International Journal of Research in Industrial Engineering is an international scholarly open access, peer-reviewed, interdisciplinary, and fully refereed journal published quarterly. This journal provides a domain for professionals, academicians, researchers, policymakers, and practitioners working in the fields of applied mathematics, industrial engineering, reliability engineering, TQM, management, and globalization to present their research findings, propose new methodologies, disseminate the latest findings, and to learn from each other’s research directions. IJRIE focuses on applied research in the areas of industrial engineering with a bearing on inter/intradisciplinary research. This journal publishes full-length research manuscripts, insightful research, and practice notes, as well as case studies. This journal respects the ethical rules in publishing and is subject to the rules of the Ethics Committee for Publication (COPE) and is committed to following the executive regulations of the Law on Prevention and Combating Fraud in Scientific Works.
ISO abbreviation: Int. J. Res. Ind. Eng. Frequency: Quarterly Language: English Review process: Double-blind Plagiarism screening: iThenticate Open access: Yes Article processing Charge: No Copyright: CC-BY 4.0 Acceptance rate: 43% Average time to first decision: 8 Days Review time (Approximately): 82 Days
Abstract Food waste in Vietnam’s food service sector poses significant economic, environmental, and social challenges. This study uses an integrated grey Delphi and Decision-Making Trial and Evaluation Laboratory (DEMATEL) approach to identify critical factors contributing to food waste. An initial list of factors was identified from existing literature and further refined through expert evaluation using the Grey Delphi approach. Sixteen experts were selected for the panel based on their background and professional experience. Finally, 34 valid factors were identified as valid and arranged into six major groups: Purchasing, receiving, inventory control, preparing, selling, and general drivers. These factors were then analyzed using grey DEMATEL to map causal relationships and influence strengths. The findings highlight improper inventory management, inaccurate demand forecasting, and limited customer awareness as key drivers requiring managerial focus. This research introduces a novel application of grey theory to address complex, data-scarce problems, offering a robust framework for analyzing food waste. By delineating the origins and pathways of waste propagation, the study provides actionable insights for managers to enhance sustainability and reduce waste in Vietnam’s food service industry.
Abstract Modern digital teaching systems focus on users' core needs, and gamification design has become a key approach to optimizing online education. However, traditional design has problems such as decision-making ambiguity and resource mismatch. This study constructs a hybrid HMSAM-MCDM model that combines Structural Equation Modeling (SEM) and the Technique for the Order of Preference by Similarity to Ideal Solution (TOPSIS) to optimize user engagement in gamified online education systems. Through the analysis of questionnaire survey data, SEM verified the intrinsic correlation of seven core variables, with the positive impact coefficients of 0.54, 0.59, and 0.47 on curiosity, happiness, and control, respectively; The positive impact coefficients of curiosity and happiness on gamified learning willingness are 0.42 and 0.49, respectively, and the positive impact coefficients those on immersion are 0.51 and 0.56, respectively; the positive impact coefficient of control on immersion is 0.43. The TOPSIS priority ranking results show that the weights and closeness of the core factors affecting user participation willingness are: Perceived Usefulness (PU) > happiness (JOY) > curiosity (CUR) > control (CTL) > immersion (FI) > Perceived Ease Of Use (PEOU). Research has shown that this hybrid model provides an accurate quantitative basis for decision-making in system optimization through the innovative integration of "SEM variable relationship recognition and TOPSIS quantitative ranking", significantly improving the scientific and operational feasibility of participation optimization in gamified education systems.
