Decision-making Model Research Articles

A miscellaneous model for comprehensive attractiveness assessment of chemical plants: Integrating chemical hazards, domino effects and plant traits

The growing threat of intentional attacks on major petrochemical plants underscores the urgent need for robust security risk assessments (SRA). Such assessments are widely acknowledged as crucial for risk mitigation and are currently a focus of active research. This study introduces a methodology for assessing the attractiveness of the petrochemical sector to intentional attacks, considering chemical hazards, domino effects, and plant traits. Within this framework, we have identified 12 risk factors that capture the multifaceted nature of targeting incentives, relating to the enterprise, location, population, and other key features. We have developed a hybrid decision-making model that analyzes the interrelationships among these factors and determines their weights, integrating the Decision-Making Trial and Evaluation Laboratory (DEMATEL) and the Analytic Network Process (ANP). The applicability of this method is demonstrated through a detailed case study. The results indicate that our method can effectively rank the attractiveness of chemical plants within a specific region, serving as a preliminary screening phase in the SRA. The findings of the attractiveness assessment may assist in identifying potential targets among numerous existing plants and support the allocation of security resources and the formulation of defensive measures.

Foreword for the special issue: “Decision-Making Models for Common Goods in Environmental, Health, and Logistics Contexts”

We present the scientific results of the call for papers in SEPS titled ’Decision-Making Models for Common Goods in Environmental, Health, and Logistics Contexts.’ As guest editors in Socio-economic Planning Sciences (SEPS), we describe the domain of our academic initiative and present the collected papers. Interestingly, our collection includes papers employing various methodologies such as multi-criteria decision making, game theory, operations research, microeconomics, computational models and insurance mathematics. We received more than fifty submissions from Asia, Europe and America. However, only thirteen papers were accepted for publication in SEPS. This demonstrates the extensive investigation of the problem of common goods and the ongoing academic debate surrounding it.

Social network group decision-making model considering interactions between trust relationships and opinion evolution

PurposeSocial network group decision-making (SNGDM) has rapidly developed because of the impact of social relationships on decision-making behavior. However, not only do social relationships affect decision-making behavior, but decision-making behavior also affects social relationships. Such complicated interactions are rarely considered in current research. To bridge this gap, this study proposes an SNGDM model that considers the interaction between social trust relationships and opinion evolution.Design/methodology/approachFirst, the trust propagation and aggregation operators are improved to obtain a complete social trust relationship among decision-makers (DMs). Second, the evolution of preference information under the influence of trust relationships is measured, and the development of trust relationships during consensus interactions is predicted. Finally, the iteration of consensus interactions is simulated using an opinion dynamics model. A case study is used to verify the feasibility of the proposed model.FindingsThe proposed model can predict consensus achievement based on a group’s initial trust relationship and preference information and effectively captures the dynamic characteristics of opinion evolution in social networks.Originality/valueThis study proposes an SNGDM model that considers the interaction of trust and opinion. The proposed model improves trust propagation and aggregation operators, determines improved preference information based on the existing trust relationships and predicts the evolution of trust relationships in the consensus process. The dynamic interaction between the two accelerates DMs to reach a consensus.

Minimisation of the Energy Expenditure of Electric Vehicles in Municipal Service Companies, Taking into Account the Uncertainty of Charging Point Operation

This article presents an original method for minimising the energy expenditure of electric vehicles used in municipal service undertakings, taking into account the uncertainty in the functioning of their charging points. The uncertainty of the charging points’ operation was presented as the probability of the occurrence of an emergency situation hindering a point’s operation, e.g., a breakdown or lack of energy supply. The problem is how to calculate the driving routes of electric vehicles so that they will arrive at charging points at times at which there is a minimal probability of breakdowns. The second aspect of this problem to be solved is that the designated routes are supposed to ensure the minimum energy expenditure that is needed for the vehicles to complete the tasks assigned. The developed method is based on two heuristic algorithms, i.e., the ant algorithm and genetic algorithms. These algorithms work in a hybrid combination, i.e., the ant algorithm generates the initial population for the genetic algorithm. An important element of this method is the decision-making model for defining the driving routes of electric vehicles with various restrictions, e.g., their battery capacity or the permissible risk of charging point breakdown along the routes of the vehicles. The criterion function of the model was defined as the minimisation of the energy expenditure needed by the vehicles to perform their transport tasks. The method was verified against real-life data, and its effectiveness was confirmed. The authors presented a method of calibrating the developed optimisation algorithms. Theoretical distributions of the probability of charging point failure were determined based on the Statistica 13 program, while a graphical implementation of the method was carried out using the PTV Visum 23 software.

