Effective Decision Support Research Articles

Pengaruh Intervensi Dukungan Pengambilan Keputusan Terhadap Masalah Manajemen Kesehatan Tidak Effektif Pada Keluarga Dengan Penyakit Kronis

Chronic disease is a degenerative disease that affects individuals for more than 6 months. Degenerative and chronic diseases that are common in Indonesian society today include hypertension. This disease requires continuous treatment. Treatment is not only done while in the hospital but also requires further medication and care at home. Families with family members who have a history of chronic disease, one of which is the problem of ineffective health management. One of the interventions that nurses can do is decision support. The purpose of this study is to determine the effect of decision-making support interventions on ineffective health management problems. The design of this research is a case study with a nursing care approach. The case study subjects were 2 families who had family members with a history of hypertension. Enforcement of the problem refers to the definition of major and minor symptoms. The results showed that there was an increase in health management from moderately declining to sufficiently increasing with 3 days of management. Effective decision-making support interventions to address health management problems are ineffective.Chronic diseaseineffective health managementDecision-making support

Simultaneous scheduling of maintenance crew and maintenance tasks in bus operating companies: a case study

PurposeThe purpose of this paper is to describe an integer linear programming model to schedule the maintenance crew and the maintenance tasks in a bus operating company.Design/methodology/approachThe proposed methodology relies on an integer linear programming model that finds feasible maintenance schedules. It minimizes the costs associated with maintenance crew and the costs associated with unavailability. The model is applied in a real-world case study of a Portuguese bus operating company. A constructive heuristic approach is put forward, based on solving the maintenance scheduling problem for each bus separately.FindingsThe heuristic finds better solutions than the exact methods (based on branch-and-bound techniques) in a much lower computational time.Practical implicationsThe results suggest the relevance of such heuristic approaches for maintenance scheduling in practice.Originality/valueThis proposed model is an effective decision-making support method that provides feasible maintenance schedules for the maintenance technicians and for the maintenance tasks in a fleet of buses. It also complies with several operational, technical and labour constraints.

Analyzing the critical success factors influencing Lean Six Sigma implementation: fuzzy DEMATEL approach

PurposeLean Six Sigma (LSS) is receiving a tremendous attention as a modern process of streaming to improve the organizational ability and customer satisfaction. A successful LSS implementation is influenced by various factors and the execution of all the influencing factors simultaneously is a very difficult task for any organization. From the perspective of limitation of resources, this paper aims to present a basic issue in an LSS implementation of clustering complex and impacting factors into groups to achieve them in a stepwise manner. This paper aims to present a fundamental issue of clustering the complex and impacting factors of an LSS implementation into groups to achieve them stepwise.Design/methodology/approachA total of 40 relevant influencing factors toward an LSS implementation have been identified from the extensive literature review and duly validated with experts’ opinions. Integrated fuzzy set theory and decision-making trial and evaluation laboratory (DEMATEL) approach are demonstrated to explore the causal relationships among influencing factors of the LSS implementation. An empirical case analysis of an Indian manufacturing organization is carried out to illustrate the utilization of the proposed model.FindingsThe proposed framework effectively finds out the significance of each influencing factor of an LSS implementation and clustered into cause–effect groups. As per the results of the empirical case analysis, ten critical success factors (CSFs) of the LSS implementation are evaluated for the successful LSS implementation. Top management pays more attention to achieve them and implement them in a phase-wise approach under the limitations of accessible resources.Research limitations/implicationsThe presented framework provides an effective, precise and systematic decision support tool for recognizing CSFs of the LSS implementation. The organization, decision-makers, industrial practitioners and academic researchers may be able to comprehend the cause–effect relationship of the influencing factors of the LSS implementation. The exploratory nature and the single case study are two major limitations of this analysis. The developed model is heavily dependent on the experts’ opinions; hence, any bias in judgment will influence the final result.Originality/valueThis analysis is the first of its kind of effort, according to the best of the authors’ knowledge, to classify the influencing factors of LSS implementation into the cause–effect cluster. The outcomes of this analysis make the complexity of a problem easier in handling and assisting the decision-making.