Abstract This study explores how occupational stress affects employee performance in the Bangladeshi textile industry. It evaluates the effectiveness of the Plan-Do-Check-Act (PDCA) cycle as a workplace improvement strategy. A descriptive research methodology was employed, combining quantitative and qualitative methods. Data were collected through convenience sampling using structured questionnaires to assess stress factors and their influence on job performance. Factor and regression analyses were conducted using the Statistical Package for the Social Sciences (SPSS) V.25. The analyses revealed key stressors, including job role disappointment, frustration, poor working relationships, concentration issues, and unfavorable physical environments.Additionally, performance was significantly affected by work pressure, resource constraints, and job insecurity. Following implementation of the PDCA cycle, participants reported noticeable reductions in stress levels and performance-related challenges, indicating PDCA's potential as a systematic approach to improving workplace conditions and employee well-being.These outcomes underscore the importance of structured stress management practices within the textile sector and suggest that the PDCA framework can be a practical tool for fostering continuous organizational improvement. By addressing workplace stress through continuous development, this study provides a pathway to enhance worker well-being and organizational outcomes effectively.
Abstract This study presents a comparative numerical analysis of four chaotic map-based strategies for solving Optimal Control Problems (OCPs). The proposed framework involves a two-phase process: first, OCPs are discretized and reformulated as nonlinear optimization problems; subsequently, optimization is performed using the Logistic Chaotic Reduction (LCR), Improved Logistic Chaotic Reduction (ILCR), Logistic Chaotic Reduction with Gradient Search, and Chaotic Gradient-based Optimization (CGO) algorithms. To evaluate performance, five benchmark problems are examined. Among the methods tested, CGO demonstrates superior convergence speed and solution precision. Numerical results show that integrating chaotic dynamics with gradient-based search effectively balances exploration and exploitation, mitigates premature convergence, and improves numerical accuracy. A comparative analysis with conventional techniques across multiple test cases further confirms the computational efficiency and robustness of CGO. These findings position chaotic optimization as a viable and competitive alternative to traditional gradient-based and heuristic approaches in solving OCPs.
Abstract Aircraft Maintenance, Repair, and Overhaul (MRO) plays a crucial role in ensuring aviation safety, operational efficiency, and cost-effectiveness. Airlines must strategically decide between in-house and outsourced MRO services, each with distinct implications for cost structures, Turnaround Time (TAT), and regulatory compliance. This study presents a comparative analysis of in-house and outsourced MRO models, focusing on cost efficiency, workforce utilization, risk mitigation, and the application of predictive analytics in capacity planning. A mixed-methods research design was employed, integrating quantitative financial assessments, performance metrics, and qualitative insights from industry professionals. Findings indicate that while in-house MROs have lower labor costs and enhanced workforce availability, outsourced MROs demonstrate higher efficiency in TAT and scalability, benefiting from advanced predictive analytics and risk mitigation strategies. Outsourced MROs reduce TAT by 18% and achieve cost savings of up to 20% compared to in-house operations, primarily due to economies of scale and optimized workflow processes. However, in-house MROs offer greater control over compliance and workforce allocation, ensuring operational stability tailored to the airline's specific requirements. Predictive analytics further enhances maintenance planning, with outsourced MROs leveraging AI-driven insights to improve maintenance scheduling and achieve 18% cost reductions. This study has significant industry implications, offering strategic recommendations for airlines and MRO providers to optimize cost structures, workforce deployment, and regulatory alignment. Future research should explore integrating emerging technologies, such as AI and blockchain, to enhance decision-making in MRO operations.
Abstract This study introduces a novel Interval-Valued Plithogenic Cognitive Map (IV-PCM) model for multi-criteria decision-making, designed to handle uncertainty, conflict, and indeterminacy in complex systems. This model handles uncertainty and complexity in several decision-making scenarios. The interval-valued representations are discussed in different instances of fuzzy, intuitionistic, and neutrosophic logics with the plithogenic framework. We apply the model to a sustainable supply chain decision-making problem, demonstrating its ability to capture complex interrelations among sustainability factors. This model is applied in the sustainable supply chain decision-making problem to bring out the sustainable factors. The study gives a comparative analysis with the single-valued Plithogenic cognitive models, resulting in greater performances.
