https://jpom.ui.ac.ir/ Research in Production and Operations Management en daily 1 Sun, 28 Dec 2025 00:00:00 +0330 Sun, 28 Dec 2025 00:00:00 +0330 https://jpom.ui.ac.ir/article_30187.html Producing defect-free products remains a major challenge in manufacturing industries due to complex operational risks. This study proposes a hybrid FMEA–ANP framework to improve the accuracy of risk assessment in the machine-made carpet industry. First, potential failure modes, their causes, and effects were identified using standard FMEA worksheets based on expert opinions from a large carpet factory in Kashan, Iran. Next, four main risk categories and 14 sub-risks were analysed. The Analytic Network Process (ANP), implemented in Super Decisions software, was applied to determine the relative importance of severity, occurrence, and detection criteria and to calculate weighted Risk Priority Numbers (RPNs). The results indicate that the most critical failure mode is the improper weaving of adjacent threads, followed by warp thread displacement, with both risks primarily concentrated in the weaving and spinning departments. Compared to conventional FMEA, the proposed approach provides a more realistic prioritisation of risks by accounting for interdependencies among evaluation criteria. The findings support managers in allocating resources more efficiently, reducing rework, and improving product quality. The study also highlights the applicability of the hybrid FMEA–ANP framework as a practical decision-support tool for risk management in manufacturing systems with complex process interactions. https://jpom.ui.ac.ir/article_30394.html Purpose: This paper aims to develop and propose three new multivariate control charts based on the MHWMA (Multivariate Homogeneously Weighted Moving Average) approach for monitoring multivariate simple linear profiles in Phase II of statistical process control. The primary motivations for this research are the limitations of existing methods in effectively detecting sudden shifts in profile parameters and the need for more sensitive monitoring schemes that accommodate the multivariate nature of regression relationships between dependent response variables and a single explanatory variable.Design/methodology/approach: To achieve the research objectives, three control methods—MHWMA, MHWMA/χ², and MHWMA-2/MMECD—are developed, each employing a distinct strategy for analyzing profile coefficients and covariance structures. The proposed charts use a uniformly weighted moving average to enhance detection capability. Performance is evaluated using the CVRL (Cumulative Variance of the Run Length) criterion via an extensive simulation study. Additionally, a sensitivity analysis assesses the robustness and efficiency of the proposed methods with respect to their design parameters. Finally, two real-world case studies validate the applicability and operational effectiveness of the proposed control charts.Findings: The simulation results indicate that the MHWMA/χ² monitoring method generally outperforms the other two proposed methods in detecting deviations in multivariate simple linear profiles across most scenarios. The sensitivity analysis demonstrates the stability and robustness of the proposed charts under various parameter settings. Furthermore, the two case studies confirm the effectiveness and practical applicability of the proposed control charts in real operational conditions.Research limitations/implications: This research is limited to Phase II monitoring of multivariate simple linear profiles. The proposed methods assume a known in-control profile model and normally distributed errors. Future research directions include extending the proposed approaches to nonlinear profiles, multivariate profiles with more than one explanatory variable, and Phase I analysis. Additionally, investigating the performance of the proposed charts under non-normal error distributions and auto-correlated data would be valuable.Practical implications: The proposed control charts provide quality engineers and process managers with effective tools for real‑time monitoring of complex industrial processes in which product quality is characterized by multivariate linear profile relationships. Implementing these charts can lead to earlier detection of process disturbances, reduced nonconforming output, lower rework and scrap costs, and improved overall process efficiency. The ease of interpretation and the demonstrated superior performance of the MHWMA/χ² method make it a practical choice for industrial applications.Social implications: By enabling more effective quality monitoring in complex industrial processes, this research contributes to improved product reliability and safety, directly benefiting consumers and society at large. Enhanced process control can reduce material waste and energy consumption, supporting environmental sustainability goals. Furthermore, the methodologies developed here can inform industry standards and regulatory policies related to statistical quality control practices, ultimately contributing to a higher quality of life through more consistent and reliable manufactured products.Originality/value: This paper introduces three novel control charts (MHWMA, MHWMA/χ², and MHWMA‑2/MMECD) for monitoring multivariate simple linear profiles, extending the application of the MHWMA framework to a previously unaddressed area. The proposed MHWMA/χ² method, in particular, demonstrates