An Analytical Decision-Making Model for Integrated Green Supply Chain Problems: A Computational Intelligence Solution

This paper presents an integrated mathematical decision-making model to reduce environmental footprints using process integration. By doing so, we address the research question: How can supplier selection, ergonomic factors, and green practices be integrated into mathematical decision-making models based on the vendor-managed inventory policy? We use the Branch and Bound method to provide optimal solutions and a metaheuristic optimization approach in computational intelligence for large-scale problems. This paper employs a multi-criteria decision-making method to compare proposed algorithms results based on six criteria. Our findings reveal that integrating the sourcing process with inventory decisions significantly improves cost performance. Furthermore, our results demonstrate that the proposed model effectively balances environmental and economic objectives using a carbon tax. Moreover, sensitivity analysis results indicate that optimizing water and energy consumption can significantly improve carbon footprints. In addition, the study shows that ergonomic factors, including lighting and temperature, have a substantial impact on performance. The primary contributions of this paper include the incorporation of a multi-sourcing strategy, green practices, ergonomic factors, and supplier selection into vendor-managed inventory decision models.

An integrated model for road freight transport firm selection in third-party logistics using T-spherical Fuzzy sets

The efficient selection of road freight transport (RFT) firms plays a critical role in constructing well-operating logistics systems for large-scale third-party logistics (3PL) providers. However, the decision-making process for such selection is often complex and uncertain due to various influential criteria and dynamic industry nature. To address this challenge, a novel decision-making model that integrates Delphi, CRiteria Importance Through Intercriteria Correlation (CRITIC) and Combined compromise ranking solution (CoCoSo) methods based on T-Spherical Fuzzy sets has been proposed in this paper. The model integrates expert opinions with an extensive literature review to identify the influential criteria and their corresponding weights. Subsequently, it employs a comprehensive evaluation approach to rank the RFT firms based on their performance. The model accommodates uncertainties and subjectivity by utilizing T-Spherical Fuzzy Numbers, offering robustness and transparency in decision-making. A case study involving 15 evaluation criteria and 12 RFT firms were selected to demonstrate the applicability and aptness of the proposed model. Flexibility and Integrability to different transport modes emerged as the two most essential criteria, whereas RFT firm A2 emerged as the best alternative with the highest performance score of 3.0861, followed by RFT firm A7 with a score value of 3.0499.

A chance-constrained net revenue model for online dynamic predictive maintenance decision-making

Most models for predictive maintenance (PdM) decision-making focus on the expected value of a system performance metric (e.g., the expected cost rate). However, focusing solely on such expected values may overlook the significant risks of resulting PdM decisions. Indeed, it would be practically valuable to make the optimal PdM decision by considering the least probability of achieving a target system performance. Furthermore, emphasizing only maintenance costs without accounting for operational revenues can render the maintenance strategies unappealing, particularly when the revenues across system lifecycles are significant. This study introduces a method that employs a stochastic net revenue model and chance-constrained programming to address these issues in PdM decision-making, wherein the preventive maintenance cost is proportional to the system's current degradation state. By exemplifying system degradation with the Gamma process, we derive the probability density function of stochastic net revenue and present a Bayesian method to simultaneously update the drift and diffusion parameters of the Gamma process model. The effectiveness and practical applicability of our proposed chance-constrained net revenue model and parameter updating method are demonstrated through a numerical example and a case study.