Abstract Universities that run international education programs must compete with foreign institutions while meeting the diverse requirements of multiple stakeholders. This paper presents an adaptable management system that aims to improve internationalization outcomes in higher education institutions. The research draws from globalization theory, higher education internationalization theory, and multi-level governance to develop its arguments about globalization, internationalization, and diversified management practices. The proposed framework consists of three interconnected components that enable institutions to assess stakeholder requirements while aligning global goals with domestic needs. The findings show that universities adopting flexible, stakeholder-centered organizational systems build stronger international relationships and achieve superior long-term development. The study also provides operational methods and step-by-step guidelines that help university administrators and policymakers design internationalization plans that meet global standards while serving national interests.
Madineh Farnam, Hadi Basirzadeh, Roohollah Abbasi Shureshjani, Gholam Hassan Shirdel
Abstract Ranking alternatives in Multi-Criteria Decision-Making (MCDM) often involves uncertainty that classical approaches cannot adequately manage. Triangular Interval Type-2 Fuzzy (TriIT2F) numbers provide richer representations of vagueness, but existing ranking techniques remain constrained. Wang’s method applies only to limited cases, Maji’s approach requires identical cores, and Gong, as well as Javanmard and Nehi, suffer from weak sensitivity and limited discriminatory capability. To overcome these shortcomings, this study introduces a generalizable and weighted ranking framework for TriIT2F numbers. By enabling adjustable weights between lower and Upper Membership Functions (UMFs), the method captures various decision-maker attitudes while preserving computational simplicity. Its linear structure further allows seamless integration with the Simple Additive Weighting (SAW) method.Comparative experiments and sensitivity analyses confirmed the method’s ability to deliver more precise and stable rankings than existing approaches. A case study on cricket player selection validates its practical utility, demonstrating superiority in discrimination, generalizability, and interpretability. Overall, the proposed framework fills a clear theoretical gap and offers strong potential for broader optimization and MCDM applications.
Abstract Building upon the foundational work of Linguistic Intuitionistic Fuzzy Sets (LIFS), this paper presents an advanced and novel neural-based framework called the Linguistic Intuitionistic Fuzzy Artificial Neural Network (LIF-ANN) for solving Multi-Attribute Group Decision-Making (MAGDM) problems. The proposed model integrates a Perceptron-driven Artificial Neural Network (ANN) with linguistic intuitionistic fuzzy inputs to address uncertainty and hesitation in decision environments. Unlike earlier approaches that focused solely on fuzzy aggregation and normalization, this method embeds linguistic fuzzy representations within a trainable ANN structure. The network learns and adjusts decision weights dynamically, enhancing adaptability and ranking precision. The new LinFWAA (Linguistic Intuitionistic Fuzzy Weighted Arithmetic Aggregation) operator is proposed for aggregating the linguistic data enhancing the decision problem. Finally, the input created from solving the MAGDM problem is also solved using the ANN model with Perceptron learning rule exclusively proposed for Linguistic Intuitionistic Fuzzy sets. Experimental results demonstrate the model’s effectiveness in delivering interpretable and scalable solutions for complex, linguistically expressed decision-making problems.
Abstract The linear programming (LP) model's strong modeling capabilities make it a valuable tool for solving real-world problems and optimizing objectives. The classical MCF problem is one such application of the LP model. These problems typically assume fixed parameters, whereas real-world scenarios often involve uncertainty in demand, cost, supply, and capacity. To address such uncertainty, neutrosophic logic extends fuzzy logic, enabling more effective handling of ambiguity, inconsistency, and incompleteness in data. This article explores the MCF problem with capacitated arcs and addresses it using neutrosophic arc costs represented by SVTN numbers. Two algorithms are developed to provide methodologies for solving the neutrosophic MCF problem with uncertain cost parameters. Algorithm I introduce a strategy for obtaining the optimal solution by converting the NMCF problem into a classical MCF problem. Algorithm II transforms each cost parameter into a weighted value and applies the possibilistic mean to produce an equivalent classical MCF problem. To validate the effectiveness of the proposed approach, three different types of numerical examples are examined, each demonstrating the applicability and performance of the two algorithms. The proposed method not only addresses current challenges but also resolves issues that previous models have not effectively handled, with a comparative analysis provided in this article.
