Decision Making Advances (DMA) publishes high-quality scientific papers that contribute significantly to the fields of decision-making, operational research, artificial intelligence, and applied mathematics. The material published is of high quality and relevance, written in a manner that makes it accessible to this wide-ranging readership. Decision Making Advances is an international peer-reviewed journal covering the main aspects of decision-making and providing a source of information for applied scientists. Decision Making Advances encourages research endeavors that identify organizational risks and opportunities by exploiting patterns found in historical and transactional data. Decision Making Advances promotes the application of simulation in enterprise and organizational contexts for examining and comparing options and scenarios prior to implementation. Decision Making Advances promote research that can help decision-makers make the best choice by means of various optimization models. Decision Making Advances invites research that supports the joint application of predictive models and optimization technology to create better solutions for decision-makers. Decision Making Advances invites research that utilizes the latest techniques in data mining, analysis, and performance management to help decision-makers gain and sustain a competitive edge. Decision Making Advances (DMA) aims at this important task by providing a venue for high-quality scientific papers that contribute significantly to the field of decision science and artificial intelligence. The material published is of high quality and relevance, written in a manner that makes it accessible to a wide-ranging readership. Papers reporting original theoretical and/or practice-oriented research or extended versions of the already published conference papers are all welcome. The scope of the journal covers the whole spectrum of decision science. Papers for publication are selected through a double-blind peer-review process to ensure originality, relevance, and readability. In doing that, the objective is not only to keep the quality of published papers high but also to provide a timely, thorough, and balanced review process. General Journal guidelines for authors Decision Making Advances publishes research articles, reviews, short communications, and case studies. Research articles must include: motivation for the work, an adequate overview of the representative work in the field including up-to-date references, a clear statement of the novelty in the presented research, suitable theoretical background, one or more examples to demonstrate and discuss the presented ideas and, finally, conclusions. Short communications are usually 4-7 pages long, research articles and case studies 8-14 pages, while reviews can be longer. Page number limits are not strict and, with appropriate reasoning, the submitted articles can also be longer or shorter. Authors are requested to follow the DMA guidelines and strictly format their manuscripts as per the article template that is available here. If extensions of previously published conference papers are submitted, Editors will check if sufficient new material has been added to fulfill the journal standards and qualify the submission for the review process. The added material must have not been previously published. New results are desired but not necessarily required; however, the submission should contain expansions of key ideas, examples, elaborations, etc. of the conference submission. Aims and Scope: The principal aim of the journal is to bring together the latest research and development in various fields of decision science. We would like to highlight that papers should refer to Aims and scope, but they are not limited to. DMA’s acceptance rate is 22%. In 2024, the median time from submission to acceptance for all articles was 78 days, and 11 days from acceptance to online publication in the ONLINE FIRST section. The ONLINE FIRST section of DMA lists the papers accepted for publication and copy-edited but not yet assigned to an issue. Publication Frequency: One issue per year is published online, but processed and accepted papers, with full bibliographic data, are added to the issue continuously over the whole year. Open Access: This is an open-access journal which means that all content is freely available without charge to the user or his/her institution. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles in this journal without asking prior permission from the publisher or the author. Publication fee: There is no submission charge or publication fee. Publication in DMA is free of charge for the authors. Frequency: One issue/ year Final Decision: 78 days Production Process: 8 days Acceptance rate: 22% Plagiarism: <18% eISSN: 2956-2384
Forecasting Aluminum Futures Prices: A Comparative Evaluation of Classical, Deep Learning and Transformer-based Models
Forecasting commodity prices remains a challenging task due to market volatility, structural breaks, and changing economic conditions. This study evaluates the forecasting performance of classical econometric, deep learning, convolutional, and Transformer-based models for aluminum futures prices. Daily aluminum futures data are analysed using 11 forecasting approaches. Forecasting experiments are conducted for two distinct evaluation periods representing the years 2022 and 2025 to assess the robustness of model performance under different market environments. The empirical results reveal substantial differences in forecasting performance across model families. Recurrent neural network architectures, particularly GRU and RNN, consistently achieve the lowest forecasting errors across most horizons. The classical ARIMA model remains highly competitive despite its relative simplicity. In contrast, Transformer-based models generally fail to outperform simpler alternatives and frequently produce higher forecast errors. Statistical comparisons based on Diebold–Mariano tests and Model Confidence Set procedures indicate that performance differences among the best-performing models are often limited, suggesting that increased model complexity does not necessarily translate into superior predictive accuracy. Overall, the findings highlight the importance of empirical model evaluation and demonstrate that parsimonious forecasting approaches can remain effective competitors to substantially more complex machine learning architectures in aluminum futures price forecasting.