Consequence analysis of a small-scale hydrogen leakage from the overhead hydrogen piping based on machine learning and physical modeling

Leakage from the overhead hydrogen piping (OHP) must be contained because of the wide flammability range and low minimum ignition energy of hydrogen; rapid leakage detection and appropriate emergency responses are necessary to ensure safe OHP operation. Consequence analysis after leakage detection is useful for hazardous-area estimation and assists operators in emergency-response planning. However, consequence analysis and leakage-detection methods have not been combined so far, and an integrated method has not yet been developed. This study aimed to develop a method based on machine learning (ML), physical modeling, and consequence analyses for rapid and appropriate decision-making after hydrogen leakage. Initially, an OHP model was constructed using physical modeling; this is a method that models target systems based on fundamental physical equations. Subsequently, an ML model for leakage detection was constructed based on various pressure or flow-rate profiles obtained from the OHP model. Regression models were constructed to estimate the leakage diameter. In addition, consequence analyses were conducted to estimate the effects of hydrogen explosions and fires using the leakage diameters predicted by the regression model. We confirmed that the ML model could distinguish between leakage and non-leakage conditions, and the estimated consequences could be used to visualize the risk level of hazardous areas near the leakage points in the case studies. The results of the case studies demonstrated the effectiveness of the proposed method for rapid decision making when hydrogen leaks from OHP. This method enables improved emergency responses in post-leakage situations.

Value-based decision-making in regular alcohol consumers following experimental manipulation of alcohol value

BackgroundDevaluation of alcohol leads to reductions in alcohol choice and consumption; however, the cognitive mechanisms that underpin this relationship are not well-understood. In this study we applied a computational model of value-based decision-making (VBDM) to decisions made about alcohol and alcohol-unrelated cues following experimental manipulation of alcohol value. Method: Using a pre-registered within-subject design, thirty-six regular alcohol consumers (≥14 UK units per week) completed a two-alternative forced choice task where they chose between two alcohol images (in one block) or two soft drink images (in a different block) after watching videos that emphasised the positive (alcohol value), and separately, the negative (alcohol devalue) consequences of alcohol. On each block, participants pressed a key to select the image depicting the drink they would rather consume. A drift–diffusion model (DDM) was fitted to reaction time and choice data to estimate evidence accumulation (EA) processes and response thresholds during the different blocks in each experimental condition. Findings: In the alcohol devalue condition, soft drink EA rates were significantly higher compared to alcohol EA rates (p = 0.04, d = 0.31), and compared to soft drink EA rates in the alcohol value condition (p = 0.01, d = 0.38). However, EA rates for alcoholic drinks and response thresholds (for either drink type) were unaffected by the experimental manipulation. Conclusions: In line with behavioural economic models of addiction that emphasise the important role of alternative reinforcement, experimentally manipulating alcohol value is associated with changes in the internal cognitive processes that precede soft drink choice.

The Rise of Social Behavior in Social Media Marketing During Pandemic

The adoption of technology has changed rapidly during the global pandemic Covid-19 especially in the sector of trading. Previous studies found that businesses that are used to utilizing technology in their operational activities tend to be more able to survive during the pandemic, including small and medium businesses. The research aims to investigate the social behavior through social media platforms and how this behavior increases during the Covid-19 pandemic in Indonesia. The study adopts consumer decision-making (CDM) model as a framework with a slight modification in the phase of post-purchase behavior. Data collected thru online questionnaires as many as 377 social media active users in Indonesia and analyzed using paired sample t-test. Paired sample t-test aims to perceive whether the analyzed paired sample experiences significant changes. The results revealed that during pandemic situations, social media as a medium for identifying, collecting information, evaluating, comparing and post-purchase expressions are increased. In addition, the data also shows an increase of consumers’ post-purchase behaviors that voluntarily promote their purchased goods and services during pandemic situations. This paper contributes on a different perspective of post purchase behavior where the main focus of this concept is not only to influence and change public behaviors but to invite the public to act as a social marketer in order to achieve broader social benefits. The article contains a different paradigm about social marketing which should be considered further and push social marketing behavior forward to gain broader benefits for the community.

