases such as sepsis. Moreover, there are large gaps in knowledge and definitive evidence demonstrating the utility, usability, and effectiveness of these types of tools in pediatric practice, which presents significant challenges to provider willingness to leverage these solutions. The next wave of transformation for pediatric healthcare delivery and research through big data and sophisticated analytics will require focusing efforts on strategies to overcome cultural barriers to adoption and acceptance. IMPACT: Big data from EHRs can be used to drive improvement in pediatric clinical care. Clinical decision support, artificial intelligence, machine learning, and precision medicine can transform pediatric care using big data from the EHR. This article provides a review of barriers and enablers for the effective use of data analytics in pediatric clinical care using pediatric sepsis as a use case. The impact of this review is that it will inform influencers of pediatric care about the importance of current trends in data analytics and its use in improving outcomes of care through EHR-based strategies.

Data-driven decision support tool for production planning: a framework combining association rules and simulation

Nowadays, guaranteeing the highest product variety in the shortest delivery time represents one of the main challenges for most of industries. The dynamic contexts where they have to compete push them to quickly readapt their processes, increasing the need for reactive decision-support tools to identify targeted actions to improve performance. Starting from the analysis of existing decision-support tools separately adopting simulation or data mining techniques, a framework that combines Association Rule Mining (ARM) and simulation has been developed to capitalize on the benefits brought by both techniques. On the one hand, ARM supports companies in identifying the main criticalities that slow down production processes, such as different causes of stoppage, giving a priority ranking of interventions. On the other hand, data-driven simulation is used to validate the ARM results and to conduct scenario analyses to compare the KPIs values resulting from different configurations of the production processes. Once the best-impacting mitigating actions have been implemented, the proposed framework can be iteratively used to define an updated set of intervention areas to enhance, promoting continuous improvement. This data-driven approach represents the key value of the framework, guaranteeing its easy-to-readapt and iteratively application. Theoretical contributions refer to the use of simulation with ARM not only to validate relations but to perform scenario analyses in an iterative way, as well as to the novelty application in a low-tech sector. From a practical point of view, a case study in the fashion industry demonstrates the usability and reliability of the proposed framework.

Technology enabled data driven decision support tools for capital projects

Technology enabled data driven decision support tools for capital projects

AIS explorer: Prioritization for watercraft inspections-A decision-support tool for aquatic invasive species management

Invasions of aquatic invasive species have caused significant economic and ecological damage to global aquatic ecosystems. Once an invasive population has established in a new habitat, eradication can be financially and logistically impossible, motivating management strategies to rely heavily upon prevention measures to reduce the introduction and spread. To be productive, on-the-ground management of aquatic invasive species requires effective decision-making surrounding the allocation of limited resources. Watercraft inspections play an important role in managing aquatic invasive species by preventing the overland transport of invasive species between waterbodies and providing education to boaters. In this study, we developed and tested an interactive web-based decision-support tool, AIS Explorer: Prioritization for Watercraft Inspections, to guide AIS managers in developing efficient watercraft inspection plans. The decision-support tool is informed by a network-based algorithm that maximized the number of inspected watercraft that move from AIS infested to uninfested lakes within and between counties in Minnesota, USA. It was iteratively built with stakeholder feedback, including consultations with county managers, beta-testing of the web-based application, and workshops to educate and train end-users. The co-development and implementation of data-driven decision support tools demonstrate how interdisciplinary methods can be used to connect science and management to support decision-making. The AIS Explorer: Prioritization for Watercraft Inspections application makes optimized research outputs accessible in multiple dynamic forms that maintain pace with discovery of new infestations and local needs. In addition, the decision support tool has supported improved and closer communication between AIS managers and researchers on this topic.