superior detection performance compared to existing alternatives. The comprehensive evaluation using the CVRL criterion, sensitivity analysis, and real case studies provides strong empirical support for the proposed methods. This research offers significant value to academic researchers in statistical process control as well as industrial practitioners seeking more sensitive tools for profile monitoring. https://jpom.ui.ac.ir/article_30079.html The present research aims to provide a supply chain management strategy model to evaluate the capabilities and limitations of the petrochemical industry with the In4 approach. The current research is an applied study in terms of purpose and qualitative in terms of its data type. This study identified the capabilities and limitations of using the thematic analysis method through interviews with ten industry experts. The supply chain management strategy was identified to evaluate the capabilities, including supply chain flexibility, supply chain sustainability, supply chain innovation, supply chain transparency, and supply chain coordination, which are defined by 15 indicators. At the same time, supply chain strategies to evaluate limitations included technological, financial, time, human resource, and organisational limitations, also outlined by 15 indicators. The findings emphasise the critical interaction between capabilities and limitations and indicate that strategically aligning these factors can result in more flexible and efficient supply chains. By strengthening capabilities while recognising, controlling, and reducing limitations, the petrochemical industries can effectively equip themselves to navigate the complexities of the industry. The findings of this research contribute to the theoretical understanding of supply chain management in the petrochemical industry sector. It likewise facilitates informed decision-making and strategic planning, ultimately contributing to sustainable growth and competitive advantage in the petrochemical industry. https://jpom.ui.ac.ir/article_30575.html Purpose: Organ transplantation is one of the most critical healthcare services for patients with end‑stage organ failure, yet a substantial gap persists between organ supply and demand. Limited transport time and short organ viability often result in long waiting lists and high patient mortality. Designing an efficient and resilient organ transplant network is therefore essential to improve organ utilization and patient outcomes. This study develops a multi‑objective facility location–allocation model for organ transplant networks. The model simultaneously determines optimal locations for organ procurement and transplant centers, assigns hospitals and demand regions, and manages organ flows while accounting for potential disruptions at transplant centers. Unlike previous studies, this work incorporates backup transplant centers to enhance network resilience and ensure continuity of service when primary centers fail due to technical issues, capacity overload, or emergencies.Design/Methodology/Approach: A bi‑objective mixed‑integer linear programming model is formulated to design an efficient organ transplant network. The model minimizes total cost and maximizes weighted organ flow while considering travel‑time and ischemia constraints. The network includes hospitals, organ procurement centers, transplant centers, transportation modes, and demand regions over a multi‑period horizon. Each primary transplant center is assigned a backup center to improve reliability during disruptions. Because the problem becomes computationally challenging for medium‑ and large‑scale instances, a multi‑objective simulated annealing (MOSA) algorithm is developed to obtain high‑quality solutions efficiently. The model is evaluated using test instances and compared with the augmented ε‑constraint method, demonstrating superior computational performance while maintaining solution quality.Findings: The results indicate that the proposed location–allocation model effectively balances total network cost and organ flow while improving system resilience. The MOSA algorithm generates high‑quality Pareto solutions in significantly shorter computational time, particularly for medium‑ and large‑scale problems. Although the augmented ε‑constraint method performs slightly better for small instances, the proposed heuristic becomes more efficient as problem size increases. Sensitivity analysis shows that higher disruption probability increases total cost and reduces organ flow, underscoring the importance of backup transplant centers. Additionally, organ demand increases cost only until supply becomes the limiting factor. Overall, the findings confirm that incorporating resilience considerations substantially improves organ transplant network design.Research limitations/implications: The study assumes deterministic demand, supply, and transportation conditions, along with a predefined disruption probability. However, real organ transplant systems involve uncertainties such as weather conditions, traffic congestion, donor availability, and unexpected operational failures. Future research may extend the model by incorporating more practical factors, including probability of successful transplantation at each center, patient waiting times, and the use of air transportation. Further studies may also examine capacity constraints, strategies to reduce organ wastage, and joint location