Abstract This study proposes a novel extension of the traditional rhotrix structure by incorporating Plithogenic set theory to support complex decision-making scenarios. The developed framework blends multi-trait Plithogenic logic with rhotrix configurations to model interrelated investment evaluations more effectively. A new heart-based multiplication operation tailored for Plithogenic fuzzy data is introduced, considering the contradiction grades among diverse trait values. The paper classifies various types of Plithogenic rhotrices and outlines a comprehensive algorithm for implementing the proposed decision-making method. A simulated case study involving companies and investment portfolios illustrates the application of the model using the Plithogenic accuracy function. Comparative results between Plithogenic fuzzy rhotrix and classical fuzzy rhotrix reveal that the proposed approach enhances interpretability and precision in multi-trait decision-making. Sensitivity analysis further confirms the robustness of this methodology in handling contradictory and fuzzy information.
Abstract Let G=(V, E) be a simple graph. A set D⊆ V(G) is called a dominating set if every vertex in V∖D is adjacent to at least one vertex in D. This study introduces the concepts of enclave dominating vertices and enclave dominating sets in graphs and defines a new domination parameter termed the enclave domination number. The investigation determines the exact number of minimum enclave dominating sets for several standard graphs, as well as for graphs constructed through combinatorial operations involving path and wheel-related structures. In addition, new characterizations are presented, and several fundamental properties of the enclave domination number are established, thereby contributing to the broader understanding of domination theory in graph structures. The enclave dominating vertex and the enclave dominating sets are formally introduced, and new characterizations and key properties of the enclave domination number are provided, highlighting its significance and potential applications within graph theory and related fields.
Abstract Financial markets are characterized by volatility and non-stationarity, posing challenges for effective investment strategies. Traditional approaches, such as trend-following and momentum strategies, have long been employed but often yield inconsistent results across stock markets and under varying market conditions. With the rise of machine learning (ML), data-driven methods offer new opportunities for enhancing trading decisions; however, their comparative effectiveness against traditional approaches remains underexplored.
This study presents a sector-specific analysis of trading strategies within the Consumer Discretionary, Healthcare, and Information Technology sectors of the S&P 500. Structured in two phases, Phase 1 evaluates the performance of traditional strategies—trend-following, momentum, and buy-and-hold—using risk-return metrics such as annualized return, volatility, and the Sharpe e ratio. Phase 2 applies ten ML models, including ensemble methods, to develop enhanced strategies based on technical indicators. Models are assessed on both predictive accuracy (accuracy, precision, recall, and F1-score) and financial performance through a 10-year backtest.
Findings suggest that ML-enhanced strategies can outperform traditional approaches, particularly in terms of risk-adjusted returns. The originality of this study lies in its sector-based framework, which not only compares conventional and ML-driven strategies but also evaluates them using a dual lens of predictive accuracy and risk–return metrics. Furthermore, for each sector with distinct characteristics, the study identifies the ML models that achieve the best balance of accuracy and financial performance.
Abstract Quality management in the garage service sector is a major issue in Ethiopia for both businessmen and customers. To improve the service quality management system and determine the best option, a model has been developed that integrates MCDM techniques with the service quality management model. The different service quality management models, including the Gummesson 4Q, have been considered in previous literature; however, their integration with the FTOPSIS methodology has not been studied. In filling this gap, in this research, firstly literature review regarding the service quality management models and MCDM tools, including FTOPSIS, has been conducted to depict the areas in which the models are applied. Second, among the models, due to its nature of providing high-quality service quality management opportunities, Gummesson’s 4Q model, with its dimensions, namely designed quality, product quality, delivery quality, and relational quality, was selected for this study as the comparison criteria. Finally, an FTOPSIS methodology was applied to prioritize garage service providers. According to the methodology, the results showed that A1, A5, and A4 are the best garage service providers with the closeness coefficients (CC) of 0.469, 0.466, and 0.458, respectively. As the main implication and contribution, the research procedures, framework, model, and obtained results using the developed methodology can help the sector investors adjust their internal resource capacity toward ascertaining the required service quality, and for end customers to perceive quality and a high level of satisfaction. Explicitly, the managerial implications of this study are defined as its importance for strategic decision making, enhancing customer satisfaction, guiding resource allocation, and providing quality improvement initiatives. In addition, it is also helpful for practitioners, academicians, researchers, and policy makers to determine the service quality through the designed management system to provide a possible solution within the sectors and similar sectors.