Secure convergence of artificial intelligence and internet of things for cryptographic cipher- a decision support system

The communication industry is rapidly growing with the passage of time and the number of communication devices is increasing. This increase of communication devices put the devices and their communication into a high risk and security challenges. Intruders tries for capturing important information from such communication devices and are using for their own benefits. To build an effective and accurate decision support system (DSS) for the convergence of Artificial Intelligence (AI) and Internet of Things (IoT) can secure the systems for secure and smooth point-to-point communication. Multi-criteria decision support systems play an important role in decision making for a particular situation based on several security criteria. Decision making based on multi-criteria is one of the exciting issues faced by practitioners and researchers for the convergence of AI and IoT. Numerous DSS are available for making decisions which have the possibility to adopt activities of the decision making. The planned study presented a DSS for the secure convergence of AI and IoT for devices. The experimental work of the proposed study was carried out in the SuperDecisions tool for plotting the hierarchy of security situations based on goal, security criteria, and alternatives.

Route selection in multimodal transportation networks: a hybrid multiple criteria decision-making approach

ABSTRACT The objective of this study is to propose a novel hybrid multiple criteria decision-making (MCDM) approach for route selection in multimodal supply chains. The proposed hybrid MCDM approach integrates the analytic hierarchy process (AHP), data envelopment analysis (DEA), and the technique for order of preference by similarity to ideal solution (TOPSIS) to address the problem based on quantitative and qualitative criteria. It utilizes the advantages of each approach, and can reconcile conflicting criteria and divergence of decision makers’ judgments to select the most appropriate alternative as well as provide the preferred order of the multimodal routes. Furthermore, an empirical route selection between Thailand and Vietnam is applied to validate the abilities of the proposed hybrid MCDM approach. The new methodological contribution of this study assists decision makers and transport policymakers to design an effective decision support tool for solving transportation and logistical problems in real-world decision-making processes.

Decision support issues in automated driving systems

AbstractMachine learning and computational processing have advanced such that automated driving systems (ADSs) are no longer a distant reality. Many automobile manufacturers have developed prototypes; however, there exist numerous decision support issues requiring resolution to ensure mass ADS adoption. In the coming decades, it is likely that production ADSs will only be partially autonomous. Such ADSs operate within predetermined conditions and require driver intervention when they are violated. Since forecasts of their 20‐year market penetration are relatively low, ADSs will likely operate in heterogeneous traffic characterized by vehicles of varying autonomy levels. Under these conditions, effective decision support must consider intangible, subjective, and emotional factors as well as influences of human cognition; otherwise, the ADS risks driver distrust and unsatisfactory performance based on an incomplete understanding of its environment. We survey the literature relevant to these issues, identify open problems, and propose research directions for their resolution.

Trustworthy Predictive Algorithms for Complex Forest System Decision-Making

Advances in predictive algorithms are revolutionizing how we understand and design effective decision support systems in many sectors. The expanding role of predictive algorithms is part of a broader movement toward using data-driven machine learning (ML) for modalities including images, natural language, speech. This article reviews whether and to what extent predictive algorithms can assist decision-making in forest conservation and management. Although state-of-the-art ML algorithms provide new opportunities, adoption has been slow in forest decision-making. This review shows how domain-specific characteristics, such as system complexity, impose limits on using predictive algorithms in forest conservation and management. We conclude with possible directions for developing new predictive tools and approaches to support meaningful forest decisions through easily interpretable and explainable recommendations.

An integrated methodology to overcome barriers to climate change mitigation strategies: a case of the cement industry in India.

Cement is a basic requirement of today's society and is the only thing that humans consume more volume than water, but cement manufacturing is the most energy- and emission-intensive process. Hence, the cement industry is currently under pressure to reduce greenhouse gases (GHGs) emissions. Climate change mitigation strategies implemented in the industry leads to GHGs reduction, climate risks, pollutants, and another adverse impact on the environment. In order to implement climate change mitigation strategies in the cement industry, a careful analysis of barriers that hinder the emission reduction must be taken. However, most existing research on the barriers to mitigation measures is focused on developed countries. Among the most important emerging economies, India, the second-largest producer and consumer of cement, faces challenges to implement emission reduction measures. To bridge this gap, this paper identifies and evaluates the barriers and solutions to overcome these barriers in the context of India. This study employs a three-phase methodology based on fuzzy analytical hierarchy process (AHP) and fuzzy technique for order performance by similarity to ideal solution (TOPSIS) to identify barriers and solutions to overcome these barriers to climate change mitigation strategies adoption in Indian cement industry. Fuzzy AHP is employed to prioritize these barriers, and to rank solutions of these barriers, Fuzzy TOPSIS is employed. Ten Indian cement manufacturing industry is taken to illustrate the proposed three-phase methodology. Finally, the result of the analysis offers an effective decision support tool to the Indian cement industry to eliminate and overcome barriers to mitigation strategies adoption and build their green image in the market.