A quantitative framework to support the decision between traditional, selective, and hybrid assembly

High-technological products often consist of several components, each with varying feature values that are critical to the assembly’s quality. As traditional manufacturing involves random selection of components, strict tolerance ranges are typically set to limit their feature variability, increasing scrap rates and lowering material efficiency. By smartly combining components based on their measured features, selective and hybrid assembly allow for more relaxed tolerances and more efficient use of raw materials without affecting the final product’s quality. However, the increased logistical complexity involves additional operational costs, complicating the choice for the best strategy.This study presents a decision-making model for selecting from traditional, selective, and hybrid assembly. The model is formulated by first defining the cost functions for the corresponding expected assembly cost, and then analytically determining the optimal choice boundaries between the three methods based on the involved process parameters. Estimation methods for the instance-specific process parameters are then discussed before validating the model with a fully elaborated case study.Results confirm the need for a quantitative framework to support an informed choice as this strongly depends on the involved parameter values. This study aims to pave the way towards more sustainable manufacturing by supporting practitioners to identify the conditions whereby intelligent assembly methods, in addition to allowing for scrap reduction and increased material efficiency, also represent the most cost-effective option.

Fuzzy Inference Systems for Discretionary Lane Changing Decisions: Model Improvements and Research Challenges

Lane Changing Decision Model (LCDM) is a critical component in semi- and fully-automated driving systems. Recent research has found that Fuzzy Inference System (FIS) is a promising approach to implementing LCDMs. To improve the FIS’s performance, this research reviewed the challenges in the development of a FIS model to make the yes,no decisions in discretionary lane changes. The FIS model was revised to bring its fuzzy inference rules more consistent with the fuzzy membership functions, and its composition and defuzzification methods more in line with the classical fuzzy logic theory. An equitable test data set with approximately equal number of yes,no data points was assembled from the same NGSIM data used in the past research. The test results proved that: (1) A LCDM’s performance was dependent on how the yes,no decisions in the test data set were manually labeled; and (2) Separating the fuzzy inference rules into a yes group and a no group and compute the results separately yielded potentially better decision accuracy. Furthermore, The Genetic Expression Programming Model (GEPM) performed better than the improved FIS-based model. The findings lead the authors to suggest two possible research directions: (1) Add the subject vehicle’s speed as an input to the LCDM and redesign the decision-making model; and (2) construct models for congested and uncongested traffic separately. The authors further suggested the use of instrumented vehicles to collect a set of high-fidelity lane changing data in the naturalistic driving environment. (word count: 246)

A multi-criteria decision-making model to determine the optimal share of variable renewable energy sources in a wholesale electricity market

In recent years, the development of renewable energy technologies has gotten increasing attention due to the realization of cleaner production and high energy efficiency. Therefore, the proportion of these resources in electricity production has experienced a dramatic increase. However, with the features of intermittent nature and merit order effects, it is challenging to effectively optimize the integration of renewable energy sources (RES) into the electricity markets. To deal with the complex problem of selecting the proportionate share of non-dispatchable RES for participation in the market, this paper establishes a multi-criteria decision-making (MCDM) framework, which combines the Analytic Hierarchy Process (AHP) and Simple Additive Weighting (SAW). Five criteria are defined and the corresponding mathematical model is solved by the proposed AHP-SAW method. In this way, the optimal share of renewable resources for participation in the wholesale market can be determined based on the desired criteria. Also, to compare the results obtained by the AHP-SAW method, the criteria weights are determined by Shannon entropy, and the problem is solved by the TOPSIS method. To assert the effectiveness of the proposed framework, two case studies are done. According to the results, by increasing the RES penetration level, the average price of the market decreases mildly while price volatility increases significantly . Also, the amount of forward contracts is decreased when the RES share increases. Moreover, the results show that a high amount of RES is not necessarily desirable and depends on the type of renewable resources and policymakers' priorities. The results for systems 1 and 2 indicate that the maximum allowable amount of RES are 85.359 and 38.24 MW, respectively.