Efficient social distancing during the COVID-19 pandemic: Integrating economic and public health considerations

Although social distancing can effectively contain the spread of infectious diseases by reducing social interactions, it may have economic effects. Crises such as the COVID-19 pandemic create dilemmas for policymakers because the long-term implementation of restrictive social distancing policies may cause massive economic damage and ultimately harm healthcare systems. This paper proposes an epidemic control framework that policymakers can use as a data-driven decision support tool for setting efficient social distancing targets. The framework addresses three aspects of the COVID-19 pandemic that are related to social distancing or community mobility data: modeling, financial implications, and policy-making. Thus, we explore the COVID-19 pandemic and concurrent economic situation as functions of historical pandemic data and mobility control. This approach allows us to formulate an efficient social distancing policy as a stochastic feedback control problem that minimizes the aggregated risks of disease transmission and economic volatility. We further demonstrate the use of a deep learning algorithm to solve this control problem. Finally, by applying our framework to U.S. data, we empirically examine the efficiency of the U.S. social distancing policy.

Simulation analysis of critical factors of casualty transportation for disaster response: a case study of Istanbul earthquake

PurposeThe purpose of this paper is to statistically assess the effects of the design factors including usage of data-driven decision support tool (DST), classification of patients (triage), prioritization based on vital scores of patients, number of ambulances and hospital selection rules, on the casualty transportation system’s performance in large-scale disasters. Besides, a data-driven DST for casualty transportation is proposed to enhance the casualty survival and ambulance transportation times during the disaster response stage.Design/methodology/approachIn this study, the authors applied simulation and statistical analysis to evaluate the effects of usage of data-driven DST, classification of patients (triage), prioritization of the patients based on vital scores, number of ambulances and hospital selection rules, on the patient survival and transportation time of the casualty transportation system. An experimental design was made, and 16 scenarios were formulated. Simulation models were developed for all scenarios. The number of unrecoverable casualties and time-spent by the casualties until arriving at the hospital was observed. Then, a statistical analysis was applied to the simulation results, and significant factors were determined.FindingsUtilization of the proposed DST was found to improve the casualty transportation and coordination performance. All main effects of the design factors were found statistically significant for the number of unrecoverable casualties. Besides, for the Time spent Until Arrival of T1-Type Casualty at the Hospital, all of the main factors are significant except the number of ambulances. Respiratory rate, pulse rate, motor response score priority and hospital selection rule based on available hospital capacities must be considered to reduce the number of unrecoverable casualties and time spent until arrival of the casualties at the hospitals.Originality/valueIn this study, the factors that significantly affect the performance of the casualty transportation system were revealed, by simulation and statistical analysis, based on an expected earthquake case, in a metropolitan city. Besides, it was shown that using a data-driven DST that tracks victims and intends to support disaster coordination centers and medical staff performing casualty transportation significantly improves survival rate of the victims and time to deliver the casualties. This research considers the whole systems’ components, contributes to developing the response stage operations by filling gaps between using the data-driven DST and casualty transportation processes.

Data-driven decision support tools for assessing the vulnerability of community water systems to groundwater contamination in Los Angeles County

Regulatory bodies that monitor community water systems (CWS) can be strained for time and resources, making it difficult to take science-informed preventative action before contamination affects local populations. This research aims to develop an easy to deploy, data-driven method to identify CWS that are vulnerable to drinking water quality degradation from groundwater contamination. We focus on a case study of Los Angeles County (LA County) in California to explore the utility of machine learning methods, specifically random forest models (RF) and artificial neural networks (ANN), as quickly deployable decision support tools based on publicly available data. We also aim to provide insight into which factors contribute to vulnerability to groundwater contamination, which may be useful in understanding how vulnerability to contamination emerges. The results of this analysis, which are based entirely on publicly available data, can help policymakers and planners target specific systems, as well as better tailor the type of support needed. We find that both RF and ANN methods can produce relatively low prediction errors but differ in what they predict and how they weigh the relative importance of input variables. The results also suggest that model results can provide stakeholders with a starting point for prioritizing at-risk service areas, but it is important to remember that the model results are most useful in combination with expert opinion.

Artefact Detection in Impedance Pneumography Signals: A Machine Learning Approach.