Research on the Optimization of a Track Circuit Maintenance Strategy Based on the BB-MOPSO Algorithm

Focusing on the issues of the effects of the high failure rate and poor maintenance on the ZPW-2000A track circuit during in-field operations, this paper proposes a multi-objective maintenance decision optimization method by considering the number of maintenance personnel. The developed method introduces not only the service age regression, increased failure rate, system’s average reliability, maintenance costs, but also the availability calculation model in the case of multiple maintenance personnel. We solve the model using the integer coded BB-MOPSO algorithm, and obtain the Pareto front solution set, and then the optimal maintenance plan of the system is further established. In addition, the number of optimal maintenance personnel is analyzed in detail. Compared to the results of traditional maintenance strategies, the results obtained in this paper show that the proposed model can reduce the maintenance costs, under the same working conditions, by 13.29%, moreover, it can more comprehensively reflect the impact of maintenance activities on the system reliability, maintenance costs, and availability. Furthermore, the method can meet the needs of decision-makers for diversified solutions and can provide an effective decision support while on site.

Optimization of the Active Composition of the Wind Farm Using Genetic Algorithms

The article presents the results of research on the possibilities of using genetic algorithms for solving the multicriteria optimization problem of determining the active components of a wind farm. Optimization is carried out on two parameters: efficiency factor of wind farm use (integrated parameter calculated on the basis of 6 parameters of each of the wind farm), average power deviation level (average difference between the load power and energy generation capabilities of the active wind farm). That was done an analysis of publications on the use of genetic algorithms to solve multicriteria optimization problems. Computer simulations were performed, which allowed us to analyze the obtained statistical data and determine the main optimization indicators. That was carried out a comparative analysis of the obtained results with other methods, such as the dynamic programming method; the dynamic programming method with the general increase of the set loading; the modified dynamic programming method, neural networks. It is established that the average power deviation for the genetic algorithm and for the modified dynamic programming method is located at the same level, 33.7 and 28.8 kW, respectively. The average value of the efficiency coefficient of wind turbine used for the genetic algorithm is 2.4% less than for the modified dynamic programming method. However, the time of finding the solution by the genetic algorithm is 3.6 times less than for the modified dynamic programming method. The obtained results provide an opportunity to implement an effective decision support system in energy flow management.

Multiple-Criteria Evaluation of Thin-Walled Energy-Absorbing Structures of Train Under Fuzzy Environment: Modeling and Algorithm

High-speed train is of great significance in the modern comprehensive transportation system. Bio-inspired engineering design, with the excellent structural and mechanical properties of the biological systems, has been a widespread concern in the design of thin-walled energy-absorbing structures for high-speed trains. However, different structural characteristics have significant effects on the performance of crashworthiness and lightweight level. Collaboration matching of performance between design and operational processes considering the engineering requirements has become an urgent problem. This study constructs the finite element model of the horsetail-bionic thin-walled energy-absorbing structure, which is inspired by horsetail’s structural characteristics. An existing high-speed train is set as the empirical case. The effects of the number of cross-section configurations on the performances of crashworthiness and light level are explored under the condition of train collision. A hybrid decision-making methodology that combines fuzzy DEMATEL and TODIM is proposed. The result shows the horsetail-bionic thin-walled structure with six-floor plates is the optimal alternative considering the multiple criteria. In addition, comparison with the existing methods and sensitivity analysis are conducted to validate the reliability of this proposed approach. This study provides an effective decision support tool for crashworthiness evaluation or structural feature selection of thin-walled structures.

Optimizing Team Sport Training With Multi-Objective Evolutionary Computation

Abstract This research introduces a new novel method for mathematically optimizing team sport training models to enhance two measures of athletic performance using an evolutionary computation based approach. A common training load model, consisting of daily training load prescriptions, was optimized using an evolutionary multi-objective algorithm to produce improvements in the mean match-day running intensity across a competitive season. The optimized training model was then compared to real-world observed training and performance data to assess the potential improvements in performance that could be achieved. The results demonstrated that it is possible to increase and maintain a stable level of match-day running performance across a competitive season whilst adhering to model-based and real-world constraints, using an intelligently optimized training design compared a to standard human design, across multiple performance criteria (BF+0 = 5651, BF+0 = 11803). This work demonstrates the value of evolutionary algorithms to design and optimize team sport training models and provides support staff with an effective decision support system to plan and prescribe optimal strategies to enhance in-season athlete performance.

