Journal of Resource Management and Decision Engineering

A Mathematical Integrated Model for Shop Scheduling Routing Considering Reliability and Maintenance under Uncertainty Conditions

Mohammad Rajabpour iD , Masoomeh Zeinalnezhad ^* iD , Vahid Hajipour iD

This study presents an integrated mathematical model for shop routing and scheduling while considering machine reliability and maintenance under uncertainty conditions. The primary objective of the proposed model is to optimize production time and system costs through effective machine management, activity scheduling, and preventive maintenance planning. To achieve this objective, the mathematical model incorporates a set of decision variables and parameters, including machine-related costs, energy consumption costs, and maintenance and repair costs. Furthermore, the model includes constraints related to activity continuity, precise activity scheduling, compliance with time restrictions, costs associated with machine utilization, and machine maintenance levels. The proposed model is particularly applicable to manufacturing environments that require efficient management of resources and machinery and can assist production managers in reducing operational costs and production lead times. The model can be solved using numerical optimization algorithms, and its results can support optimal decision-making regarding scheduling, routing, and machine maintenance in manufacturing systems.

The Ecosystem Model of the Country’s Prisons with an Emphasis on Prisoner Behavioral Reform (Case Study: Tehran Prisons Organization)

Pedram Mohammadzadeh iD , Seyed Mehdi Alvani ^* iD , Seyed Ahmad Hosseini Golafshani iD , Afsaneh Zamani Moghadam iD , Seyed Abdullah Sajjadi Jaghargh iD

Prisoners, as a vulnerable group within society, face numerous challenges that not only affect their quality of life but also influence their successful reintegration into society. Prison conditions are recognized as a determining factor in the process of prisoner rehabilitation and reformation. In this regard, various factors, including hygiene and sanitation, security, the social environment, and educational and support programs, can play a significant role in reducing tensions and improving prisoners’ quality of life. This study seeks to examine the environmental and social conditions of prisons and identify the impact of rehabilitative and educational programs on prisoners’ capacity for successful reintegration into society. Furthermore, it investigates the economic and social support mechanisms that may facilitate this process. This research was conducted using a qualitative approach and employed in-depth interview content analysis methods. The study population consisted of inmates from several selected prisons, as well as staff members and specialists working in the field of correction and rehabilitation. Data were collected through semi-structured interviews using purposive sampling to ensure a comprehensive examination of diverse perspectives. Data analysis was performed using thematic analysis to identify key patterns and concepts. Through ongoing data collection and continuous analysis, theoretical saturation was ultimately achieved, such that no new data contributed substantial additional information to the findings. This process enabled the generation of more precise results regarding the effects of prison conditions and rehabilitative programs on prisoner rehabilitation.

Development of a Model of Factors Affecting Startup Success Using Directed Content Analysis

Fatemeh Aminsaffar iD , Gholamreza Tavakoli ^* iD , Abolfazl Bagheri iD , Manoochehr Manteghi iD

Startup success is a multidimensional phenomenon, and explaining it solely through a single factor such as an innovative idea, venture financing, or market growth is insufficient. This study aims to identify the factors affecting startup success and to develop its conceptual model based on case-based evidence. The research is applied in terms of purpose and qualitative in terms of approach. In the first stage, relevant studies on startup success were reviewed using Sandelowski and Barroso’s seven-step meta-synthesis method, and the main factors were extracted. Then, to develop the model, three startups with different conditions, including a successful and developing startup, a growing startup, and an unsuccessful and discontinued startup, were examined as case studies. The meta-synthesis findings indicated that the factors affecting startup success can be classified into five main categories: leadership and team, product or service, market and customer, business model and finance, and external environment. The case study results showed that sustainable startup success depends not only on the existence of these factors but also on their alignment and executability. Accordingly, components such as strategic focus, simplicity of value proposition, specialized trust-building, continuous customer support, revenue model executability, stakeholder alignment, and ecosystem leverage were added to the initial model. The developed model can be used to assess startups’ growth status, sustainability, and investment readiness.

