Hybrid Decision-making Research Articles

Multimodal transport route selection: An integrated fuzzy hierarchy risk assessment and multiple criteria decision-making approach

Selecting multimodal transport routes is a complex problem due to the presence of various, often conflicting decision-making attributes, such as transportation cost and time, diverse risks with hierarchical relationships both within and across groups, decision-makers’ preferences, and numerous possible options. To address this problem, a novel decision-making approach is proposed, integrating the best-worst method (BWM), fuzzy hierarchy risk assessment (FHRA), and additive ratio assessment (ARAS). This approach decomposes the route decision-making problem into a hierarchy structure and determines the optimal solution through three distinct phases. In the first phase, BWM assigns relative weights to various criteria within the hierarchical structure, ensuring that the decision-makers’ preferences are accurately reflected. In the second phase, FHRA evaluates qualitative risks from the lowest to the highest hierarchy levels. This process involves calculating risk magnitudes at the lowest hierarchy level and progressively recalculating them as they ascend to the highest level of the hierarchy. The multimodal transport cost-model provides transportation cost and time. In the final phase, ARAS synthesizes all the decision-making data to rank all possible multimodal alternative routes based on utility scores. The realistic case study validates the practical applicability and effectiveness of the proposed approach. The findings indicated that the proposed methodology effectively identify the most appropriate multimodal transport route as the best compromise choice. This study contributes new knowledge regarding a new hybrid decision-making framework that combines multiple criteria decision-making methods with fuzzy hierarchy risk analysis, thereby enhancing the design of transportation plans to align with organizational goals and customer requirements.

Hybrid lane change strategy of autonomous vehicles based on SOAR cognitive architecture and deep reinforcement learning

Research on lane change strategies for autonomous vehicles holds paramount importance in optimizing traffic flow efficiency, enhancing driving safety, and adapting to complex traffic environments. While numerous rule-based or machine-learning approaches have been explored to tackle the challenge of lane change on highways, they frequently exhibit limited performance owing to the complexity of driving environments. This study proposes a novel lane change strategy for autonomous vehicles, which utilizes a hybrid framework integrating the SOAR cognitive architecture and deep reinforcement learning (DRL) to address the lane change challenge on highways. First, we introduce a rule extraction algorithm, the RuleCOSI+, which is based on tree ensemble algorithms, designed to extract concise lane change rules from large-scale human driving data. These straightforward rules, together with traffic regulations and safety rules, constitute the long-term memory of the SOAR cognitive architecture, enabling transparent decision-making processes. Next, by analyzing the clipping mechanism of the proximal policy optimization (PPO) algorithm, we propose an Adaptive Clipping PPO (ACPPO) algorithm which is based on the importance of samples. This algorithm adopts different clipping strategies for SOAR samples and ACPPO samples during the training process, enabling the algorithm to more effectively utilize samples with different levels of importance. Then, we propose a hybrid decision-making algorithm: SOAR-ACPPO, which combines the SOAR cognitive architecture with the ACPPO algorithm. This algorithm leverages SOAR’s prior knowledge to effectively and safely guide agent learning. Finally, by selecting appropriate intervention probability and weaning strategy, the system avoids inappropriate knowledge intervention and ensures adequate environment exploration. Simulation experiments conducted using the CARLA simulator illustrate that the proposed strategy not only improves model learning efficiency but also enhances driving efficiency and safety. Additionally, it demonstrates a certain degree of human-like characteristics and interpretability.

Fintech competencies in emerging markets: Cognitive hybrid decision-making approach

The purpose of this study is to make a priority analysis for the factors affecting fintech competency for emerging markets. A novel 3-stage decision-making model has been established to reach this objective. First, missing evaluations are completed by using recommender systems. The next stage includes the weight calculation of the criteria via quantum spherical fuzzy cognitive maps. The final stage gives information about the ranking of BRIC countries (Brazil, Russia, India, China) for the fintech competency performance. It is defined that technological infrastructure plays the most crucial role in this regard.

A novel hybrid decision-making framework based on modified fuzzy analytic network process and fuzzy best–worst method

A novel hybrid decision-making framework based on modified fuzzy analytic network process and fuzzy best–worst method

A Hybrid Multidimensional Performance Measurement Model Using the MSD-MPSI-RAWEC Model for Turkish Banks

Abstract The purpose of the paper is to analyze the multidimensional sustainability performance of deposit banks that operate in the Turkish banking industry. For this aim, the current research presents a novel hybrid decision-making model comprising of MSD, MPSI and RAWEC methodologies. In the developed decision-making model, the MSD and the MPSI objective weighting methods are utilized to assign significance weights to the criteria, while the RAWEC, a relatively new technique, is employed for banks' ranking. In order to check the robustness of the recommended model, various sensitivity and benchmark analyses were conducted. According to the findings of the study, the most important criterion in determining the sustainability performance of deposit banks is the total hours spent on employee training. Moreover, the most successful bank in terms of multidimensional sustainability is Garanti BBVA. Furthermore, sensitivity and comparison analyses prove that the integrated framework in this study is a powerful, reliable and useful decision tool that can be utilized in assessing the sustainability performance of banks. Besides, practical and managerial implications based on the findings of the applied decision-making tool are discussed.