B-FMEA-TRIZ model for scheme decision in conceptual product design: A study on upper-limb hemiplegia rehabilitation exoskeleton

Upper-limb rehabilitation devices are essential in restoring and improving the motor function of hemiplegic patients. However, developing a product design that meets the needs of users is challenging. Current design tools and methods suffer from limitations such as a single model, poor synergy between integrated models, and subjective bias in analysing user needs and translating them into product attributes. To address these issues, this study proposes a new structural design decision-making model based on Behaviour Analysis (B), Failure Mode Effect Analysis (FMEA), and Teoriya Resheniya Izobreatatelskikh Zadatch (TRIZ theory). The model was developed and applied to design an upper-limb rehabilitation exoskeleton for hemiplegia. In this paper, an empirical investigation was conducted in several rehabilitation hospitals in Xuzhou City and used user journey mapping to identify potential failure points in the behaviour process. Then, the fault models were ranked according to the Fuzzy Risk Priority Number (FRPN) calculated by FMEA and used TRIZ theory to determine principles for resolving contradictions and generating creative design solutions for the product. By integrating B, FMEA, and TRIZ theory, it eliminated subjective bias in product design, improved the design decision-making process, and provided new methods and ideas for designing assistive rehabilitation devices and similar products. The framework of the proposed approach can be used in other contexts to develop effective and precise product designs that meet the needs of users.

Integration of blockchain and machine learning for safe and efficient autonomous car systems: A survey

The integration of blockchain and machine learning technologies has the potential to enable the development of more secure, reliable, and efficient autonomous car systems. Blockchain can be used to store, manage, and share the large amounts of data generated by autonomous vehicle various sensors and cameras, ensuring the integrity and security of these data. Machine learning algorithms can be used to analyze and fuse these data in real time, allowing the vehicle to make informed decisions about how to navigate its environment and respond to changing conditions. Thus, the combination of these technologies has the potential to improve the safety, performance, and scalability of autonomous car systems, making them a more applicable and attractive option for consumers and industry stakeholders. In this paper, all relevant technologies, such as machine learning, blockchain and autonomous cars, were explored. Various techniques of machine learning were investigated, including reinforcement learning strategies, the evolution of artificial neural networks and main deep learning algorithms. The main features of the blockchain technology, as well as its different types and consensus mechanisms, were discussed briefly. Autonomous cars, their different types of sensors, potential vulnerabilities, sensor data fusion techniques, and decision-making models were addressed, and main problem domains and trends were underlined. Furthermore, relevant research discussing blockchain for intelligent transportation systems and internet of vehicles was examined. Subsequently, papers related to the integration of blockchain with machine learning for autonomous cars and vehicles were compared and summarized. Finally, the main applications, challenges and future trends of this integration were highlighted.

A Review of Sports Analytics

Abstract: Due to the lack of review papers published in the field of operations research in sports analytics, the central aim of the authors of this paper is to conduct a bibliometric review of publications on the application of Operations research in sports analytics between 2000 and 2022. Through this bibliometric analysis, this paper aims to identify research gaps for future academicians to enable them to work on the topic. The data (130 papers) used for the paper comprises various central as well as interlinked disciplines and topics like sports scheduling, decision-making, and mathematical models used in sports. To present the data cleanly and allow the reader to visualize it, the authors have used various types of different graphs, tables, and figures. The present bibliometric review would be instrumental in helping academicians and students in identifying areas of research as well as helping tournament organizers and team managers, among others, by enumerating various areas that they can use to optimize decision-making in sports further.