Copyright (c) 2026 László Vancsura, Tibor Tatay, Tibor Bareith, Kornélia Mészáros https://creativecommons.org/licenses/by/4.0
Acceleration and Regenerative Braking Energy of Electric Trains: A Review for Energy-Management Decision Making
Optimizing traction energy is central to decarbonizing electrified railways, where two quantities dominate a train's electricity balance: energy consumed during acceleration and recovered during regenerative (recuperative) braking. Both are based on calculations or measurements, yet inconsistent definitions, metering boundaries, and reporting conventions make published values difficult to compare for energy-management decision-making. This paper reviews the possibilities for calculating and measuring electric locomotives and multiple units across 100 publications. A two-axis framework crosses energy type with analytical task, cross-cut by alternating versus direct-current supply and locomotive-hauled versus distributed-traction trains. For acceleration, it covers the multiplicative correction-factor method, effective mass and running resistance formulations, traction-measuring cars, onboard logging, optical character recognition, inertial sensors, and machine learning. For regeneration, it contrasts analytical, simulation-based, and data-driven calculations with onboard, wayside, and hybrid measurements, network receptivity, and usable energy. Measured energy depends strongly on sampling rate and metering boundary; wayside measurements underestimate recovery on direct-current systems; multiple units recover more efficiently than locomotive-hauled trains; and driving style and timetable structure dominate. Representative magnitudes recur: acceleration-energy correction factors of about 1.3 (range ≈1.25–1.5), regenerative-chain efficiencies of roughly 0.70–0.85, and recoverable braking energy of 10–30% of traction energy. The chief obstacle to comparability and reliable decision-making is methodological rather than technological; high-frequency, position-synchronized measurement, multi-train simulation, train-specific efficiency data, and harmonized metrics define best practice.
Copyright (c) 2026 Szabolcs Fischer https://creativecommons.org/licenses/by/4.0
Beyond Teleoperation: Enhancing VLA Robustness via Explicit Kinematic Retargeting of Human Demonstrations
Vision-Language-Action (VLA) models have demonstrated remarkable capabilities in generalized robotic control, yet their scalability is fundamentally bottlenecked by the high cost and low diversity of teleoperated data. While abundant, human demonstration videos cannot be directly utilized for policy training due to the severe morphological differences between human anatomy and robotic manipulators. To bridge this embodiment gap, this work proposes a lightweight retargeting pipeline that kinematically retargets human interaction data (DexYCB) onto six-degree-of-freedom manipulator trajectories to fine-tune policies based on the pi0.5 architecture. By prioritizing Cartesian positional alignment via constrained Inverse Kinematics (IK) and introducing an object-based grasping heuristic, smooth geometric priors are generated without relying on computationally heavy visual synthesis. Physical evaluations demonstrate that retargeted models significantly outperform standard teleoperation (40.6% success rate), achieving 65.6% success via co-training and a peak 78.1% success rate via two-stage cross-embodiment co-training. Furthermore, evaluations under extreme visual clutter reveal that explicitly retargeted policies exhibit immunity to semantic visual distractors. Finally, we examined and analysed Terminal State Ambiguity, a temporal failure mode where generative models fail to terminate the task when exposed to scenarios similar to the nature of human video priors.
Copyright (c) 2026 José António Nunes Andrade, Mauro Castelli https://creativecommons.org/licenses/by/4.0
From Waste to Value: A Hybrid Decision-Making Framework for Industrial Symbiosis Implementation in an Industrial Park
The transition from a linear to a circular economy is a crucial strategy for addressing resource scarcity, waste generation, and climate change. Industrial Symbiosis (IS) plays a central role in this transition by facilitating the exchange of waste and by-products across industries, thereby improving both environmental and economic performance. This study develops and applies a hybrid methodology for IS implementation in the Manisa Industrial Park (Türkiye), one of the country’s largest industrial zones. The proposed framework integrates Multi-Criteria Decision-Making methods (AHP, VIKOR, and ELECTRE), Material Flow Analysis (MFA), and Mixed-Integer Linear Programming (MILP) to identify sectoral priorities through expert consultation and survey-based criteria weighting, and to optimize inter-company waste exchange networks. The findings designate the metal, plastic, plant-based, and chemical sectors as priority domains, while the optimization results indicate a relative improvement of up to 35% under normalized assumptions through optimized waste exchange networks. The proposed framework bridges theoretical models with real-world applications, providing a replicable decision-support tool for enterprises, policymakers, and industrial park administrators in emerging economies, while aligning with key Sustainable Development Goals (SDGs) related to decent work, sustainable cities, and climate action.
Copyright (c) 2026 Bengü Güngör, Enes İçer, Sema Kayapınar Kaya, Tuğba Keskin Gündoğdu, Dragan Pamucar https://creativecommons.org/licenses/by/4.0
Industrial Waste Management Planning Evaluation Using Integrated Best-Worst Technique With Fuzzy TOPSIS
The management of industrial waste and electronic waste is increasingly critical today. Industrialization and inadequate planning lead to daily industrial byproducts and waste that pose significant challenges to society. Industrial waste also presents increasing and complex challenges, resulting in soil and water contamination, degradation of air quality, and considerable threats to ecosystems and human health. To keep society safe from the detrimental consequences of waste, it's essential to have sustainable and efficient methods for managing industrial waste. In this paper, we discuss how an expert-based multi-criteria decision-making technique can aid in implementing an effective industrial waste management strategy by analyzing various expert reviews and public opinions. Multiple waste management strategies are reviewed in response to various important factors, including environmental, social, legal, compliance, cost, and technical considerations. These decision-making factors are analyzed and ranked based on their weights using the Best-Worst technique. Subsequently, the ranking for the effective industrial waste management(IWM) strategy is obtained through the fuzzy TOPSIS ranking technique. A sensitivity analysis has been conducted based on different factor weights. Finally, the ranking of the management strategy has been verified by utilizing another well-established decision-making technique.