Supporting practitioners in developing effective decision support tools for natural resource managers

In the United States, there is a growing interest in the participatory development of agricultural and natural resource–focused decision support tools (DSTs). To provide greater insight for practitioners developing these DSTs, we conducted a review of manuscripts ( n = 23) that describe DSTs in US agricultural and forestry sectors, both those designed through participatory processes and otherwise. Our work operationalizes a novel conceptual framework developed to support participatory DST development, as recent scholarship suggests participatory processes lead to better adoption and use of DSTs. Our analysis suggests that tool developers should, in reporting on their efforts, more clearly articulate the ways decision makers are included in DST development, from problem identification through evaluation. Failure to do so limits our collective understanding of the utility of these tools. Following our review, we present recommendations for DST developers and other practitioners who want to support effective and transparent development of stakeholder-driven DSTs. We propose practitioners (1) implement complete assessments of relevant stakeholder network(s) that might use new DSTs; (2) engage stakeholders iteratively throughout the development process; (3) improve evaluation of DSTs, including an assessment of the usability, usefulness and usage of tools across their life cycle; and (4) and describe the process of stakeholder engagement process in published …

Multi-criteria Optimization in Solving the Problem of Expanding Production Capacity of an Enterprise as a Method of Modeling Strategic Directions for the Development of Production Systems

Managing production capacity of industrial enterprises is an important element of the strategy for the development of production systems. The relevance of effective decision support methods in managing production capacity especially increases in the current conditions of a long-term decline in investment activity of Russian industrial enterprises and, as a result, a shortage of modern high-tech equipment, which becomes a significant limiting factor in the development of territorially oriented production systems. The article presents approaches to mathematical modeling and optimization of production capacity of industrial enterprises in solving the problem of its expansion under given budget constraints. The proposed mathematical model includes key parameters of the technical and economic planning of production capacity of a technological site and serves as a tool to justify the choice of an acceptable method for its expansion. The multicriteria optimization methods used in the model serve in this case as a means of formalized description and reaching a reasonable compromise based on the required values of the target parameter and budget constraints, as well as preferences of the decision maker. The article contains an example of the numerical implementation of the developed model in the management of production capacity of a machining site; the example illustrates practical applicability of this model and the result of multicriteria optimization from the standpoint of restrained pessimism of the decision maker.

Review of real-time effective decision support system for water resource management

The current technologies are predominantly attract the water resource management(WRM); if this water can not adequately utilizing for human activities, then several future problems are happening. Therefore super hydro controlling technology and it’s management systems are necessary. In this investigation, various water resource management methodologies, it’s merits and demerits are reviewed. The 2R-decision support system(DESS) implementation can change the conventional water management mechanisms, DESS addresses the engineering and non-engineering issues related to WRM system. The global WRM system designs and utilizations are facing many questionaries from experts. In India, there are many WRMs, but these are work for the nation, due to this limitation nations are facing many contemporary issues. Several methods have overcome these limitations but 2R-DESS answering the complete and accurate solutions to WRM. The advanced WRM system and its functionalities are verified on one example with the river basin agencies through DESS adequately. In this research work, for WRM calculating the various measures such as catchment area improvement, supply flow, upper flow, lower flow and acknowledgement of various effecting factors. The designed 2R DESS challenging the present WRM methodologies and results outperforms present technologies.

Optimization of integrated fuzzy decision tree and regression models for selection of oil spill response method in the Arctic

The challenging oil spill response in the Arctic calls for effective response decision support tools. In this study, a framework comprising the development of various integrated fuzzy decision tree and regression (FDTR) models as well as model optimization was developed to facilitate the selection of suitable response methods for oil spill accidents in Arctic waters. The FDTR models took into account the influential attributes affecting the effectiveness of oil spill response in harsh Arctic environments. Different FDTR models were developed based on the combinations of three regression analyses, including linear, non-linear, and Gaussian process regression (GPR) and four information evaluation measures for splitting a decision tree, including information gain, deviance, GINI impurities (GINI), and misclassification error. Non-dominated sorting differential evolution (NSDE) optimization was employed to enhance the predictive performance of the FDTR models. The prediction performance of the FDTR models was compared using an oil spill dataset. Using this framework, the average prediction accuracy and the number of rules (representing the robustness) of FDTRs were increased by 14% and decreased by 57%, respectively. A set of optimal prediction models to promptly select an appropriate response method can be obtained using this framework. Among all models, GPR-GINI performed the best concerning optimal values of objective functions.