of transportation agencies with other facilities to better assess their impact on time and cost.Practical implications: The proposed decision‑support framework assists healthcare managers in identifying optimal locations for organ procurement and transplant centers and making more effective allocation decisions. By incorporating backup transplant centers, it supports reliable planning and ensures service continuity during disruptions. The model helps reduce transportation time, improve transplant success rates, and minimize organ wastage, leading to more efficient resource use and saving more lives. From a managerial perspective, it reduces operational costs and enhances overall system performance. Moreover, the MOSA algorithm enables efficient solution of large‑scale planning problems, making the framework practical for real‑world decision‑making.Social implications: Improving organ transplant network design yields significant social benefits. By reducing organ losses caused by transport delays and increasing reliability during disruptions, the proposed approach can lower patient mortality, shorten waiting lists, and improve access to transplant services. Better utilization of donated organs also strengthens public trust in organ donation systems and supports fairer access to healthcare. Overall, it enhances patients’ quality of life and contributes to a more resilient healthcare system.Originality/value: This study offers several original contributions. First, it incorporates backup transplant centers into a strategic facility location–allocation model to improve resilience against disruptions. Second, it simultaneously maximizes weighted organ flow while considering travel‑time and ischemia constraints. Third, it integrates hospitals as the first operational level and models the transfer of brain‑dead donors from hospitals to procurement centers, an aspect rarely addressed in prior research. Finally, the development of a multi‑objective simulated annealing algorithm provides a practical solution approach for large‑scale organ transplant network design problems. https://jpom.ui.ac.ir/article_30220.html Supply chain agility (SCA) is critical for industries operating in volatile environments, yet limited research addresses technology-driven agility in sanctions-constrained contexts. This study investigates how emerging technologies enhance SCA in Iran's midstream oil terminals using a mixed-methods approach combining Grounded Theory (GT) and Interpretive Structural Modelling (ISM). Through interviews with 12 senior managers and a six-expert ISM panel, we identified 17 key factors across causal, contextual, and intervening conditions. The core phenomenon "Technology-Driven Supply Chain Agility" redefines technology from a supporting tool to a strategic imperative. ISM and MICMAC analyses revealed market instability and resource allocation as root drivers, while procurement challenges emerged as highly dependent outcomes. Four strategic frameworks were developed: Information Intelligence (blockchain, AI), Supply Digitalisation (3D printing, ML forecasting), Predictive Maintenance (IoT, digital twins), and Enabling Ecosystem Development (VR/AR training). This research advances theory by proposing a GT-ISM integration framework and provides practitioners with a prioritised roadmap for technology investment in high-risk environments. https://jpom.ui.ac.ir/article_30644.html Purpose: Telecommunications equipment manufacturing is among the most complex production sectors, marked by rapid technological change, shortened product cycles, global supply chains and rising customer expectations. Even minor disruptions can quickly translate into lost market share. While artificial intelligence offers considerable promise for improving demand forecasting, inventory management, supplier evaluation, maintenance and quality inspection, many organizations struggle with successful implementation. The literature review reveals that primary obstacles are predominantly organizational and cultural rather than technical. Prior research falls into three categories: macro‑level reviews lacking operational depth, quantitative studies testing predetermined hypotheses without contextual consideration and descriptive barrier inventories failing to capture dynamic interactions. These gaps are especially pronounced in Iran’s telecommunications industry, where firms additionally contend with sanctions, cultural norms and resource constraints. This study aims to design and empirically validate a context‑sensitive model for AI deployment tailored to supply and production processes in an Iranian telecommunications equipment manufacturer.Design/methodology/approach: This research adopted a sequential exploratory mixed‑methods design. The qualitative phase employed systematic grounded theory (Strauss and Corbin) with its paradigm model. Data were collected from 26 in‑depth semi‑structured interviews with executives, managers and IT specialists. Purposive sampling with snowball techniques was used until theoretical saturation. Analysis proceeded through open, axial and selective coding. The quantitative phase used variance‑based structural equation modeling (PLS‑SEM) through SmartPLS. A 17‑item Likert‑scale questionnaire, derived from qualitative findings, was completed by 100 managers and experts, exceeding the minimum 40 cases suggested by the ten‑times rule. The measurement model was evaluated for reliability, convergent