Abstract To address the enhancement challenges in low-light, non-uniform illumination, and underwater images,we propose an enhanced Retinex image processing method with good generalization capability. The research framework consists of the following key components: First, the raw RGB image is converted to YIQ color space to separate luminance and chrominance components. Subsequently, the luminance component is enhanced using a multi-scale Retinex algorithm based on gradient domain guided image filtering, while adaptive Non-Local Means filtering is introduced for noise suppression. For chrominance enhancement, a nonlinear gamma model is constructed to expand the display dynamic range. Finally, the processed YIQ image is reconstructed into RGB format. Experimental results on low-light, non-uniform illumination, and underwater image datasets demonstrate that the proposed method significantly outperforms traditional enhancement algorithms in terms of brightness adjustment, structure preservation, and detail clarity, providing an effective solution for image quality improvement in complex image enhancement domain.
Abstract Unmanned Aerial Vehicles (UAVs), also known as drones, pose recent challenges in emergency applications due to packet loss and dynamic changes in network topology. The increasing presence of UAVs in the airspace has given rise to flying ad hoc networks (FANETs), which can enhance ad hoc missions. However, developing a routing scheme for such a network becomes complex due to resource constraints and dynamic topology. A trust-based routing based on ant colony optimization (ACO) is proposed for FANETs to address these challenges. The hybrid nature of trust is computed to identify the non-cooperative drones/UAVs. Then the ACO-based scheme uses this trust score to establish the secure route through the leader’s drones/UAVs. The proposed scheme is developed to meet the needs of flexible and dynamic communications with such a network. This approach improves FANET performance by enhancing resilience, dependability, and extended route stability. It provides reliable route selection schemes through leaders based on ant colony optimization. The simulation results demonstrate significant improvements, with a 21% reduction in latency and a 17% decrease in routing overhead. Furthermore, the proposed scheme's packet delivery ratio (PDR) outperforms by more than 26%, compared to the SecRIP and UNION schemes.
Abstract The primary objective of this study is to explore numerical techniques for solving intuitionistic fuzzy wave equations, with a focus on applying this approach to bridge suspension cables or strings. Existing intuitionistic fuzzy finite difference methods suffer from the explicit nature of finite difference, which causes a conditionally stable solution. Additionally, most of the uncertainty in the literature is based on the classical fuzzy set. This study extends the explicit fuzzy finite difference scheme to the intuitionistic implicit fuzzy finite difference method for solving intuitionistic fuzzy wave equations. The proposed method uses the second-order finite difference method to approximate the wave equation in implicit form. The initial and boundary conditions of the wave equation are described using intuitionistic triangular fuzzy numbers. The error analysis, consistency, convergence, and stability of the scheme are rigorously analysed to ensure mathematical reliability. A representative problem from engineering applications, such as analysing a bridge cable under wind, has been solved. To demonstrate the method’s practicality, the numerical results are compared with the exact solution. By stability analysis, the method is found to be unconditionally stable. The findings confirm that the proposed approach approximates the solution with good accuracy, making it a significant contribution to the numerical methods for solving intuitionistic fuzzy wave equations in real-life application problems.
