Decision Support Model Research Articles

A decision-support model through online reviews: Consumer preference analysis and product ranking

Online reviews have become an important reference for consumers when choosing and purchasing products (or services); however, previous research based on these reviews has overlooked consumer preferences at the attribute level, as well as product ranking at the regional level. Here, we propose a decision-support model through online reviews involving 4 modules: (i) data collection and preprocessing, (ii) transformation from star ratings into probabilistic linguistic term sets (PLTSs), (iii) consumer preference analysis, and (iv) product ranking. After data collection and preprocessing, star ratings are represented as probabilistic linguistic term sets. Then, the most satisfactory product attribute and the least satisfactory product attribute are extracted from text comments using the well-known term-frequency-inverse document frequency (TF-IDF) algorithm. Thereafter, a data-driven best-worst method, in which preferences are given by objectively quantifying multiple product attributes based on star ratings and the weightage score obtained from the TF-IDF results, is established to analyze consumer preferences. Furthermore, product ranking is investigated from two levels: a region-based ranking method to discuss product ranking over different regions and a ranking position aggregation method to discuss product overall ranking based on a 0-1 integer programming model. We verify the decision-support model with a dataset of 10987 reviews of 20 automobile models from the Autohome platform. Some comparisons, theoretical and practical implications are provided.

Prediction of Radio Signal Failures of Communication Based Train Operating Systems by Machine Learning Methods

The use of rail systems in urban public transportation has become a necessity for reasons such as time saving, travel comfort and operating costs, especially in cities with high population and road traffic. Communication based train operating systems (CBTC) are used for the safe use of urban rail systems and the maximum capacity of the railway line. In this study, in line with the data collected from the trains on a railway line operated with CBTC, the status of the radio signals that enable the wireless communication of the trains with the trackside signaling equipment was evaluated by machine learning methods, and the situations that may have negative effects on the train operations of the problems at the signal level were evaluate. The problems on the antennas which receives signals from trackside above trains, the poor connection related with fiber optical and LAN cables, the trackside transmitter antenna orientation problems causes decrease on signal levels. It is aimed that to take actions about the problematic signal levels without any negative impact on the passenger comfort and the operation yet. The radio signal losses cause unexpected trains stops and delays. A decision support model has been developed that will offer early solution suggestions to system maintainers in order to intervene first. In conclusion, since it is the first study related with failure prediction by using radio signal levels data on railway signaling system, this study presents an important innovation in terms of literature.

Least-cost or sustainable? Exploring power sector transition pathways

The 2022 energy crisis augmented the European ambition for RES capacity expansion. However, beyond the issue of “how many” renewables, the research question that remains, is which capacity mixes achieve the emissions reduction, renewable integration, and energy autarky targets in a cost-effective way. This paper tries to answer this inquiry supported by two energy models and one decision support model. The analysis focuses on Greece, as an example country which opted for lignite phase out, turning to temporary heavy reliance on natural gas for power generation. The results indicate that even though from the energy and environmental targets’ perspective, more wind turbines in the RES capacity mix yield better results, when incorporating the economic point of view, balanced wind and solar mixes enable the achievement of targets in a cost-effective way. The most cost-efficient mixes consist of 50–55% wind and 45–50% solar capacity, with cost per additional percentage of RES share in the generation mix, equal to 40 million €. Nevertheless, in order to materialize them in a timely manner, and limit the short-term dependency on imported gas, permitting simplification, tenders for large-scale projects and incentives for small-scale projects are needed to facilitate accelerated RES deployment.

Group decision support system based on multi-granular fractional orthotriple fuzzy 2-tuple linguistic information model

The aim of this work is to put forward the concept of a novel decision support model for multi-criteria group decision making problems with multi-granular fractional orthotriple fuzzy 2-tuple linguistic (FOF2TL) information. The idea of fractional orthotriple fuzzy 2-tuple linguistic (2TL) model is put forward by integrating the advantages of fractional orthotriple fuzzy set and 2-tuple linguistic method. We define a transformation function to deal the consistency of multi-granular FOF2TL information. Secondly, the Archimedean coupla as well as the co-coupla are used to define operational laws for fractional orthotriple fuzzy 2-tuple linguistic numbers (FOF2TLNs). We take the relationship between attribute and experts, and defined fractional orthotriple fuzzy 2-tuple linguistic Banzhaf Choquet-Copula aggregation (FOF2TLBCCA) operators. Third, a method explains how to determine the fuzzy measure of expert and attribute sets. Next, the suggested operators are used to develop associated decision-making approaches. Finally, a numerical case is offered to demonstrate the concept's usefulness, along with pertinent comments; the merits of the suggested methodology are also evaluated by comparing it to several existing decision approaches.