Copyright (c) 2026 Haridas Mondal, Totan Garai, Otavio Oliveira, Shariful Alam https://creativecommons.org/licenses/by/4.0
Strategies for Effective Regional Technical and Vocational Education and Training Integration via an Interval-Valued Spherical Fuzzy Approach
This article explores the current challenges of the technical and vocational education and training (TVET) system in West African countries and identifies strategies for effective integration within the Economic Community of West African States (ECOWAS). The study proposed a decision-making approach including the interval-valued spherical fuzzy (IVSF) Stepwise Weight Assessment Ratio Analysis (SWARA)-the Nearest Solution to References (REF), marking the first application of these techniques in combination to investigate regional TVET system integration within ECOWAS. Furthermore, this study introduces the FF-REF into the decision-making literature for the first time. The findings indicated that while limited funding and fragmentation of TVET programs are the two most critical challenges to effective regional TVET integration, the harmonization of TVET programs is the most appropriate strategy to overcome them. This study contributes to the literature by providing a new integrated framework for assessing TVET system integration at the regional level. It also offers practical insights for regional institutions and policymakers by prioritizing key challenges and actionable strategies to strengthen cooperation and harmonization of TVET systems across ECOWAS member states
Copyright (c) 2026 Simeng Dong, Ahmet Aytekin, Mouhamed Bayane Bouraima https://creativecommons.org/licenses/by/4.0
Challenges for Adapting High-speed Rail Network in Developing Countries: A Picture Fuzzy MCDM based Approach
High-speed rail adoption in developing countries faces challenges for several reasons. Our research work identifies the most impactful challenges of it. It is an extremely demanding work. It mainly depends on several factors, including high capital costs and financing issues, the purchase of land and resettlement, political instability that affects policy continuity, maintenance and operating costs, the current market value, technology and technical skills, and the impact on both the environment and human society. In this study, the proposed adoption challenges for high-speed rail networks in developing countries are constructed and processed using the Multi-Criteria Decision Making (MCDM) process in an uncertain environment. Here, we apply the Fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL) technique-based MCDM methodology to evaluate the criteria weights. We gather valuable opinions from three decision-makers (DMs) for data collection. Picture Fuzzy Numbers (PFNs) are utilised as uncertainty tools to address ambiguity.
Copyright (c) 2026 Aditi Biswas, Kamal Hossain Gazi, Sankar Prasad Mondal, Arijit Ghosh https://creativecommons.org/licenses/by/4.0
Artificial Intelligence Driven Sustainable Consumption Decisions of Generation Z: A Systematic Literature Review
The need to engage in sustainable consumption is increasing, and extrapolating. Consumer decision-making on artificial intelligence (AI) is changing how people make decisions and consume products, providing better and more personalized advice based on data. Being a digital-native and sustainability-sensitive generation, Generation Z is one of the key factors that determine the future consumption trends; nevertheless, current studies tend to view AI and sustainable consumption in isolation. The given study attempts to fill this gap by applying systematic literature review with the help of the Dimensions.ai database. An initial list of 2,606 records was then found and 876 publications were selected based on PRISMA-based screening. The thematic and bibliometric analysis demonstrate such topics such as AI-based recommendation systems, influence on behaviour, trust and explainability, and connection with multi-criteria decision-making (MCDM). The research reveals that there exist substantial gaps in the combined framework and real-life application, and suggests a theoretical framework of the connection between AI, decision-making and sustainable consumption. The results provide useful information that researchers and practitioners can use to design smart systems that can spur sustainable consumption among the generation z.
Copyright (c) 2026 Supriya Sahu, Sushil Kumar Sahoo, Bibhuti Bhusan Choudhury, Prasant Ranjan Dhal, Ipsita Dhar, Monika Imreh-Tóth https://creativecommons.org/licenses/by/4.0
Evaluation of Customer Loyalty Using the Fuzzy Multi-Criteria Decision-Making Model
Customer loyalty is a crucial aspect for maintaining competitiveness in a turbulent market that is exposed to the influence of intense changes on a daily basis. In order for companies to retain loyal customers and potentially attract new ones, it is necessary to monitor their own performance and maintain daily communication with their customers. The aim of this paper is to determine the level of customer loyalty across six age groups for two types of perfumes: oriental and designer. Through performed questionnaire with experts in the field and a review of relevant literature, nine criteria were defined based on which customer loyalty was evaluated. IMF SWARA (Improved Fuzzy Stepwise Weight Assessment Ratio Analysis) integrated with the Fuzzy Bonferroni operator was used to determine the weighting coefficients of the criteria. The Fuzzy MARCOS (Measurement Alternatives and Ranking according to Compromise Solution) method, due to its methodological framework and expert preferences, was used to determine the level of loyalty of age groups for both types of perfumes. The results show that the youngest category (aged 15-25) is the most loyal to oriental perfumes, while the group aged 26-35 demonstrates the highest loyalty to designer perfumes. The findings were tested through several additional analyses, like sensitivity analysis, comparative analysis, and correlation tests, which confirmed the obtained results.