validity and discriminant validity. Structural model testing used bootstrapping with 5,000 resamples.Findings: Qualitative analysis yielded categories mapped onto the paradigm model. Causal conditions captured internal and external pressures: rising costs, compressing margins, competition, demand fluctuations and the need for faster decisions. The core phenomenon emerged as behavioral intention to deploy AI, representing commitment through management advocacy, strategies, budgets and project teams. Contextual conditions encompassed culture, structure, IT infrastructure, leadership and executive backing. Intervening conditions were constraining: talent shortages, budget limitations, sanctions, employee resistance, fragmented AI understanding and cybersecurity concerns. Strategies included pilots, staff training, recruitment, outsourcing and partnerships. Consequences covered lower costs, improved quality, reduced downtime, agility and resilience. Quantitative analysis confirmed reliability and validity. Path analysis revealed that causal conditions significantly shape strategies (β=0.485, t=6.762), and intervening conditions strongly influence strategies (β=0.470, t=7.327). Strategies predicted consequences (β=0.448, t=7.069), which exerted the strongest effect on behavioral intention (β=0.746, t=17.090). Intervening conditions directly influenced behavioral intention (β=0.160, t=2.728). Contextual conditions showed no significant direct path to strategies (β=-0.198, t=1.749), suggesting they function primarily as foundational enablers and prerequisites rather than direct drivers of strategic choices. The moderating effect of intervening conditions on the causal‑conditions‑to‑strategies relationship was significant (β=-0.165, t=2.304), indicating that pronounced obstacles weaken the influence of causal pressures on strategy selection.Research limitations/implications: The single case study limits generalizability. Future comparative studies across multiple sectors such as automotive, pharmaceuticals or petrochemicals could test broader applicability. Cross‑sectional data restrict observation over time; longitudinal designs would be valuable for tracking dynamic feedback loops. Environmental moderators including government policies, sanctions intensity and national digital maturity were not separately examined. Despite limitations, the mixed‑methods design and empirical support provide a solid platform for theoretical refinement and practical application.Practical implications: Managers should adopt a pilot‑oriented approach based on the robust path from strategies to consequences and the stronger link from consequences to behavioral intention. Instead of large, expensive projects, managers might select well‑defined operational areas with clear AI potential, design pilots with explicit goals and metrics, build teams combining technical and operational expertise and document results transparently. Successful pilots build trust and reinforce intention to continue. Managers must actively address intervening conditions through upskilling investments, dedicated budgets, compelling business cases and structured change management. Framing AI as augmenting rather than replacing human capability reduces resistance. Creating systematic feedback loops that measure and communicate tangible outcomes—cost reductions, quality improvements—generates positive reinforcement cycles sustaining long‑term transformation.Social implications: AI contributes to sustainable production through reduced waste and optimized resource use. Enhanced quality control and predictive maintenance mean fewer defective products and less downtime. The model emphasizes upskilling over automation, alleviating job‑displacement fears. In Iran’s context, this locally grounded approach builds internal capabilities, strengthening national self‑sufficiency. Transparent communication fosters public trust. Benefits include preserving skilled jobs, improving product quality and supporting responsible environmental practices, aligning with corporate social responsibility and informing policy.Originality/value: This research offers several novel contributions. First, it employs a sequential exploratory mixed‑methods design combining qualitative depth with quantitative validation, uncommon in developing‑country manufacturing. Second, it produces a dynamic paradigm model capturing causal interplay, revealing two specific findings: the direct effect of intervening conditions on behavioral intention and the moderating role of intervening conditions in the causal‑conditions‑to‑strategies relationship. Third, the focus on telecommunications equipment manufacturing fills a gap where no prior localized AI deployment model exists. Fourth, the study undertakes thorough localization for Iran’s business environment, explicitly incorporating sanctions, limited technology access, organizational culture and scarce AI talent. The result is an empirically grounded theory offering researchers a framework for testing and practitioners a roadmap for navigating AI adoption complexities. https://jpom.ui.ac.ir/article_30264.html In regulated pharmaceutical markets with fixed pricing and government oversight, trade credit is widely used to manage demand and sustain market share; however, uncoordinated use can lead to financial imbalances, bankruptcies, and restricted access to essential medicines, particularly in subsidised systems. This paper develops