A Review of Game Theory Models to Support Production Planning, Scheduling, Cloud Manufacturing and Sustainable Production Systems

Cyber-physical systems, cloud computing, the Internet of Things, and big data play significant roles in shaping digital and automated landscape manufacturing. However, to fully realize the potential of these technologies and achieve tangible benefits, such as reduced manufacturing lead times, improved product quality, and enhanced organizational performance, new decision support models need development. Game theory offers a promising approach to address multi-objective problems and streamline decision-making processes, thereby reducing computational time. This paper aims to provide a comprehensive and up-to-date systematic review of the literature on the application of game theory models in various areas of digital manufacturing, including production and capacity planning, scheduling, sustainable production systems, and cloud manufacturing. This review identifies key research themes that have been explored and examines the main research gaps that exist within these domains. Furthermore, this paper outlines potential future research directions to inspire both researchers and practitioners to further explore and develop game theory models that can effectively support the digital transformation of manufacturing systems.

A simulation-based performance evaluation model for decision support on drone location and delivery scheduling

PurposeThis paper aims to propose a simulation-based performance evaluation model for the drone-based delivery of aid items to disaster-affected areas. The objective of the model is to perform analytical studies, evaluate the performance of drone delivery systems for humanitarian logistics and can support the decision-making on the operational design of the system – on where to locate drone take-off points and on assignment and scheduling of delivery tasks to drones.Design/methodology/approachThis simulation model captures the dynamics and variabilities of the drone-based delivery system, including demand rates, location of demand points, time-dependent parameters and possible failures of drones’ operations. An optimization model integrated with the simulation system can update the optimality of drones’ schedules and delivery assignments.FindingsAn extensive set of experiments was performed to evaluate alternative strategies to demonstrate the effectiveness for the proposed optimization/simulation system. In the first set of experiments, the authors use the simulation-based evaluation tool for a case study for Central Florida. The goal of this set of experiments is to show how the proposed system can be used for decision-making and decision-support. The second set of experiments presents a series of numerical studies for a set of randomly generated instances.Originality/valueThe goal is to develop a simulation system that can allow one to evaluate performance of drone-based delivery systems, accounting for the uncertainties through simulations of real-life drone delivery flights. The proposed simulation model captures the variations in different system parameters, including interval of updating the system after receiving new information, demand parameters: the demand rate and their spatial distribution (i.e. their locations), service time parameters: travel times, setup and loading times, payload drop-off times and repair times and drone energy level: battery’s energy is impacted and requires battery change/recharging while flying.

A Decision Support System for Sustainable Agriculture and Food Loss Reduction under Uncertain Agricultural Policy Frameworks

The EU Green Deal requires the reduction in pesticides and fertilisers in food crop production, whilst the sustainable development goals require reductions in food loss and food waste. In a complex and interacting system like the food system, these goals are difficult to coordinate. Here, we show an approach using Bayesian network modelling for decision support. Bayesian networks are important tools for modelling complex systems which may develop emergent behaviour and for providing quantitative comparisons for different candidate policies, approaches or interventions under the Integrating Decision Support System paradigm. Using lettuce as an exemplar crop, we demonstrate that expected food loss changes under different agricultural input reduction and integrated pest management combinations can be quantified to aid decision making for growers.