Copyright (c) 2026 Žaneta Kavaliauskienė, Erika Jonuškienė, Željko Stevic, Boris Novarlić https://creativecommons.org/licenses/by/4.0
MAXC2: An Enhanced Iteration of the MAXC Method for Criterion Weight Determination
Determining weights for criteria is a crucial task in multi-criteria decision-making (MCDM) problems. MAXC (MAXimum of Criterion) is an objective weighting method that calculates criterion weights based on the definition of maximum criterion values from the initial matrix and is known as a simple method in the calculation process. However, the data normalization method used in this method cannot be applied if the total value of a certain criterion across all alternatives equals zero. This study improves the MAXC method by creating a new version named MAXC2. MAXC2 improves upon MAXC by changing the normalization formula used in the MAXC method. When both methods are applicable, the maximum difference in the weights calculated by these two methods is only about 4.76%. When only MAXC2 can be used, the calculation of criterion weights using the MAXC2 method ensures high stability in the ranking of alternatives, with the minimum average Spearman correlation coefficient between ranking methods being 0.8667. MAXC2 overcomes the limitations of the MAXC method and can be considered a highly reliable method.
Copyright (c) 2026 Do Duc Trung, Branislav Dudic, Bui Thi Thu Trang, Aleksandar Ašonja https://creativecommons.org/licenses/by/4.0
A Neutrosophic Expert Reputation Rating (ERERA) based Barrier Classification Model for the Sharing Economy
This paper considers barriers to the development of the sharing economy from the perspective of actors who are directly faced with the challenges of running a business. An original barrier classification model referred to as the two-phase neutrosophic Expert REputation RAting (ERERA) is formulated. Uncertainties regarding severity estimations of barriers and reputation appraisals of decision analysts are addressed using type-2 neutrosophic numbers (T2NNs). In the first phase, neutrosophic ERERA differentiates the decision analysts by taking into account their reputation indicators and built-in trade-off parameters. In the second phase, this innovative barrier classifier determines the severity degrees of the barriers and categorizes them by taking into account the nature of the addressed problem. The number of intrinsic parameters of neutrosophic ERERA equals the number of reputation indicators plus classification parameters, thus making it flexible for real-life multi-criteria group decision-making applications. Based on research conducted in Serbia, a set of critical severity barriers is found. In-depth sensitivity analysis confirmed the stability of the initial classes produced by neutrosophic ERERA. Funding, pre-opening requirements, and familiarity should be regarded as critical severity barriers to the sharing economy adoption in Serbia.
Copyright (c) 2026 Vladimir Simic, Nataša Bojković, Tanja Živojinović, Marijana Petrović, Erfan Babaee Tirkolaee https://creativecommons.org/licenses/by/4.0
From Transformational Change to Organizational Performance: Unpacking the Moderating Role of Organizational Agility
The increasing pace of digitalization has made digital transformation a critical strategic priority for small and medium-sized enterprises (SMEs), particularly in developing economies. This study examines the effects of Digital Transformation Leadership (DTL) on Business Model Innovation (BMI) and Firm Performance (FP), while investigating the moderating role of Organizational Agility (OA). Data were collected from 500 employees working in Pakistani SMEs and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings reveal that Digital Transformation Leadership has significant positive effects on Organizational Agility, Business Model Innovation, and Firm Performance. However, Organizational Agility does not significantly moderate the relationships between Digital Transformation Leadership and either Business Model Innovation or Firm Performance. The structural model demonstrates satisfactory explanatory and predictive capability, confirming the strategic importance of Digital Transformation Leadership in enhancing organizational innovation and performance. This study contributes to the literature by providing empirical evidence from an emerging economy and by demonstrating that Organizational Agility functions as an important organizational capability but does not strengthen the effects of Digital Transformation Leadership on Business Model Innovation or Firm Performance. The findings suggest that managers should prioritize leadership capabilities and digital transformation initiatives while simultaneously developing complementary organizational capabilities beyond agility to maximize digital transformation outcomes. The study also provides practical implications for policymakers seeking to strengthen SMEs' digital competitiveness through investments in digital infrastructure, digital skills, and innovation support programs.
Copyright (c) 2026 Md Billal Hossain, Mujib Ur Rahman, Akan S. Nurbatsin, László Vasa https://creativecommons.org/licenses/by/4.0
Integrated Fuzzy SiWeC-CORASO Framework for Sustainable Supplier Selection in the Construction Industry
The construction industry is confronted with the challenge of greenhouse gas emissions. Additionally, this industry is responsible for CO2 emissions through its supply chain operations and the production of construction materials. To address this issue, this research investigates the role of suppliers in the decarbonisation process. Selecting a supplier is a strategic choice for every organization. The goal of this research is to evaluate suppliers of modular materials for the purpose of reducing CO2 emissions in the construction industry. In this research, four generic suppliers were analysed based on 11 criteria. The evaluation of these suppliers was conducted using seven companies involved in the construction of modular homes. A fuzzy approach was employed to assess the significance of these criteria and to evaluate the degree to which suppliers fulfill these established objectives. By utilizing the fuzzy SiWeC (Simple Weight Calculation) technique, the significance of the criteria was analyzed, revealing that the foremost criterion in the selection of suppliers is waste management and recycling. Suppliers were ranked through the fuzzy CORASO (Compromise Ranking from Alternative Solutions) method, demonstrating that a large international supplier best meets the established goals. The research conducted indicated that with the appropriate selection of suppliers, it is feasible to impact the reduction of carbon dioxide emissions within the construction industry.