an optimisation framework for a two-echelon pharmaceutical supply chain, incorporating fixed prices, subsidies, and temperature-sensitive perishability, to prevent financial flows from undermining drug supply and access. The model employs concave fractional programming in a Stackelberg game; the manufacturer sets credit terms, and the distributor optimises replenishment. A numerical search algorithm tackles computational complexity. Multi-scenario analyses (coordinated, non-coordinated, centralised) reveal that treating trade credit as a dual-purpose instrument, supporting both demand management and coordination, improves profitability, financial stability, and drug availability. A demand function that captures sales managers’ credit decisions and competitive credit effects is incorporated, and subsidies on key inputs are shown to strengthen financial flows. A Weibull-based inventory model is employed to reduce spoilage in temperature-sensitive drugs. Validation via Python-based brute-force search confirms the accuracy of the optimisation results. The framework provides managerial and policy insights for balancing economic sustainability and public health. https://jpom.ui.ac.ir/article_30665.html Purpose : This paper aims to develop and validate an integrated system dynamics model for analysing the dynamic interactions between sustainability and resilience in industrial ecosystems through the simultaneous consideration of four interdependent sustainability subsystems: economic, environmental, social and institutional. Motivated by the increasing complexity of industrial ecosystems and the inability of conventional linear models to capture nonlinear feedback relationships, the study seeks to explain how these interconnected dimensions influence long‑term system behaviour and sustainable production performance. Using Kerman Province, Iran, as a case study, the research investigates how causal interactions among economic growth, environmental resource constraints, social development and institutional governance shape production resilience and identifies high‑leverage intervention points for improving long‑term sustainability while balancing economic prosperity, environmental protection, social well‑being and institutional effectiveness. Design/methodology/approach : The study adopts a system dynamics methodology to model the complex feedback structures governing sustainability and resilience in industrial ecosystems. An integrated stock‑and‑flow simulation model was developed by combining four interrelated subsystems representing the economic, environmental, social and institutional dimensions of sustainable development. The model incorporates causal loop diagrams and feedback mechanisms derived from the sustainability and resilience literature and is calibrated using data from Kerman Province. Model structure and behaviour were verified through the Extreme Conditions Test, Model Structure Verification Test and scenario‑based validation using Vensim. Multiple policy scenarios were simulated to examine the effects of research and development (R&D) investment, agricultural water‑demand management, quality‑of‑life improvements and institutional reforms. Comparative scenario analysis evaluated their effects on gross domestic product, production performance, resource availability, pollution, demographic trends, governance quality and system resilience. Findings : Sustainability and resilience emerge from the interaction of all four subsystems rather than from isolated interventions. Sustained investment in R&D generates long‑term improvements in gross domestic product, production output, technical efficiency and innovation adoption, whereas limited investment produces only marginal gains. Aggressive investment in agricultural water‑demand management, pollution control and ecosystem restoration is essential for achieving environmental sustainability and resilient production systems, while moderate interventions provide only temporary improvements. Social policies that improve healthcare, education and quality of life stabilise demographic trends, reduce mortality and strengthen the long‑term labour force. Institutional reforms that enhance government effectiveness, accountability, rule of law and anti‑corruption measures improve governance quality and adaptive capacity. Comparative scenario analysis indicates that water‑demand management has the greatest influence on long‑term production sustainability, followed by quality‑of‑life improvements, institutional effectiveness and R&D investment. Overall, coordinated policy implementation across the four sustainability dimensions produces greater resilience and sustainability than isolated sectoral policies. Research limitations/implications : The system dynamics model necessarily simplifies complex real‑world interactions, which may limit representation of all behavioural dynamics. The empirical analysis focuses on a single case study in Kerman Province, limiting direct generalisation to other regions or industrial contexts. In addition, calibration and validation relied partly on incomplete or historical datasets, requiring certain assumptions and parameter estimation. Future research should apply the framework to different manufacturing sectors and regions, incorporate more comprehensive real‑time datasets, integrate additional sustainability dimensions and combine system dynamics with complementary modelling approaches to improve predictive