Machine Learning First Response to COVID-19: A Systematic Literature Review of Clinical Decision Assistance Approaches during Pandemic Years from 2020 to 2022

Background: The declaration of the COVID-19 pandemic triggered global efforts to control and manage the virus impact. Scientists and researchers have been strongly involved in developing effective strategies that can help policy makers and healthcare systems both to monitor the spread and to mitigate the impact of the COVID-19 pandemic. Machine Learning (ML) and Artificial Intelligence (AI) have been applied in several fronts of the fight. Foremost is diagnostic assistance, encompassing patient triage, prediction of ICU admission and mortality, identification of mortality risk factors, and discovering treatment drugs and vaccines. Objective: This systematic review aims to identify original research studies involving actual patient data to construct ML- and AI-based models for clinical decision support for early response during the pandemic years. Methods: Following the PRISMA methodology, two large academic research publication indexing databases were searched to investigate the use of ML-based technologies and their applications in healthcare to combat the COVID-19 pandemic. Results: The literature search returned more than 1000 papers; 220 were selected according to specific criteria. The selected studies illustrate the usefulness of ML with respect to supporting healthcare professionals for (1) triage of patients depending on disease severity, (2) predicting admission to hospital or Intensive Care Units (ICUs), (3) search for new or repurposed treatments and (4) the identification of mortality risk factors. Conclusion: The ML/AI research community was able to propose and develop a wide variety of solutions for predicting mortality, hospitalizations and treatment recommendations for patients with COVID-19 diagnostic, opening the door for further integration of ML in clinical practices fighting this and forecoming pandemics. However, the translation to the clinical practice is impeded by the heterogeneity of both the datasets and the methodological and computational approaches. The literature lacks robust model validations supporting this desired translation.

Multi-criteria decision support model for material and supplier selection in the construction industry

Existing material and supplier selection practices in the construction industry are time-consuming and error-prone due to reliance on human judgment and personal communication with a limited number of suppliers. To address this problem, the present paper focuses on developing a new Material and Supplier Selection Model (MSSM) to identify the best selection of construction materials and suppliers based on: (1) unique characteristics of construction projects such as project location, quantity of work, and material/product specifications; (2) characteristics of suppliers and vendors such as price, delivery time, and quality; and (3) characteristics of materials such as performance, and environmental impacts. The model is integrated with updatable databases of materials and suppliers that include: (1) suppliers’ data such as name, location, and lead time; and (2) materials data such as model, specifications, and costs. The developed MSSM is implemented using the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) method due to its capability of executing computations for problems containing both qualitative and quantitative criteria. An example project for material and supplier selection is used to demonstrate the MSSM use and document its new capabilities. The present model is expected to support contractors, construction managers, and decision-makers in selecting the best suppliers and materials based on specified criteria such as cost, time, and environmental impacts.

Personalized decision support system for tailoring IgA nephropathy treatment strategies

BackgroundThe ongoing debate surrounding the use of immunosuppressive treatments for IgA nephropathy (IgAN) underscores the demand for personalized and effective strategies. MethodsAnalyzed data from 807 IgAN patients over 5+ years using three methods: Random Forest with molecular biomarkers, network biomarkers with graph engineering, and an auto-encoder model. All models were trained using identical demographic, clinical, and pathological data, employing an 80–20 split for training and testing purposes. ResultsIn the comprehensive assessment of IgAN prognosis, the Random Forest model, employing molecular biomarkers, demonstrated strong performance metrics (AUC = 0.83, sensitivity = 0.51, specificity = 0.96). However, traditional graph feature engineering on patient-specific networks outperformed these results with an AUC of 0.90, sensitivity of 0.64, and specificity of 0.94. The Auto-encoder model showed the best accuracy (AUC = 0.91, sensitivity = 0.46, specificity = 0.96). The findings highlighted the superior predictive capabilities of network biomarkers over molecular biomarkers for adverse renal outcome prediction in IgAN. Consequently, we integrated Auto-encoder-derived Network Biomarkers with Random Forest Models to enhance prognostic precision in diverse IgAN treatment scenarios. The prediction for the prognosis of patients receiving supportive care, glucocorticoid therapy, and immunosuppressant treatment yielded AUC values of 0.95, 0.96, and 1, respectively, indicating high specificity. Drawing from these insights, we pioneered the development of an innovative decision support model for IgAN treatment. This model demonstrated the ability to make medical decisions comparable to those by experienced nephrologists, enabling the customization of personalized disease management strategies. ConclusionOur system accurately predicted IgAN prognosis and evaluated various treatment efficacies, aiding physicians in devising optimal therapeutic strategies for patients.