A prediction and interpretation framework of acute kidney injury in critical care

Acute kidney injury (AKI) is a common clinical condition with high mortality and resource consumption. Early identification of high-risk patients to achieve an appropriate allocation of limited clinical resources and timely interventions is of significant importance, which has attracted substantial research to develop prediction models for AKI risk stratification. However, most available AKI prediction models have moderate performance and lack of interpretability, which limits their applicability in supporting care intervention. In this paper, a machine learning-based framework for AKI prediction and interpretation in critical care is presented. First, an ensemble model is developed to predict a patient’s risk of AKI within 72 h of admission to the intensive care units. Next, the model is interpreted both globally and locally. For the global interpretation, the important predictors are pinpointed and the detailed relationships between AKI risk and these predictors are illustrated. For the local interpretation, patient-specific analysis is presented to provide a visualized explanation for each individual prediction. Experimental results show that such a prediction and interpretation framework can lead to good prediction and interpretation performance, which has the potential to provide effective clinical decision support.

Neural network method for search of the active site of a wind power plant

The article presents the results of the study of the possibilities of using neural networks to solve the problem of determining the active set of a wind farm (WF), taking into account the efficiency of each wind turbines (WT). The comparative analysis of the obtained results with the known methods of determining the active composition of WF, such as: the method of dynamic programming; the method of dynamic programming with increasing the load on the experimentally set percentage; modified method of dynamic programming. The advantages and disadvantages of using each of the studied methods in terms of the possibility of achieving a given generation power at the maximum efficiency of the selected WT are determined. It is established that when using recurrent neural networks to solve the problem of determining the active composition of WF, the minimum direct linear variation of the difference between the power to be generated and the actual power of the determined active set of WF is 2.7%. Under the same conditions, the use of other known methods, in particular, the modified method of dynamic programming ensures the achievement of this parameter at the level of 0.05%. This significantly increases the time to solve the problem. By computer simulation, it was found that under equal conditions, the time to solve the problem using neural networks - 0.04 s, and using a modified method of dynamic programming 3.4 s. The obtained results provide an opportunity to implement effective decision support systems in energy flow management.

Evolution of Clinical Medicine: From Expert Opinion to Artificial Intelligence

Artificial intelligence provides a vast opportunity and conquest of the science of knowledge. Twenty-first-century medicine will be characterized by an extraordinary ability to access and process medical information to provide patient-specific, timely, and effective clinical decision support. The knowledge gained by patient care experience and clinicians' expertise has led to many clinical care advances. Access to a large volume of data, along with ever-growing information and knowledge of diseases, can allow us to optimize diagnoses and management strategies by using advances in machine learning and artificial intelligence. Changing the medical culture from only relying on the experts to use medical informatics advances to improve the experts' clinical judgment would be an uphill battle. It is necessary to overcome the clinicians' traditional training to empower them into moving in the data science, statistics, and artificial intelligence era. As the incorporation of artificial intelligence in clinical practice seems inevitable, a thorough understanding of its capacities and flaws is essential to the emergence of a new clinical practice world. This review paper describes some of the nuances of past, current, and future clinical decision support systems and artificial intelligence's impact on this process.

Strategic global supply chain network design – how decision analysis combining MILP and AHP on a Pareto front can improve decision-making

Integrating a broad range of information types and finding trade-offs between conflicting goals is a challenge in global supply chain network design (GSCND). Effective decision support systems (DSS) should be user-friendly, provide transparency, and support human judgement. There is a wide range of optimisation models that aim to improve the outcome of network design decisions. However, their practical performance often remains unknown, as their implementation into the managerial decision process is largely neglected. Such theory-driven models usually focus on single aspects of the decision, without being able to accommodate the practical problem comprehensively. We employ the CIMO approach to resolve the issue and contribute by showing how an integration involving these methods can be useful for managers once the proper knowledge transfer has been effectuated. An innovative decision support framework, which combines mixed-integer linear programming, the Analytical Hierarchy Process, and the Pareto front is created and analysed during a case study in the med-tech industry. Results show that the framework accommodates managerial experience, integrates qualitative as well as quantitative criteria, and provides transparency over the entire range of efficient solutions. The framework and application results contribute towards the development of more flexible and easy-to-use decision support systems for GSCND.