Copyright (c) 2026 Jurica Bosna, Adis Puška, Božanić Darko https://creativecommons.org/licenses/by/4.0
A Hybrid Simulation and the RAWEC Model for Performance Evaluation of Pharmacy Service Strategies
Outpatient pharmacy operations frequently experience inefficiencies due to unpredictable demand, limited resources, and patient behaviors such as queue jockeying, resulting in extended wait times and reduced performance. This research introduces a hybrid framework that combines Discrete-Event Simulation (DES) with Multi-Criteria Decision-Making (MCDM) to assess and prioritize improvement strategies for complex systems. A discrete-event simulation model was created to replicate a multi-server pharmacy, incorporating realistic factors such as time-dependent patient arrivals, variable service durations, and explicit jockeying behavior. The model produced performance data for five strategies: base case, single queue, self-service kiosks, dynamic staffing, and restricted jockeying. The output metrics, including waiting time, queue length, staff utilization, throughput, and jockeying frequency, were organized into a decision matrix. The Ranking of Alternatives with Weights of Criterion (RAWEC) method, a contemporary multi-criteria decision-making technique utilizing a reward–punishment mechanism grounded in average performance, was subsequently employed to rank the strategies according to varying managerial weightings (patient-centric versus efficiency-centric). The findings indicate that the single queue strategy (A1) emerged as the optimal choice, attaining the highest rank in both patient-centric and efficiency-centric managerial frameworks. This suggests that the implementation of a single-line system is advantageous for both patient experience and operational efficiency, thereby removing the perceived trade-off between these objectives in this particular instance. The hybrid DES–RAWEC approach offers managers a robust, data-driven instrument for formulating strategic decisions that reconcile competing objectives within complex service environments, such as healthcare pharmacies.
Copyright (c) 2026 Ibrahim Badi, Mouhamed Bayane Bouraima, Naibei Dan Maraka https://creativecommons.org/licenses/by/4.0
Optimizing Priority Sequencing Rules in Parallel Machine Scheduling: An Evaluation and Selection Approach using Hybrid MCDM Techniques
Priority sequencing criteria are of utmost importance in the determination of the sequence in which jobs are processed at workstations in parallel machine scheduling. The utilization of diverse priority rules can result in varied sequencing arrangements, hence requiring more experimentation to ascertain the optimal rule. Hence, it is imperative to formulate a thorough approach for the selection of the most suitable priority sequencing rule from the standpoint of management decision-making. The objective of this research is to analyze and compare six different priority sequencing rules in the context of parallel machine scheduling. Additionally, a methodology is proposed for the assessment and selection of the most suitable rule. This methodology combines the full consistency method (FUCOM) with the measurement of alternatives and ranking according to compromise solution (MARCOS) method, which are both multi-criteria decision-making techniques. When reviewing and selecting the optimal priority sequencing rule, seven parameters are taken into consideration. The weights of these criteria are computed using the FUCOM method, while the relative proximity values of all priority sequencing rules are derived by the MARCOS method. The data indicate that the priority sequencing rules are prioritized according to their level of importance. The approach outlined in this study is essential for workstation management to make well-informed decisions regarding the choice of the most advantageous priority sequencing rule for parallel machine scheduling.
Copyright (c) 2024 Ibrahim Badi, Mouhamed Bayane Bouraima, Yanjun Qiu, Željko Stević https://creativecommons.org/licenses/by/4.0
Transfer Learning Empowered Bone Fracture Detection
Detection of bone fractures using modern technology has significant implications in medical analysis and artificial intelligence. This importance is especially pronounced in the realm of deep learning. Deep learning techniques find extensive application in the field of medicine and disease classification. The early identification of bone fractures is crucial for efficient treatment planning and patient care. Our research proposes a transfer learning-based model for predicting bone fractures using a dataset of bone X-ray images. These images will be classified into two categories: normal and bone fracture, based on extracted features. Our proposed model, the Bone Fracture Detection Transfer Learning Algorithm (BFDTLA), achieved an average accuracy of 97% on the dataset. The BFDTLA model demonstrated superior performance when compared to previous quantitative and qualitative research studies. This research focuses on the early detection of bone fractures using transfer learning algorithms, emphasizing the significance of accurate and timely diagnosis.
Copyright (c) 2024 Muhammad Daniyal Baig, Hafiz Burhan Ul Haq, Waseem Akram, Awais Shahzad https://creativecommons.org/licenses/by/4.0
Development of an Algorithm and Software for Optimal Route Selection
The paper deals with developing an optimal route selection algorithm and software, which, taking into account various criteria, ensures the adoption of efficient and economical route selection decisions. The algorithm is based on permutations for defining routes. It utilizes multi-criteria analysis methods such as TOPSIS and CRITIC, which enable the selection of optimal routes based on various factors, including cost optimization, time savings, safety measures, etc. The paper also presents the proposed algorithm's software, which helps to solve real-world problems related to route selection.