Abstract The scope of commercial credit circuits is closely linked to the field of data analysis within a network flow, making it essential for such companies to employ state-of-the-art modeling techniques, performance evaluation methods, and algorithmic strategies in order to effectively achieve their primary objectives across economic, social, and environmental domains. In a similar context, Mountex functions as an innovative economic network that supports businesses in discovering new opportunities and fostering regional collaboration. The general objective of this study is to identify potential areas for performance improvement in commercial credit circuits across various dimensions, with a particular focus on enhancing their efficiency and productivity—specifically through leveraging the capabilities of the Mountex platform. Guided by this goal, we begin by reviewing fundamental concepts from graph theory, followed by the presentation of a matrix-based model for the Mountex system over a specific time interval. This model offers significant flexibility and serves as a foundation for generating reliable predictions regarding the future state of the Mountex network. Subsequently, we outline the key challenges currently facing Mountex, particularly in relation to performance evaluation, incentive mechanisms, knowledge transfer, and sustainability assessment. Simultaneously, several potential solutions are proposed, each of which may serve as a direction for improving not only Mountex but also any commercial credit circuit operating under similar conditions.
Abstract This study examines how a leading firm can optimally select promotional partners in a market comprising three firms: two offering complementary products and one selling an independent product. Using a game-theoretic framework based on a Stackelberg model, in which the leading firm acts as the decision-maker, four promotional partnership scenarios are analyzed: no cooperation, cooperation with one complementary firm, cooperation with both complementary firms, and full collaboration between all three firms. Factors such as product complementarity, price elasticity, promotional effectiveness, and market share distribution can influence the firm's optimal promotional strategy. When individual promotions are effective and complementarity is strong, firms prefer alliances with complementary partners. Conversely, when complementarity leads to market cannibalization or when other firms possess larger market shares, the leading firm may favor partnering with the independent firm. Cooperation among all firms tends to be the most profitable when feasible. This research offers practical insights for managers seeking to enhance the impact of promotional alliances and contributes to the literature by extending previous models through a hierarchical Stackelberg framework, thereby extending prior cooperative advertising models to incorporate both complementary and independent products under asymmetric brand reputation. Furthermore, although the analysis is theoretical, it provides a conceptual basis for future empirical validation using real or simulated market data.
Abstract With the active nature of the financial markets, the fund managers have ceased to be in the conventional asset selection role and have taken up strategic decision making, as well as market sensing. The paper will examine how managerial analytical competence influences the performance of the funds, and how market perception and investment decisions mediate the effect of managerial analytical competence. Based on the dynamic capabilities, behavioral finance and information asymmetry theories, this study forms a structural model of the relationship between fund managers analytical capabilities based on their education, experience and historical performance and fund performance, in terms of absolute and risk-adjusted returns. Primary data were gathered through a structured questionnaire that was given to 320 fund managers who were members of the China Securities Investment Fund Association and secondary data on fund performance indicators between 2020 and 2024. The results obtained with the help of structural equation modeling (SEM) indicate that the analytical competence of managers is a significant level of managerial performance improvement either directly or indirectly and in terms of a better market perception and more rational investment decisions. Process perception appears to be a partial mediator between investment decisions and analytical competence as well as a moderator of the effect of analytical capability on decisions. This paper provides empirical data concerning behavioral and cognitive processes that exist between fund manager skills and fund returns. This finding indicates that industrial organizations and regulatory bodies need to focus more on the analytical competencies and market consciousness of fund managers. The research also helps in developing future literature on fund governance by incorporating managerial cognition in performance evaluations structure.
Abstract This study addresses fundamental challenges in cloud-based remanufacturing systems, including uncertainties in the quality of returned components, geographical dispersion of suppliers, and the need for simultaneous optimization of multiple, often conflicting, performance criteria such as time, cost, and energy. These challenges undermine the efficiency of closed-loop production processes and increase decision-making complexity in real-world manufacturing settings. To address these issues, this study develops an innovative framework based on a robust possibilistic programming model, which integrates fuzzy logic, possibility theory, and robust optimization to manage conflicting objectives under severe uncertainty conditions effectively. The proposed model simultaneously optimizes total remanufacturing time (considering uncertain processing durations), operational costs (including service and logistics expenses), and energy consumption (accounting for variations in equipment performance). Uncertainty is modeled through possibilistic programming and converted into a deterministic equivalent using the necessity measure. Four key parameters are introduced to control the robustness level and confidence degree. The α and θ parameters are used to adjust the fuzzy confidence level and necessity-based constraints, while the γ and δ parameters help tune the model's conservativeness in response to severe uncertainties in the objectives. Empirical results show that the robust model reduces total time by over 50% and cost by around 4% compared to the deterministic model, while providing significantly more stable and reliable solutions under uncertainty.