Designing a Crowdfunding Investment Marketing Model for Enhancing Sustainable Urban Revenues: A Case Study of Tehran Municipality

Abolghasem Yousef Soltanieh iD , Mahmoud Ahmadi Sharif ^* iD , Arezo Ahmadi Danyali iD , Nader Gharibnavaz iD

https://doi.org/10.61838/kman.jrmde.372

This study aimed to design and validate a crowdfunding investment marketing model for enhancing sustainable urban revenues in Tehran Municipality, with emphasis on the role of innovation drivers. This applied study was conducted using a mixed-methods exploratory–confirmatory design. In the qualitative phase, the Delphi technique was used to identify and validate the main dimensions and indicators of the model based on the opinions of 15 experts in urban management, municipal finance, innovation, investment, and crowdfunding. In the quantitative phase, a structured questionnaire was administered to 250 managers and senior experts of Tehran Municipality who were involved in financial, investment, planning, innovation, and public participation domains. Data were analyzed using exploratory factor analysis, confirmatory factor analysis, reliability testing, and structural equation modeling through SPSS and SmartPLS/AMOS. Inferential findings confirmed the adequacy of the measurement model. The KMO values for innovation in urban management, crowdfunding investment marketing, and sustainable urban revenue were 0.874, 0.884, and 0.893, respectively, and Bartlett’s tests were significant at p < 0.001. The extracted factors explained 57.45% of the variance in innovation, 68.30% of the variance in crowdfunding investment marketing, and 72.63% of the variance in sustainable urban revenue. Reliability was also confirmed, with Cronbach’s alpha coefficients of 0.907, 0.931, and 0.923, respectively. Structural equation modeling showed that innovation drivers had a positive and significant effect on crowdfunding investment marketing (β = 0.78, t = 8.45). Crowdfunding investment marketing significantly affected sustainable urban revenue (β = 0.65, t = 6.12). The direct effect of innovation drivers on sustainable urban revenue was also significant (β = 0.32, t = 3.11), and the indirect effect through crowdfunding investment marketing was confirmed (β = 0.51, t = 5.88). The results indicate that innovation drivers provide the institutional, technological, and organizational foundation for effective crowdfunding investment marketing, while crowdfunding marketing functions as a strategic mechanism for converting citizen trust and participation into sustainable municipal revenues.

An Integrated Expert System for Prioritizing Concrete Railway Bridges Maintenance: A Hybrid Fuzzy Delphi - Fuzzy TOPSIS - MLP Framework

Hossein Farshad iD , Nader Abdoli Yazdi ^* iD , Ehsan Eshtehardian iD

Maintenance prioritization of railway bridges is a critical challenge due to limited financial resources and the direct impact of bridge failures on transportation network safety. Traditional approaches relying on individual expert judgment are inadequate for managing the complexity of maintaining precast concrete railway bridges, particularly in environmentally aggressive corridors. This study presents an integrated three-stage expert system (hybrid decision-support framework) for maintenance prioritization of 85 precast box-girder concrete bridges along the BonekuhGorgan railway corridor (Northern Iran). The proposed framework combines: (1) Fuzzy Delphi for extracting, screening, and weighting maintenance criteria through the judgment of 15 domain experts; (2) Fuzzy TOPSIS for quantitative prioritization and computation of the Intervention Priority Index (Cl+) across three inspection periods (2020, 2022, and 2024); and (3) Multilayer Perceptron (MLP) neural network for predicting the future trend of the Cl+ index and supporting preventive maintenance planning. In the Fuzzy Delphi stage, 12 key criteria were identified from 20 initial candidates; expansion joints (C5) and guardrails (C12) received the highest weights (w = 0.096). Kendall's concordance coefficient (W = 0.69) and Cronbach's alpha (α = 0.847) confirmed satisfactory consensus and reliability among experts. Fuzzy TOPSIS results showed that bridge B1, with Cl+ = 0.826, had the most critical condition requiring immediate intervention, while bridge B48, with Cl+ = 0.214, exhibited the lowest deterioration level. The MLP model with a 13-64-1 architecture and Walk-Forward Validation predicted the Cl+ index with R² = 0.931 and RMSE = 0.0214, demonstrating high generalizability. The results indicate that the proposed framework can serve as a practical Decision Support System (DSS) for railway bridge maintenance management, supporting optimal allocation of limited resources with scientific precision and transparency.

Predicting Costs and Risks in Construction Projects Using BIM and Artificial Intelligence Data Analytics