Copyright (c) 2025 Irakli Basheleishvili, Manana Chumburidze, Elza Bitsadze, Lia Janadze https://creativecommons.org/licenses/by/4.0
An Extended AROMAN Method for Cargo Bike Delivery Concept Selection
Nowadays, cargo bikes play a vital role in the last-mile delivery process. Parcel distribution by cargo bikes becomes a more accessible and ecologically friendly solution. This paper addresses the investment decision on the cargo bike delivery concept selection problem. We investigated a better solution in terms of whether the company needs to perform the delivery by investing in its fleet of cargo bikes or renting cargo bikes from a third party. The third solution is to combine those two alternatives. This case considers four criteria: cargo bike procurement cost, cargo bike maintenance cost, return on investment, and financial profitability. To solve this problem, we applied the extended alternative ranking order method accounting two-step normalization (AROMAN) method. The results compared with the MARCOS and ARAS methods confirmed that delivery concept 2 (i.e. renting cargo bikes from third-party providers) represented the best solution for the e-commerce company.
AROMAN; MCDM; Cargo Bikes; Last-Mile Delivery
Copyright (c) 2023 Sara Bošković, Libor Švadlenka, Momčilo Dobrodolac, Stefan Jovčić, Marina Zanne https://creativecommons.org/licenses/by/4.0
Smart and Sustainable Waste Tire Management: Decision-Making Challenges and Future Directions
The vehicle number has grown significantly in recent decades, resulting in a significant increase in the number of waste tires globally. The environment and public health are seriously endangered by the growing amount of waste tires. As a result, considerable effort is being put into the search for viable alternatives for sustainable management. Even though numerous treatments for waste tires have already been tested and proved successful, their continued industrial implementation remains difficult in both everyday life and research efforts. Finding practical, cost-effective, and ecologically friendly ways to manage waste tires is one of the biggest environmental challenges of the 21st century. By focusing on smart tire waste management, this study examines some of the most important decision-making challenges that support social and environmental sustainability. The main topics of this research are the examination of certain fundamental problems and challenges, as well as some key management strategies for this environmentally unfavorable type of increasing waste. This comprehensive analysis not only identifies significant gaps but also establishes the foundation for addressing some of the most significant decision-making issues in waste tire management by providing recommendations for further research in this important and vulnerable area of academic and practical analysis.
Waste Tires; Waste Management; Challenges; Fundamental Trends.
Copyright (c) 2023 Svetlana Dabic-Miletic, Vladimir Simic https://creativecommons.org/licenses/by/4.0
Overcoming Obstacles to Renewable Energy Development in Libya: An MCDM Approach towards Effective Strategy Formulation
Renewable energy development in Libya faces numerous obstacles that hinder its progress. This paper aims to identify these obstacles and propose effective strategies to overcome them. Based on the literature review and expert opinions, eight obstacles were identified: lack of infrastructure, dependence on fossil fuels, lack of a stable investment climate, political instability, weak regulatory framework, varying environmental conditions, lack of public awareness, and technological barriers. The analytic hierarchy process (AHP) method was used to calculate the weights of these obstacles. The results showed that lack of infrastructure was the most critical obstacle, followed by dependence on fossil fuels. Seven strategies were suggested to overcome these obstacles: encouraging private sector investment, providing financial incentives, strengthening the regulatory framework, capacity building, promoting public awareness, technology transfer, and international cooperation. The combined compromise solution (CoCoSo) method was used to rank these strategies based on their effectiveness. The results showed that encouraging private sector investment was the most important strategy to overcome the obstacles. The findings of this paper can support decision-makers in Libya to take the right decisions and allocate resources effectively to overcome the identified obstacles and promote renewable energy development. Additionally, the paper provides insights into other countries facing similar challenges in the development of renewable energy.
Copyright (c) 2023 Ibrahim Badi, Željko Stević, Mouhamed Bayane Bouraima https://creativecommons.org/licenses/by/4.0
A Comprehensive Review of Multiple Criteria Decision-Making (MCDM) Methods: Advancements, Applications, and Future Directions
This research paper presents a comprehensive review of Multiple Criteria Decision-Making (MCDM) methods, encompassing their advancements, applications, and future directions. The study begins with an introduction emphasizing the significance of MCDM in complex decision-making scenarios. Through a systematic literature review, recent developments in MCDM techniques are examined, including multi-objective methods, fuzzy-based approaches, data-driven models, and hybrid methodologies. The strengths and limitations of each method are critically analyzed. Furthermore, the paper investigates the diverse applications of MCDM in domains such as business, engineering, environment, healthcare, and public policy, highlighting the practical implications through real-world case studies. The study then identifies emerging trends and challenges in MCDM research, discussing the integration of MCDM with emerging technologies, enhancing robustness and adaptability, addressing uncertainty, and identifying unexplored domains for potential application. This comprehensive review serves as a valuable resource for decision-makers and researchers, providing insights into the advancements, applications, and future directions of MCDM methods.