A novel requirement elicitation and evaluation framework for product-service systems based on contextual matching and hybrid decision-making

Most studies concerning requirement elicitation and evaluation for product-service systems (PSSs) disregard the importance of completeness and the preferences of different stakeholders. In order to address these issues, a novel requirement elicitation and evaluation framework for PSSs is proposed. First, a new requirement elicitation method based on contextual matching is introduced to capture the complete PSS requirements. Second, a hybrid decision-making method that considers the preferences of users and enterprise experts, as well as the uncertainty and stochasticity during the decision-making process, is proposed to determine the prioritization of PSS requirements. Finally, an illustrative case study of an automatic guided vehicle PSS (AGV-PSS) is demonstrated to examine and justify the proposed framework and methods.

Analyzing barriers to sustainable healthcare waste disposal: a hybrid decision-making framework

PurposeIn the recent post-pandemic era, the globe has been anxious for the sustainable disposal of healthcare waste to protect public health, protect the environment and enhance future preparedness. Developing countries, in particular, have struggled to dispose of healthcare waste (HCW) to eradicate the hazardous effects of medical waste generated during and after the deadly COVID-19 pandemic. Hence the purpose of the research paper is to develop a hybrid decision-making framework to identify various barriers for sustainable disposal of healthcare waste use of Grey-Decision Making Trial and Evaluation Laboratory (G-DEMATEL) and Analytical Network Process (ANP).Design/methodology/approachA hybrid framework of Grey-Decision Making Trial and Evaluation Laboratory (G-DEMATEL) and Analytical Network Process (ANP) has been used to rank barriers and sub-barriers in the disposal of healthcare waste.FindingsThe study’s findings suggest that lack of segregation practices, absence of green procurement policy, obsolete technologies and resistance to adopting change management are the topmost causal barriers influencing the remaining barriers. Lack of commitment among healthcare administrations, lack of standard performance measures and resistance to adopting change appear to be the topmost crucial barriers.Practical implicationsThe study’s finding enables all stakeholders to prioritize the barriers systematically for better performance and save resources during the process. The policymakers can use the results to design a clear regulatory framework.Originality/valueThe literature has highlighted the factors and their association with the disposal of healthcare waste mainly in isolation. The results are validated against the Grey-Analytical Hierarchy Process (G-AHP) to ensure the robustness of the proposed framework. This paper is one of the preliminary attempts to propose a framework of the interrelationships of the factors that have a direct role in survival for management education.

A dynamic attribute reduction algorithm based on relative neighborhood discernibility degree

This paper addresses the current existence of attribute reduction algorithms for incomplete hybrid decision-making systems, including low attribute reduction efficiency, low classification accuracy and lack of consideration of unlabeled data types. To address these issues, this paper first redefines the weakly labeled relative neighborhood discernibility degree and develops a non-dynamic attribute reduction algorithm. In addition, this paper proposes an incremental update mechanism for weakly tagged relative neighborhood discernibility degree and introduces a new dynamic attribute reduction algorithm for increasing the set of objects based on it. Meanwhile, this paper also compares and analyses the improved algorithm proposed in this study with two existing attribute reduction algorithms using 8 data sets in the UCI database. The results show that the dynamic attribute reduction algorithm proposed in this paper achieves higher attribute reduction efficiency and classification accuracy, which further validates the effectiveness of the algorithm proposed in this paper.

Optimized Grid Partitioning and Scheduling in Multi-Energy Systems Using a Hybrid Decision-Making Approach

This paper presents a thorough review of our state-of-the-art technique for enhancing dynamic grid partitioning and scheduling in multi-energy source systems. We use a hybrid approach to T-spherical fuzzy sets, combining the alternative ranking order method accounting for the two-step normalization (AROMAN) method for alternating ranking order to enable two-step normalisation with the method based on removal effects of criteria (MEREC) for eliminating criteria effects. This enables us to obtain the highest level of accuracy from our findings. To ascertain the relative importance of these criteria, we use MEREC to perform a rigorous examination of the influence that each evaluation criterion has on the outcomes of the decision-making process. In addition, we use AROMAN to provide a strong foundation for assessing potential solutions by accounting for spherical fuzzy sets to account for any ambiguity. We illustrate how our approach successfully considers several factors, such as social acceptability, technical feasibility, environmental sustainability, and economic feasibility, through the analysis of an extensive case study. Our approach provides decision-makers (DMs) with a rigorous and rational framework for assessing and choosing the best grid division and scheduling options. This is done in an effort to support the administration and design of resilient and sustainable multi-energy systems. This research contributes to the growing body of knowledge in this area by offering insights that help to direct policy, planning, and investment decisions in the shift towards more sustainable energy infrastructures. Moreover, it adds to the growing body of information on multi-criteria decision-making (MCDM) in energy system optimization.