Yi Zhang, Mohamad Zuber Abd Majid, Ahmad Zamri Bin Mansor
Abstract Purpose: This paper examines the impact of financial knowledge, parental influence and exposure to financial education on financial behavior of university students and the role of financial self-efficacy (FSE) in mediating the impact. Recent causal and meta-analytic findings indicate that properly-designed financial education programs can enhance knowledge and downstream behavior, but the mechanism by which the process works, particularly the role of FSE in transforming inputs into action, has not been specified in terms of student populations. Socialization in the family setting and new randomized evidence found in school-based curricula are the basis of a unified model tested with FSE as intermediate. Methodology: A cross-sectional survey of N students was carried out at . Financial knowledge, parental influence, exposure to education and FSE, and financial behavior were measured with a set of items that were validated. The quality of measurement was determined through confirmatory factor analysis ( α, CR, AVE, HTMT ). Direct and indirect mediation were estimated using structural equation modelling; mediation was tested using bias-corrected bootstrapping (5,000 resamples). Adequacy of the model was evaluated using 3 metrics which are 2 /df, cfi, RMSEA and SRMR. (Complete items and eligibility criteria are found in Methodology). Findings: Direct impacts on financial behavior were positive and significant in the case of financial knowledge, parental influence and exposure to education. FSE had a positive relationship on behavior and mediated the effects of each antecedent. The trend aligns with: (a) meta-analytic data that financial education enhances behaviors; (b) family-socialization studies that adhered to the observed parental behaviors and financial behavior in early adulthood; and (c) behavioral results identifying efficacy/confidence as a proximal driver of favorable financial behavior. Research and Practical implications: Findings predate FSE as a process that links instruction, knowledge and family socialization with behavior of students.
Abstract Strategically locating healthcare facilities is crucial for ensuring accessible and efficient service delivery, particularly during emergencies and disasters. Many healthcare systems employ a hub-and-spoke model; however, disruptions at hub facilities can significantly undermine their effectiveness. This paper addresses the challenge of optimizing hub locations in the face of such disruptions. We propose a novel mixed-integer, bi-level optimization model rooted in game theory to determine the optimal placement of hubs and the assignment of spokes while minimizing transportation costs and maximizing demand coverage. To address the complexity of this multi-objective problem, we developed two metaheuristic algorithms designed to generate Pareto-optimal solutions. Computational experiments reveal that these algorithms deliver high-quality solutions efficiently, offering practical tools for decision-makers to improve the resilience and efficiency of healthcare systems under both normal and disrupted conditions.
Peter Kibagendi Mokaya, James Wakiru, Josephat Tanui
Abstract Warm air infiltration is a hidden phenomenon that can go unnoticed for weeks, months, or even years in cold storage envelopes. It is a common source of energy wastage that can be explored to achieve significant energy and cost savings. This research assesses warm air infiltration into fruit cold storages using a case study approach for an international fruit exporter based in Kenya, covering baseline study, root cause analysis, and developing a risk-based mitigation strategy to minimize the infiltration rates. Thermal graphic measurements, electricity bills, and on-site observation of the operation patterns provided source data. An Ishikawa diagram and a risk-based Failure Mode and Effect Analysis (FMEA) were used to identify and prioritize root causes, respectively, and a modified decision tree was then utilized to structure the mitigation strategy. The study established a lack of awareness of cold storage operations, irregular and untimed maintenance of components, broken door seals, and inconsistency in the frequency of cold storage door openings as the critical root causes for the warm air-infiltration challenge. It was further revealed that cold storage facilities need to take advantage of the available sensory and operational data to introduce maintenance management systems, temperature-airflow monitoring systems, and environmental control devices to complement the functionality of cold storage components. To operate fruit cold storages optimally and efficiently, facilities management must comprehensively understand the sources of temperature variations and adopt mitigation strategies that minimize warm air infiltration. There is no one-size-fits-all approach to reducing warm air infiltration; thus, both systemic and behavioral approaches must be adopted and integrated into cold storage operations.