Copyright (c) 2023 Sushil Kumar Sahoo, Shankha Shubhra Goswami https://creativecommons.org/licenses/by/4.0
A Novel Hybrid Decision Making Framework for Comparing Market Performance of Metaverse Crypto Assets
With proliferation of smartphone usage and rapidly increasing internet access, the financial sectors has been witnessing a transformational change in the last few years, leading to the digital economy. The cryptocurrencies (digital assets) and the metaverse assets that use blockchain technology extensively have emerged as an attractive investment option to the investors. The review of the extant literature shows a scantiness of research in comparing the performance of metaverse crypto assets based on market indicators. In this regard, the current work aims to provide a framework to compare the metaverse crypto assets based on their market performance. To this end, the present paper proposes a novel hybrid framework such as Logarithmic Percentage Change driven Compromise Solution based Appraisal (LOPCCSA). To compare the alternatives the variables like return, momentum of the daily closing price, market capitalization, trading volume and risk (in terms of historical volatility realized over the study period 2022). From the result we observe that the momentum of the closing prices and volatility of the price movements hold the higher importance as derived by calculation of objective weights. Theta Network (A10) comes out as the top performer ahead of ApeCoin (A1) or MANA (A3) or Internet Computer (A6). The comparison of the results obtained by LOPCCSA with other MCDM models show considerable consistency. The sensitivity analysis indicates that LOPCCSA provides a stable solution. The present paper is a distinct attempt that shall attract the decision makers and investors to carry out a deeper and comprehensive analysis of the metaverse crypto assets in near future.
Cryptocurrencies; Metaverse coins; Multi-Criteria Decision Making; MCDM; Compromise solution; Logarithmic Percentage Change driven Compromise Solution based Appraisal.
Copyright (c) 2023 Dragan Pamucar, Sanjib Biswas https://creativecommons.org/licenses/by/4.0
Wheelchair Accessibility: Bridging the Gap to Equality and Inclusion
This research paper investigates the impact of wheelchair accessibility on the overall well-being and quality of life of individuals with mobility limitations. The paper highlights the importance of wheelchair accessibility as a key determinant of independence, social participation, and overall societal inclusion. By examining the barriers and challenges faced by wheelchair users in various environments, including public spaces, workplaces, and educational institutions, this study sheds light on the profound implications of limited accessibility. Furthermore, it explores the benefits and potential outcomes of improved wheelchair accessibility, such as increased opportunities for employment, education, and social engagement. The research emphasizes the need for proactive measures, including policy reforms, infrastructure modifications, and awareness campaigns, to enhance wheelchair accessibility and break down the physical and attitudinal barriers that hinder full participation. By recognizing wheelchair accessibility as a crucial component of a more inclusive society, this research contributes to the ongoing dialogue and advocacy efforts aimed at promoting equal rights and opportunities for individuals with mobility impairments.
Copyright (c) 2023 Sushil Kumar Sahoo, Bibhuti Bhusan Choudhury https://creativecommons.org/licenses/by/4.0
Aczel-Alsina Aggregation Operators Based on Complex Single-valued Neutrosophic Information and Their Application in Decision-Making Problems
To develop the theory of aggregation operators based on the Aczel-Alsina t-norm and t-conorm is a very challenging task for individuals, where the idea of Aczel-Alsina norms is the modified version of the algebraic norms. The main theme of this manuscript is to evaluate the Aczel-Alsina operational laws and Aczel-Alsina aggregation operators for complex single-valued neutrosophic (CSVN) information such as CSVN Aczel-Alsina weighted averaging (CSVNAAWA), CSVN Aczel-Alsina ordered weighted averaging (CSVNAAOWA), CSVN Aczel-Alsina hybrid averaging (CSVNAAHA), CSVN Aczel-Alsina weighted geometric (CSVNAAWG), CSVN Aczel-Alsina ordered weighted geometric (CSVNAAOWG), CSVN Aczel-Alsina hybrid geometric (CSVNAAHG) operators, idempotency, monotonicity, and boundedness. Additionally, in the presence of the above-derived operators, we illustrated a model of the MADM “multi-attribute decision-making” procedure for CSVN values. Finally, we employed various numerical examples to demonstrate the supremacy and efficiency of the discovered theory to compare it with various prevailing operators.
Copyright (c) 2023 Areeba Naseem, Maria Akram, Kifayat Ullah, Zeeshan Ali https://creativecommons.org/licenses/by/4.0
A Transportation Problem Considering Fixed Charge and Fuzzy Shipping Costs
This paper considers a fixed-charge transportation problem with fuzzy shipping costs. Whereas the shipping costs of routes are fuzzy intervals, with increasing linear membership functions, fixed costs, supplies, and demands are deterministic numbers. By defining a membership function associated with the objective values and utilizing Bellman-Zadeh’s max-min criterion instead of the main problem, a new crisp nonlinear mixed-integer programming problem is formulated. To solve this non-linear programming problem, at first, we find optimality conditions for a feasible solution, then, using these optimality conditions, reformulate the non-linear programming problem as a linear mixed-integer fractional programming problem, and finally transform the fractional programming problem as a mixed-integer linear programming problem, which can be solved using the existence methods. An illustrative example is solved to explain the presented details.