A single-valued neutrosophic CIMAS-CRITIC-RBNAR decision support model for the financial performance analysis: A study of technology companies

Financial performance stands as a fundamental performance indicator that reflects companies' current financial conditions, providing information to investors and stakeholders about companies' financial well-being. In this research, a decision support system is developed for identifying the financial performance of companies traded on stock exchanges. This decision support system is based on a multicriteria decision-making (MCDM) approach and neutrosophic logic. The research introduces a novel method for calculating and ranking financial performance, using financial ratio indicators as selection criteria. The importance levels of financial ratio indicators are weighted through two different approaches. In the first approach, expert opinions and criteria importance assessment (CIMAS)-based on single-valued neutrosophic (SVN) sets are utilized for calculation. In the second approach, criteria importance through intercriteria correlation (CRITIC)-based on reference-based normalization processes is employed for criteria weighting. Subsequently, the results of the two criterion weightings are combined to calculate the final criterion weights. The SVN reference-based normalization alternative ranking (RBNAR) method is presented for ranking companies based on their financial performance. Thus, SVN-CIMAS-CRITIC-RBNAR is developed and its algorithm is presented. The novel hybrid decision support model is applied to a case study of technology companies traded on the Borsa Istanbul. The research results support the applicability of the SVN-CIMAS-CRITIC-RBNAR hybrid method. The results of the case study and sensitivity analyses affirm the applicability and robustness of the SVN-CIMAS-CRITIC-RBNAR hybrid model. The research provides detailed implications and insights for financial managers.

Matching decision support modeling frameworks to disease emergence stages and associated management objectives

AbstractWildlife disease management decisions often require rapid responses to situations that are fraught with uncertainty. By recognizing that management is implemented to achieve specific objectives, resource managers and science partners can identify an analysis technique and develop a monitoring plan to evaluate management effectiveness. For emerging infectious diseases, objectives may take several distinct forms, dependent on the perceived stage of disease emergence (i.e., pre‐epidemic, early outbreak, mid‐epidemic, and endemic), the expected rate of spread, and the anticipated effect of the disease on host populations. Identifying modeling techniques and metrics that are linked to management objectives will require early and consistent communication between managers and science partners. We link modeling approaches that can be used to forecast and evaluate the performance of intervention strategies with a range of disease management objectives. Our aim is to help scientists recognize alternative modeling approaches which may better align with different forms of disease management objectives, and to help managers evaluate the relevance of proposed modeling approaches to their specified objectives for disease management. Recognizing that disease management objectives can take different forms, and thus require different modeling approaches, can help wildlife disease response teams (i.e., natural resource managers, scientists, and stakeholders working collaboratively) better prepare and respond to disease threats.

Perancangan Sistem Pandukung Keputusan Pemilihan Perguruan Tinggi Swasta Di Kota Semarang Dengan Metode AHP Untuk Siswa SMK Bina Negara Gubug

The selection of universities has a very important role, at the end of school one of the problems often faced by students is determining where to continue the next level of education, including high school students, they find it difficult to determine which colleges and fields of study they will be capable of. Universities including private universities in the city of Semarang are increasingly making it difficult for students to determine private universities according to their criteria considering the difficulty of selection to enter public universities, in terms of quality or accreditation from these private universities, tuition fees, campus location close to boarding houses, places to eat, markets and a safe and conducive environment that supports them throughout their lives to study, distances that are easily accessible by private vehicles or public transportation, even the facilities obtained on campus are in accordance with the costs incurred or not, there are There are several criteria that are considered when choosing a private university.AHP is a decision support model developed by Thomas L. Saaty. This decision support model will describe a multi-factor problem or complex criteria into a hierarchical form. The AHP method is precise and systematic in the decision-making process according to the criteria set by Kopertis 6 based on data analysis. The research method used is R&D. Research and Development method is research that is used to produce certain products and test the effectiveness of these products. In this study the authors built a decision support system using the AHP method using 5 criteria (Cost, Facilities, Mileage, Quality, Location

Optimising business models through digital alignment and strategic flexibility: Evidence from the manufacturing industry

Abstract The increasing integration of digital technologies in business processes calls for a deeper understanding of their impact on business model efficiency. This study explores how digital alignment, composed by strategic decision support and operational support, affects business model efficiency, while also examining the extent to which strategic flexibility moderates this relationship. To test the proposed hypotheses, we adopt a quantitative approach on a sample of Italian small and medium-sized enterprises in the manufacturing industry. In particular, a regression analysis, complemented by a necessary condition analysis, is performed. We find that digital alignment, both in terms of strategic decision support and operational support, fosters business model efficiency. Strategic flexibility strengthens the relationship between strategic decision support and business model efficiency. To the best of our knowledge, this study is the first to operationalise digital alignment as composed of strategic decision support and operational support. Accordingly, this study contributes to the extant literature on digital alignment and business models.