Abstract The Quadratic Assignment Problem (QAP) is one of the problems of combinatorial optimization belonging to the NP-hard problems’ class and has a wide application in the placement of facilities. Thus far, many efforts have been made to solve this problem and countless algorithms have been developed to achieve the optimal solutions; one of which is the Simulated Annealing (SA) algorithm. This paper aims at finding a suitable layout for the facilities of an industrial workshop by using a Developed Simulated Annealing (DSA) method.
Abstract Nowadays, the recommendation system has made finding the things easy that we need. Movie recommendation systems aim at helping movie enthusiasts by suggesting what movie to watch without having to go through the long process of choosing from a large set of movies which go up to thousands and millions that is time consuming and confusing. In this article, our aim is to reduce the human effort by suggesting movies based on the user’s interests. To handle such problems, we introduced a model combining both content-based and collaborative approach. It will give progressively explicit outcomes compared to different systems that are based on content-based approach. Content-based recommendation systems are constrained to people, these systems don’t prescribe things out of the box, thus limiting your choice to explore more. Hence, we have focused on a system that resolves these issues.
Doulotuzzaman Xames, Fowzia Tasnim, Tamanna Islam Mim, Asduzzaman Kiron
Abstract The COVID-19 pandemic has pushed the world into chaos as it has never seen before. Bangladesh, a country of developing economy, has experienced severe disruptions in almost all sectors. Its food supply chain has become one of the most vulnerable sectors when exposed to those stoppages and uncertainties caused by the COVID-19 pandemic. The country’s food supply chains networks have undergone unforeseen circumstances. In this research paper, we have conducted rigorous search to gather evidence of food supply chain disruptions in Bangladesh from the articles published in journal articles, conference papers, and reliable local and international newspapers. We have summarized the plausible impacts of those disruptions on the food supply chain. Later, we suggest some potential strategies to mitigate those effects. Strategies such as contactless delivery, e-commerce adoption, robust collaborative demand forecasting, and decentralization of food manufacture and production, and efficient information sharing, could effectively mitigate the supply chain disruptions caused by the pandemic. This paper can provide essential insights and managerial guidelines for Bangladesh as well as other developing countries to tackle food supply chain disruptions under the COVID-19 epidemic scenario.
Abstract Doctor scheduling is a complex, costly and time-consuming exercise. This study develops a constraint satisfaction and penalty minimisation scheduling model for meeting ‘hard constraints’ and minimises the cost of violating ‘soft constraints’, i.e. the user inputs, the total number of doctors to be scheduled, the maximum penalty to be met, and the minimum number of doctors to be assigned per shift. The algorithm creates a schedule which checks against all the constraints. The total schedule penalty associated with the constraint violations should be less than or equal to the user input penalty. If this condition is met, the schedule gets produced as the final and near-optimal solution. The model is managed to create a near optimal schedule with the minimal rule violations. However, it is challenging to provide a schedule with no rule violations. Such a situation is shown by the amount of computational time required to create a zero-penalty schedule, hours or even days needed to create a zero-penalty schedule. The system creates a schedule for a short period (weekly schedule) to promote flexibility; however, such a system does not promote fairness. Fairness is achieved through a cyclic schedule with rotations equal to the total number of doctors being scheduled. The system is managed to create a streamlined and flexible working environment and helped to improve the quality of healthcare. An optimization protocol can be incorporated into the system to reduce the search space and get the best optimal schedule since it is possible to get many schedules under the same user-defined parameters.