A hybrid decision-making technique based on extended entropy and trapezoidal fuzzy rough number

A hybrid decision-making technique based on extended entropy and trapezoidal fuzzy rough number

Analysis of bridge structure selection using a hybrid decision-making technique

Implementation of sustainability principles in civil engineering has increased the substantive range of bridge engineering. The consideration of additional criteria, in particular ecological and social ones, requires the design process to be supported by appropriate tools. It refers especially to large bridge elements which affect their load-bearing capacity, e.g. girders or decks. The aim of this paper is to develop an original MCDA (Multi-Criteria Decision Analysis) method as a potential tool to support a decision-making process in the selection of material and design alternatives for bridge main girders. Therefore, an advanced hybrid algorithm was created consisting of the following methods: EA FAHP+FDEMATEL+ZUM (Extent Analysis Fuzzy Analytic Hierarchy Process + Fuzzy Decision-Making Trial and Evaluation Laboratory + Zero Unitarization Method), applied at the structure design stage. The pre-dimensioned alternatives selected for analysis were then subjected to an evaluation process based on a complex set of criteria arranged in a hierarchical control structure (HCS). The algorithm has been applied based on the example of a medium span slab-and-girder bridge, assuming 6 alternative concepts of girders, different in material and dimensions. The hybrid method was compared with the EA FAHP method. Analysis results obtained based on judgments from 3 teams of Decision Makers (DM) indicate effectiveness of the proposed algorithm and its practical aspect, which may contribute to improved quality and safety of bridge structures.

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.

“Quasi-Metacognitive Machines: Why We Don’t Need Morally Trustworthy AI and Communicating Reliability is Enough”

Many policies and ethical guidelines recommend developing “trustworthy AI”. We argue that developing morally trustworthy AI is not only unethical, as it promotes trust in an entity that cannot be trustworthy, but it is also unnecessary for optimal calibration. Instead, we show that reliability, exclusive of moral trust, entails the appropriate normative constraints that enable optimal calibration and mitigate the vulnerability that arises in high-stakes hybrid decision-making environments, without also demanding, as moral trust would, the anthropomorphization of AI and thus epistemically dubious behavior. The normative demands of reliability for inter-agential action are argued to be met by an analogue to procedural metacognitive competence (i.e., the ability to evaluate the quality of one’s own informational states to regulate subsequent action). Drawing on recent empirical findings that suggest providing reliability scores (e.g., F1-scores) to human decision-makers improves calibration in the AI system, we argue that reliability scores provide a good index of competence and enable humans to determine how much they wish to rely on the system.

Hybrid decision-making in atmospheric plasma spraying enables human–machine teaming

With the development of human-cyber-physical-production systems in intelligent manufacturing, cyber-supported production based on artificial intelligence is becoming an increasingly powerful means of controlling machines and collaborating with human users. Semi-autonomous systems with a medium degree of automation enable human-centered, flexible, and sustainable production, for instance, in hybrid decision-making. Especially in applications that do not meet the requirements for full automation and when humans are to be involved in their role as qualified decision-makers, teaming-capable systems are desirable and offer considerable advantages. This paper outlines the transdisciplinary concept of human–machine teaming and the role of human cognition in engineering tasks with multi-criteria decision-making. An illustrative real-life example from thermal spray technology is used to show how explainable artificial intelligence models offer targeted, hybrid cyber decision support. This new approach based on fuzzy pattern classifiers combines expert knowledge- and data-based modeling and enables a transparent interpretation of the results by the human user, as shown here using the example of test data from atmospheric plasma spraying. The method outlined can potentially be used to provide hybrid decision support for a variety of manufacturing processes and form the basis for advanced automation or teaming of humans and cyber-physical-production systems.