A Decision Support Model for Mitigating Supply Chain Risks Based on a Modified FMEA, Multi-Objective Optimization and Multi-Criteria Decision-Making Approach

Abstract Recent years have seen a huge development in the subject of supply chain risk management. In this increasingly uncertain world, the use of practical and effective tools for decision making and risk mitigation has become more necessary than ever. In this research, mitigation strategies for a tier one multinational company operating in the automotive industry and providing an assembly operation to final customer Renault Tanger and Renault SOMACA were prioritized according to their effectiveness, as well as their implementation costs. Based on research in the literature and the opinions of experts in the field. 44 risks and 55 mitigation strategies were identified. FMEA (Failure Modes and Effects Analysis) method was used based on the latest AIAG 2019 edition to filter and identify the risks to be prioritized, we used then a multi-objective optimization approach to identify the mitigation strategies that constitute the Pareto front for each of the risks and finally used the EDAS method for the final ranking of the strategies. Our case revealed that strategies like ensuring elaborating a contingency planning and defining the responsibilities, imposing contractual obligations on subcontractors, applying a flexible supply contract were found to be relevant risk mitigation strategies for the company. Managers interested in mitigating risk can deploy this model to prioritize risk mitigation strategies.

Advances in the pilot point inverse method: Où En Sommes-Nous maintenant?

At a conference some years ago, one of the attendees came up to Ghislain de Marsily and asked, “Excuse me, but aren’t you the de Marsily who developed the Pilot Point Method? It is an honor to meet you.” His response highlights one of Ghislain’s greatest qualities, his humility: “Yes I am, thank you, you are very kind, but that was a very long time ago.” Since Ghislain de Marsily first developed the Pilot Point Method (PPM) in 1978, its development and use has grown significantly in applied decision-support modeling settings including hydrogeology, as well as in other industries, e.g., petroleum reservoir engineering. A technique that was once confined to academic realms, the PPM is now widely accepted as one of the industry pillars of inversion and uncertainty quantification for predictive groundwater modeling. Herein, we provide an update to de Marsily’s paper entitled “Four Decades of Inverse Problems in Hydrogeology” [De Marsily et al., 2000], but with a particular focus on the incredible adoption and advancement of de Marsily’s PPM and related inverse techniques over the last twenty years in the field of predictive groundwater modeling.

Quantitative methods for integrating climate adaptation strategies into spatial decision support models

With the onset of rapid climate change and the legacy of past forest management and fire suppression policies, the capacity for forested landscapes to maintain core functionality and processes is being challenged. As such, managers are tasked with increasing the pace and scale of management to mitigate negative impacts of future large disturbances and improve resilience and climate adaptation of large landscapes. Such efforts require consensus building, with partners and stakeholders to determine where to allocate scarce resources. We present a methodology to identify strategic (where to go) and tactical (what to do) priorities across large landscapes to assist in project level planning. The model integrates a spatial assessment of current ecosystem resource conditions and spatial outputs from a landscape succession and disturbance simulation model (LANDIS-II) to assess the potential to achieve desired conditions under climate change with ongoing disturbances. Based on the expected trajectory of landscape conditions over time, the model applies fuzzy logic modeling to provide quantitative support for four management strategies (Monitor, Protect, Adapt, and Transform) across the landscape. We provide an example application of these methods targeting sustainable carbon loads across a 970,000 ha landscape in the central Sierras in California. By including future landscape conditions in the model, decisions made at the stand-level are inherently tied to and influenced by larger landscape-level processes that are likely to have the greatest impact on future landscape dynamics. The methods outlined here are able to incorporate multiple metrics to capture the many resources targeted by management. Model outputs could also be used as inputs into spatial optimization models to assess tradeoffs and synergies among treatment options and to aid in long-term planning.