Mohammadmoein Zohreh ^* iD

This study aimed to predict cost overrun and project risk in construction projects using BIM-based indicators and artificial intelligence data analytics. This applied, quantitative, predictive, and analytical study was conducted on 64 construction projects in Tehran, Iran, with the participation of 148 construction professionals, including project managers, BIM specialists, cost and quantity surveying experts, planning and project control engineers, and site engineers. Data were collected using BIM-based project documentation, cost and schedule records, risk assessment forms, and expert validation checklists. The main variables included BIM maturity level, number of detected BIM clashes, design revision frequency, schedule deviation, material cost fluctuation, procurement delay, change orders, rework cost ratio, cost overrun percentage, and composite project risk score. Data were analyzed using correlation analysis and artificial intelligence-based predictive models, including multiple linear regression, decision tree, support vector regression, artificial neural network, random forest, and gradient boosting models. Inferential findings showed that cost overrun had significant positive correlations with schedule deviation, design revisions, BIM clashes, material cost fluctuation, and composite project risk score, while BIM maturity had significant negative correlations with cost overrun, schedule deviation, BIM clashes, and risk score. Gradient boosting regression produced the strongest cost prediction performance, with the lowest mean absolute error and root mean square error and the highest coefficient of determination, explaining 84% of the variance in cost overrun. For project risk classification, gradient boosting also achieved the highest performance, with accuracy of 0.89 and area under the curve of 0.93. Feature importance analysis identified schedule deviation, design revision frequency, BIM clashes, material cost fluctuation, and BIM maturity as the strongest predictors of cost and risk outcomes. The findings indicate that integrating BIM-based project information with artificial intelligence analytics provides a reliable predictive framework for early identification of cost overrun and project risk in construction projects. AI-based models, especially gradient boosting, can support proactive decision-making, improve cost control, and strengthen risk management throughout the project life cycle.

Determining the Dimensions, Components, and Indicators of Transformational Digital Leadership in SAIPA Automotive Group

Hamzeh Esmaili iD , Mohammad Reza Rabiee Mandejin ^* iD , Vahid Araei iD , Mohammadreza Saeedi iD

The purpose of this study was to identify and develop the dimensions, components, and indicators of transformational digital leadership in SAIPA Automotive Group. This study employed a qualitative developmental research design grounded in the interpretive paradigm and guided by an inductive approach. Data were collected through semi-structured interviews with 17 experts, senior and middle managers, policymakers, consultants, and specialists with substantial experience in digital transformation, leadership, information technology, innovation, and automotive industry management. Participants were selected using purposive criterion-based sampling, and interviews continued until theoretical saturation was achieved. The collected data were transcribed and analyzed using Braun and Clarke’s six-phase thematic analysis method with the support of MAXQDA software. Trustworthiness was enhanced through repeated data review, continuous comparison of codes and themes, expert participation, and systematic documentation of the analytical process. The analysis resulted in the extraction of 570 initial concepts, which were reduced to 354 basic codes after removing overlaps and redundancies. These codes were subsequently organized into 26 subthemes and 7 overarching themes. The final model of transformational digital leadership consisted of: (1) Digital Transformation Leadership and Strategy, including transformational leadership, digital vision, strategic alignment, and dynamic capabilities; (2) Data Governance and Digital Technologies, encompassing data governance, analytics, artificial intelligence, and digital infrastructure; (3) Digital Transformation in Production and Operations, including smart manufacturing, digital operations, supply chain digitalization, and digital product management; (4) Digital Innovation and Technology Ecosystem, comprising innovation capability, knowledge management, ecosystem collaboration, and digital R&D; (5) Digital Market and Customer Value Creation, including digital customer experience, digital marketing, connected services, and customer analytics; (6) Human Capital and Digital Culture, involving digital culture, talent development, employee empowerment, and change management; and (7) Security, Risk, and Macro Digital Governance, including cybersecurity, digital governance, risk management, and digital sustainability. The findings demonstrate that transformational digital leadership in the automotive industry is a multidimensional and integrative phenomenon extending beyond technological implementation.

Meta-Synthesis of Factors Influencing Consumer Behavior in the Strategic Positioning of the Country’s Vegetable Oil Industry

Hamid Reza Izadi ^* iD , Farshid Namamian iD

The aim of this study was to metasynthesis of factors affecting consumer behavior in the strategic positioning of the country's vegetable oil industries. In the present study, the statistical population was articles and documents related to the research area in the period from 2005 to 2018 and from 1385 to 1397 solar. The present study is qualitative in terms of its applied purpose, in terms of the nature of the data and the method of analysis, and in terms of the method of data collection, it is based on documentary information. The research method is descriptive analytical and the open coding method was used to analyze the data. All documents and documents published in the conceptual field of the research in reputable domestic and foreign databases were defined based on keywords and were reviewed in order to achieve a sample that leads to theoretical saturation.In the final research model, two main components of consumer behavior and positioning were identified and quantified. Each of these main components had sub-categories and content codes. Finally, the results were discussed and reviewed, and suggestions were provided to the authorities

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The Mediating Role of Job Autonomy and Organizational Identity in the Relationship Between Transformational Leadership and Work Engagement (Case Study: Employees of Kurdistan Cement Factory)

Identifying and Prioritizing the Role of Artificial Intelligence in Predictive Maintenance and Repairs in the Automotive Industry

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