Analysis of market environment for smart grid technology investments via facial action coding system-enhanced hybrid decision-making model

PurposeThis paper aims to market analysis on the base many factors. Market analysis must be done correctly to increase the efficiency of smart grid technologies. On the other hand, it is not very possible for the company to make improvements for too many factors. The main reason for this is that businesses have constraints both financially and in terms of manpower. Therefore, a priority analysis is needed in which the most important factors affecting the effectiveness of the market analysis will be determined.Design/methodology/approachIn this context, a new fuzzy decision-making model is generated. In this hybrid model, there are mainly two different parts. First, the indicators are weighted with quantum spherical fuzzy multi SWARA (M-SWARA) methodology. On the other side, smart grid technology investment projects are examined by quantum spherical fuzzy ELECTRE. Additionally, facial expressions of the experts are also considered in this process.FindingsThe main contribution of the study is that a new methodology with the name of M-SWARA is generated by making improvements to the classical SWARA. The findings indicate that data-driven decisions play the most critical role in the effectiveness of market environment analysis for smart technology investments. To achieve success in this process, large-scale data sets need to be collected and analyzed. In this context, if the technology is strong, this process can be sustained quickly and effectively.Originality/valueIt is also identified that personalized energy schedule with smart meters is the most essential smart grid technology investment alternative. Smart meters provide data on energy consumption in real time.

Assessment of road safety performance based on Entropy-RSR model

This paper develops a hybrid decision-making model, Entropy-RSR, for benchmarking road safety for Southeast Asian countries at the regional level. This model generates ranking and grouping of the countries, which provides policymakers and practitioners with a valuable tool for policymaking and decision-making in road safety management.

A hybrid decomposed fuzzy multi-criteria decision-making model for optimizing parcel lockers location in the last-mile delivery landscape

Parcel lockers are considered an effective solution to address inefficiencies in last mile delivery. However, choosing the appropriate location is crucial to provide optimal service and improve logistic performance. Therefore, this research aims to assess the most suitable locations for parcel lockers in Dublin, the capital of Ireland, using a novel hybrid decision-making model. This study seeks to extend the Combinative Distance based Assessment method and integrate the Analytical Hierarchy Process method to handle parcel locker location evaluation problems through Decomposed Fuzzy Sets. The proposed model is a pioneering application in real-life scenarios, contributing to the existing knowledge in this field. The integrated model we propose offers a suitable approach for selecting parcel locker locations. The outcomes of this study will provide valuable insights for policymakers, aiding them in making strategic decisions regarding the selection of parcel locker locations within Dublin city.

Optimizing CI Engine Ethanol Fuel Induction Techniques Using the AHP-PROMETHEE II Hybrid Decision Model

Ethanol along with nanoparticles stands out as a promising alternative in the pursuit of environmentally sustainable fuel options, offering a potential solution to the dual challenge of curbing NOx and PM/soot emissions while optimizing engine performance in compliance with stringent pollution regulations for compression ignition (CI) engines. The research study aims to optimize ethanol fuel induction techniques for CI engines. It utilizes a hybrid decision-making approach that integrates the analytic hierarchy process- AHP- for problem structuring and the derivation of preference weights. Subsequently, the preference ranking organization method for enrichment evaluations-PROMETHEE II is applied to assess and rank the existing alternatives. The study entails a methodical assessment of diverse ethanol induction methods across varying engine load ranges, considering multiple criteria including engine performance, emissions, combustion behavior, and exhaust after-treatment efficiency. Hybrid AHP-PROMETHEE II model provides criteria weights and ranks ethanol induction techniques and fuel blends across low, medium, and high engine loads for decision-making. It ensures that the method chosen aligns with goals, such as reducing NOx and soot emissions, optimizing engine performance, enhancing combustion, and minimizing exhaust after-treatment costs for CI engines. According to the research findings, the hybrid AHP-PROMETHEE II model identifies the CI engine operating at medium load with ethanol blending (DE10) and without the use of nanoparticles as the preferred choice. Additionally, AHP-PROMETHEE II (AHP derived criteria weights) and PROMETHEE II (direct rating derived criteria weights) models, suggested DE10 with nanoparticle (DE10_NP) using blending technique at low load and combined blending-fumigation technique with nanoparticles at high load. However, at medium load, PROMETHEE II recommends DE10_NP, while AHP-PROMETHEE II recommends DE10 blending technique. To assess the performance and reliability of this model, the consistency ratio and Spearman's rank correlation coefficient indices were computed, yielding values of 0.05 and 0.59, respectively. Both indices fall below the predetermined threshold limits, indicating a high level of consistency of the model.

A hybrid decision-making framework for a supplier selection problem based on lean, agile, resilience, and green criteria: a case study of a pharmaceutical industry

A hybrid decision-making framework for a supplier selection problem based on lean, agile, resilience, and green criteria: a case study of a pharmaceutical industry

Hybrid Decision-Making for Intelligent High-Speed Train Operation: A Boundary Constraint and Pre-Evaluation Reinforcement Learning Approach

Hybrid Decision-Making for Intelligent High-Speed Train Operation: A Boundary Constraint and Pre-Evaluation Reinforcement Learning Approach