Alternative prioritization for mitigating urban transportation challenges using a Fermatean fuzzy-based intelligent decision support model

Practitioners and decision-makers often face difficulties in selecting and prioritizing effective strategies to address challenges to sustainable urban transportation development. Although there has been considerable research conducted on the subject, the Tanzanian context, which is greatly affected by social and environmental problems, has received inadequate attention. Therefore, this study intends to bridge this gap by pinpointing the obstacles to sustainable urban transportation and proposing the most appropriate strategies to tackle them. The study proposes seven strategies and determines five criteria to prioritize them. To accomplish this, the study proposes a novel Fermatean fuzzy-based intelligent decision support model to assess the criteria weights and prioritizes strategies based on the weighted criteria. The study validates the proposed methodology by conducting a sensitivity analysis, which indicates that restricting car use (A5), improving sector coordination (A1), and conducting extensive research on transportation issues (A7) are the top three strategies for promoting sustainable urban transportation. The study’s findings hold significant value in providing urban transportation planners with helpful guidance to develop optimization techniques that can improve transportation systems.

Machine learning decision support model for discharge planning in stroke patients.

Efficient discharge for stroke patients is crucial but challenging. The study aimed to develop early predictive models to explore which patient characteristics and variables significantly influence the discharge planning of patients, based on the data available within 24 h of admission. Prospective observational study. A prospective cohort was conducted at a university hospital with 523 patients hospitalised for stroke. We built and trained six different machine learning (ML) models, followed by testing and tuning those models to find the best-suited predictor for discharge disposition, dichotomized into home and non-home. To evaluate the accuracy, reliability and interpretability of the best-performing models, we identified and analysed the features that had the greatest impact on the predictions. In total, 523 patients met the inclusion criteria, with a mean age of 61 years. Of the patients with stroke, 30.01% had non-home discharge. Our model predicting non-home discharge achieved an area under the receiver operating characteristic curve of 0.95 and a precision of 0.776. After threshold was moved, the model had a recall of 0.809. Top 10 variables by importance were National Institutes of Health Stroke Scale (NIHSS) score, family income, Barthel index (BI) score, FRAIL score, fall risk, pressure injury risk, feeding method, depression, age and dysphagia. The ML model identified higher NIHSS, BI, and FRAIL, family income, higher fall risk, pressure injury risk, older age, tube feeding, depression and dysphagia as the top 10 strongest risk predictors in identifying patients who required non-home discharge to higher levels of care. Modern ML techniques can support timely and appropriate clinical decision-making. This study illustrates the characteristics and risk factors of non-home discharge in patients with stroke, potentially contributing to the improvement of the discharge process. STROBE guidelines.

Decision support model to assess pesticide safeness toward environment.

For farmers around the world to protect crops from disturbing pests, it is common to use pesticides to ward off the growth of pests or even eliminate them. Even though pesticides are seen as a good thing for protecting crops, there is one thing that mustn't be forgotten the origin of the pesticide itself is a toxin compound that is dangerous if used irresponsibly. The main concern of this study is excessive use of pesticides may cause serious consequences to the ecosystem and environment through the accumulation of pesticide residue by irresponsible farmers. To minimize the effects of pesticide residues, the selection of the type of pesticide needs to be considered which type may not be harmful to the environment's health even though accumulation happens. Therefore, in this study, a fuzzy-based computational model assessor was built to measure the safety level of pesticides toward the environment. The fuzzy model was created with consideration of several parameters related to pesticide behaviors, its effects on beneficial organisms, and its persistence in the environment. The method used for this study includes literature reviewing, fuzzification, statistical approach, expert knowledge sharing, and quantitative analysis. The model created in this study can assist in a more accurate and realistic method of selecting better pesticide options that will be used by farmers. To ensure the validity of the model, verifying and validating the formula and pesticide result assessment were done with related literature articles. In this study, from 10 types of pesticides used as a sample, dodine, and iprodione pesticides are the best option for protecting crops with a safety level of 7.36, and abamectin, dimethoate, chorpyrifos, and methidathion are not safe options for farming use because of its potential of harming the environment.