Impedance pneumography has been suggested as an ambulatory technique for the monitoring of respiratory diseases. However, its ambulatory nature makes the recordings more prone to noise sources. It is important that such noisy segments are identified and removed, since they could have a huge impact on the performance of data-driven decision support tools. In this study, we investigated the added value of machine learning algorithms to separate clean from noisy bio-impedance signals. We compared three approaches: a heuristic algorithm, a feature-based classification model (SVM) and a convolutional neural network (CNN). The dataset consists of 47 chronic obstructive pulmonary disease patients who performed an inspiratory threshold loading protocol. During this protocol, their respiration was recorded with a bio-impedance device and a spirometer, which served as a gold standard. Four annotators scored the signals for the presence of artefacts, based on the reference signal. We have shown that the accuracy of both machine learning approaches (SVM: 87.77 ± 2.64% and CNN: 87.20 ± 2.78%) is significantly higher, compared to the heuristic approach (84.69 ± 2.32%). Moreover, no significant differences could be observed between the two machine learning approaches. The feature-based and neural network model obtained a respective AUC of % and %. These findings show that a data-driven approach could be beneficial for the task of artefact detection in respiratory thoracic bio-impedance signals.

Progress towards clinically informative data-driven decision support tools in psychotherapy

Timely identification of patients receiving psychotherapy who are elevated risk of a poor outcome can enable clinicians to intervene early, make appropriate treatment adjustments, and ultimately improve outcomes. In many real-world settings, this process is done intuitively and idiosyncratically, but studies have shown that providing therapists with regular, structured feedback about patients' treatment progress can improve outcomes, in particular for patients who are classified as not on track and therefore at increased risk of poor outcomes.

Multi-Objective Two-Stage Stochastic Programming Model for a Proposed Casualty Transportation System in Large-Scale Disasters: A Case Study

Disaster management is a process that includes mitigation, preparedness, response and recovery stages. Operational strategies covering all stages must be developed in order to alleviate the negative effects of the disasters. In this study, we aimed at minimizing the number of casualties that could not be transported to the hospitals after the disaster, the number of additional ambulances required in the response stage, and the total transportation time. Besides, we assumed that a data-driven decision support tool is employed to track casualties and up-to-date hospital capacities, so as to direct the ambulances to the available hospitals. For this purpose, a multi-objective two-stage stochastic programming model was developed. The model was applied to a district in Istanbul city of Turkey, for a major earthquake. Accordingly, the model was developed with a holistic perspective with multiple objectives, periods and locations. The developed multi-objective stochastic programming model was solved using an improved version of the augmented ε-constraint (AUGMECON2) method. Hence, the Pareto optimal solutions set has been obtained and compared with the best solution achieved according to the objective of total transportation time, to see the effect of the ambulance direction decisions based on hospital capacity availability. All of the decisions examined in these comparisons were evaluated in terms of effectiveness and equity. Finally, managerial implication strategies were presented to contribute decision-makers according to the results obtained. Results showed that without implementing a data-driven decision support tool, equity in casualty transportation cannot be achieved among the demand points.

A Data-Driven Decision Support Tool for Offshore Oil and Gas Decommissioning

A growing number of oil and gas offshore infrastructures across the globe are approaching the end of their operational life. It is a major challenge for the industry to plan and make a decision on the decommissioning as the processes are resource exhaustive. Whether a facility is completely removed, partially removed or left in-situ, each option will affect individual parties differently. Stakeholders’ concerns and needs are collected and analyzed to obtain the most compromised decommissioning decision. Engaging with hundreds of stakeholders is extremely complicated, hence time-consuming and costly. This issue can be addressed using a predictive model to provide suggested decommissioning options based on the data of previously approved projects. However, the lack of readily available relevant datasets is the main hindrance of such an approach. In this paper, we introduce a new oil and gas decommissioning dataset extensively covering all types of offshore infrastructures in the UK landscape over a 21-year period. An experimental framework using several learning algorithms on the new dataset for predicting the decommissioning option is presented. Various resampling methods were applied to tackle the imbalanced class distribution of the dataset for improved classification. Promising results were achieved despite the exclusion of some stakeholder-related features used in the traditional approach. This shows signs of a potential solution for the industry to significantly reduce time and cost spent on a decommissioning project, and encourages more efforts put into researching on this timely topic.