A hybrid multi-attribute decision-making model for the systematic evaluation of exoticism-themed retail spaces from the perspective of consumer experience

Previous studies have provided abundant evidence confirming the significant impact of the physical environment in retail spaces themed with exoticism on brand establishment, consumer attraction, and stimulation of consumption behavior. However, there has been limited research employing systematic approaches to examine the influence of physical environmental elements on consumer experiences in Exoticism-themed retail spaces. Therefore, this study aims to propose a comprehensive and systematic evaluation framework from the perspective of consumer experience. It integrates the Caption Evaluation Method (CEM), Kano model, and Decision Making Trial and Evaluation Laboratory (DEMATEL) technique into a Multiple Attribute Decision Making (MADM) model. This study selects cases from China to demonstrate the superiority of the model. The research findings suggest that in the development and renovation process of integrating exoticism elements into retail environments, more attention should be paid to infrastructure such as street layout and facilities. Real plants, in particular, have the greatest primary influence on other elements. Theoretically, it offers a novel perspective on decision-making concerning the enhancement of the physical environment system in Exoticism-themed retail spaces. Practically, the analytical model developed in this study can provide decision-making insights and experiential references for similar themed retail spaces globally.

Assessment of the Economic Consequences of Riverbank Erosion: The Case of the South Morava River, Serbia

Landscape transformation, degradation and destruction are caused by fluvial processes as the predominant erosive processes in Serbia. The loss of arable land due to riverbank erosion is permanent, and the economic consequences are, therefore, especially pronounced. The primary aim of this study was to quantify the intensity of riverbank erosion in the lower part of the South Morava River (Serbia) during period 1924–2020, evaluate its economic consequences, and conduct a cost benefit analysis of revetment investments. The economic effects of riverbank erosion were analyzed by means of land loss and reduction in the quantity of agricultural production. An interdisciplinary research approach was applied using specific methodological procedures to calculate the riverbank erosion and soil (land) loss intensity (geographic information system-GIS), the economic consequences of riverbank erosion (ECRE), and the results of an investment decision-making model. The results showed that 202.6[Formula: see text]ha of arable land was lost during the observed period, the value of arable land loss was almost 622,000[Formula: see text]USD, and the loss in agricultural production was 7.5 million USD (discount rate 3.7%). The model is seen as the main research output and can be used for the assessment of long-term capital-intensive infrastructure projects in developing countries. The analysis identified the river segments that are economically viable for investments in riverbank revetments to preserve the largest area of fertile agricultural land. The results are especially valuable for river channel management, environmental planners and policy-makers, who deal with decisions regarding planning and the protection of bank erosion in areas of particular interest.

A Decision-Making Model for Prioritizing Low-Carbon Policies in Climate Change Mitigation

A Decision-Making Model for Prioritizing Low-Carbon Policies in Climate Change Mitigation

Modeling heterogeneous and adaptive homeowners’ decision-making for green retrofit

Green retrofitting (GR) of existing residential buildings is widely acknowledged as a potential solution for addressing climate and energy challenges. However, the realization of GR potential depends on homeowners’ decision-making, as they play a pivotal role in initiating GR and are the primary users of retrofitted homes. Previous studies have explored homeowners’ GR decisions from various perspectives. The majority of these studies may have encountered limitations, such as the use of static analysis methods, homogeneity assumptions, and oversimplified decision processes. These limitations can potentially hinder a comprehensive understanding of homeowners’ GR adoption, indicating the need for further research. To address this research gap, this study develops a decision-making model for homeowners that incorporates multiple dimensions: heterogeneity (e.g., individual attributes and GR motivations), a multi-stage decision process (needs-utility-willingness-adoption), and dynamic adaptability (both at individual and overall networks). The proposed model is empirically validated through Python simulations based on a case study in China. The findings of this study reveal the critical roles of GR needs, perceived (actual) utility, and perceived behavioral control in homeowners’ adoption of GR. Moreover, this study highlights the significance of coordinating homeowners’ overall perceived utility with the specific sub-utility associated with their motivations. Furthermore, empirical evidence underscores the influence of social factors on GR adoption and emphasizes the existence of gaps across different stages of the decision-making process. Drawing from these research findings, policy implications are proposed for promoting housing GR. This research contributes to the advancement of decision models for building GR and provides valuable insights for policymakers to promote housing GR.