Copyright (c) 2023 Esmaiel Keshavarz, Ali Mahmoodirad, Sadegh Niroomand https://creativecommons.org/licenses/by/4.0
Evaluation based on Relative Utility and Nonlinear Standardization (ERUNS) Method for Comparing Firm Performance in Energy Sector
The present paper introduces a new multi-criteria decision making (MCDM) model such as Evaluation based on Relative Utility and Nonlinear Standardization (ERUNS). The proposed method provides a number of unique features such as use of non-linear standardization combined with a new definition for calculating utility values of the alternatives and design flexibility. The new method is applied to compare the firm performances using Economic, Environment, Social and Governance (EESG) framework. Over the last few decades alongside economic gain, the firms have been equally emphasizing on environmental protection, social values and good governance. This paper aims to bring in the economic aspect in terms of market capitalization and enterprise value while uses rating scores for environment, social and governance to put forth an extended performance measurement framework. A set of eight leading firms belonging to the energy sector listed to NSE, India has been considered for the period FY 2022-23 as a case study. The criteria weights are calculated using the Logarithmic Percentage Change-driven Objective Weighting (LOPCOW) method. It is observed that ERUNS provides a stable and reliable ranking result free from the rank reversal issue. This paper shall be of interest to both analysts and practitioners in terms of its robust method and practical performance assessment framework.
Economic, Environment, Social and Governance (EESG) performance
Evaluation based on Relative Utility and Nonlinear Standardization
Logarithmic Percentage Change-driven Objective Weighting
Copyright (c) 2024 Sanjib Biswas, Dragan Pamucar, Satirtha Dawn, Vladimir Simic https://creativecommons.org/licenses/by/4.0
Supplier Selection in the Age of Industry 4.0: A Review on MCDM Applications and Trends
The advent of Industry 4.0 has triggered a profound transformation in manufacturing and supply chain management, necessitating the adaptation of supplier selection processes to this evolving technological landscape. This comprehensive review paper places a significant focus on the role of multi-criteria decision-making (MCDM) methodologies and emerging trends in the context of supplier selection within Industry 4.0. It underscores the critical importance of effective supplier selection in achieving operational excellence and bolstering supply chain resilience in this new era, exploring the challenges and opportunities presented by technological advances such as the Internet of Things, big data analytics, artificial intelligence, and advanced manufacturing techniques. The paper looks into the theoretical underpinnings of MCDM techniques, highlighting their suitability for addressing the complex, multi-dimensional criteria associated with supplier selection. As a valuable resource, this study caters to researchers, practitioners, and decision-makers navigating the intricate landscape of supplier selection within the dynamic area of Industry 4.0, emphasizing the pivotal role of MCDM methodologies and the ever-evolving dynamics shaping supplier selection processes in the digital age.
Copyright (c) 2024 Sushil Kumar Sahoo, Shankha Shubhra Goswami, Rohit Halder https://creativecommons.org/licenses/by/4.0
From Ambiguity to Clarity: Unraveling the Power of Similarity Measures in Multi-Polar Interval-Valued Intuitionistic Fuzzy Soft Sets
The utilization of similarity measures in the context of multi-polar interval-valued intuitionistic fuzzy soft sets (mPIVIFSS) is a significant theoretical approach that offers a valuable structure for tackling intricate situations that are characterized by imprecise information and uncertainty. Nevertheless, these issues frequently entail conditions that are not limited in scope and exhibit a considerable level of uncertainty in scenarios involving multiple dimensions, hence presenting substantial difficulties. The objective of this research study is to provide a comprehensive understanding of similarity measures in the context of mPIVIFSS. Additionally, we will examine several operations, such as complement, subset, union, intersection, AND, OR, and De-Morgan’s Laws, as they relate to mPIVIFSS. This study not only includes theoretical debates but also explores practical applications. These efforts contribute to the improvement of decision-making, pattern recognition, and data analysis in settings characterized by ambiguous and uncertain information, hence emphasizing the importance of similarity measures in efficiently addressing multi-dimensional problems.
Copyright (c) 2024 Muhammad Saqlain, Muhammad Saeed https://creativecommons.org/licenses/by/4.0
Sustainable Delivery Model Selection using AROMAN Approach
The development of electronic commerce has resulted in an increase in the number of shipments in their transfer systems. Delivery companies strive to respond to emerging situations, meet customer needs, and achieve profit through the optimization of their operations. The imperative is to establish a delivery system that is in line with the principles of sustainable development. This paper defines the task of choosing a sustainable model for shipment delivery and proposes the AROMAN method for its resolution. The applicability of the proposed method is demonstrated by solving the defined task in the territory of the city of Belgrade.
Copyright (c) 2024 Momčilo Dobrodolac, Sara Bošković, Stefan Jovčić, Dragan Lazarević https://creativecommons.org/licenses/by/4.0
Marine Object Detection using YOLOv4 Adapted Convolutional Neural Network
This research presents an innovative application of the YOLOv4 object detection model for the identification and classification of marine objects within a dataset encompassing seven distinct classes. The study focuses on enhancing the robustness and accuracy of object detection in challenging marine environments, leveraging the unique capabilities of YOLOv4. Pre-processing steps involve resizing raw images, applying data augmentations, and normalizing pixel values to ensure optimal model training. Specifically tailored for underwater scenarios, additional color space transformations address variations in lighting conditions. The model is trained to detect marine objects such as fish, corals, and underwater structures, contributing to advancements in underwater exploration, environmental monitoring, and marine resource management. Experimental results demonstrate the effectiveness of the proposed approach, showcasing YOLOv4's ability to accurately identify and classify marine objects across the specified seven classes. This research not only expands the applicability of YOLOv4 in the marine domain but also provides valuable insights for the development of intelligent systems for underwater object detection.
Copyright (c) 2024 Muhammad Daniyal Baig, Hafiz Burhan Ul Haq https://creativecommons.org/licenses/by/4.0