capability and external validity. Practical implications : The proposed framework provides policymakers, industrial managers and sustainability practitioners with a decision‑support tool for evaluating long‑term policy alternatives before implementation. The results suggest that sustained investment in R&D, cleaner production technologies, integrated water‑resource management, environmental restoration, education, healthcare and institutional reform should be prioritised to improve manufacturing resilience and sustainability. The model enables decision‑makers to identify leverage points, anticipate unintended consequences and coordinate interventions across economic, environmental, social and institutional domains, thereby improving resource efficiency, operational resilience and long‑term production performance. Social implications : Sustainability policies extending beyond economic performance can generate substantial societal benefits. Improvements in education, healthcare and quality of life contribute to healthier demographic structures, stronger labour‑force stability and enhanced social well‑being. Environmental policies that reduce pollution and conserve water resources improve ecosystem resilience and support long‑term community welfare. Strengthening governance, transparency and institutional effectiveness also promotes public trust, reduces corruption and improves policy implementation. The framework therefore provides evidence for governments seeking to promote sustainable industrial development while enhancing environmental quality, social equity and institutional resilience. Originality/value : The principal contribution of this study lies in integrating the four core dimensions of sustainable development (economic, environmental, social and institutional) within a single system dynamics framework that explicitly incorporates resilience into sustainability analysis. Unlike previous studies that examine these dimensions independently, the proposed model captures their nonlinear interactions, feedback mechanisms and long‑term dynamics within industrial ecosystems. By validating the framework through a real‑world case study and identifying policy leverage points that simultaneously improve sustainability and resilience, the study provides both a novel theoretical framework and a practical decision‑support model for policymakers, industrial managers and researchers interested in resilient manufacturing systems. https://jpom.ui.ac.ir/article_30671.html Purpose: The design of submarine pressure hulls inherently involves conflicting objectives: weight minimization, safety enhancement, cost reduction, and operational effectiveness. Traditional deterministic approaches rely on overly conservative safety factors, leading to 18–25% excess weight. Additionally, a significant gap exists between engineering simulations and multi‑stakeholder, risk‑informed design criteria. This research develops a novel probabilistic multi‑criteria decision‑making framework that integrates Reliability‑Based Design Optimization (RBDO) with multi‑criteria analysis to achieve an optimally balanced design for submarine pressure hulls, simultaneously addressing weight, reliability, cost, and operational effectiveness. Design/methodology/approach: The framework comprises seven main stages. A baseline pressure hull was defined per marine standards (ABS, DNV) for 500 m depth. Design variables include six geometric parameters: shell thickness, stiffener spacing, web height and thickness, and flange width and thickness. The objective is weight minimization under probabilistic constraints for three failure modes (yield, buckling, collapse) with a target reliability index β≥3.5 (Pf≤2.3×10⁻⁴). Nonlinear finite element analysis was performed in Abaqus/Standard using S4R shell elements, incorporating geometric and material nonlinearities for A516 Grade 70 steel (E=200 GPa, ν=0.3, yield 260 MPa). The Riks method handled post‑buckling. Validation against analytical and experimental data showed <5% discrepancy. Uncertainties were modeled as yield strength (normal, COV=2.7%), shell thickness (normal, COV=3%), and out‑of‑roundness (Gumbel, COV=40%). A genetic algorithm (population 50, 100 generations) with RBDO constraints generated a Pareto frontier (M1–M5). A Kriging surrogate (R²>0.98) reduced computational cost in Monte Carlo simulation (100,000 samples). Global sensitivity analysis used importance vectors α, δ, η. The Analytic Hierarchy Process (AHP) with ten naval experts evaluated the Overall Measure of Effectiveness (OMOE) across six operational capabilities. Life‑Cycle Cost (LCC) was calculated via activity‑based costing (material, manufacturing, welding). Finally, a multi‑criteria comparison integrated weight, reliability, LCC, and OMOE to select the optimal design. Findings: The framework achieved 23.4% weight reduction (51.0 to 39.1 t) with a reliability index of 3.50. Model M2 was optimal, satisfying β≥3.5 while maximizing weight reduction. Model M5 had similar weight reduction but failed reliability (β=3.10<3.5). M2 scored OMOE=0.85 vs. baseline 0.67. LCC showed a 40.9% cost reduction ($42,640 to $25,205 k), mainly from lower welding costs. Meeting the reliability target required only 0.4% extra cost compared with unreliable M5, demonstrating a high safety return. Sensitivity analysis identified yield strength as dominant (70% contribution to failure variance). The η vector confirmed that reducing yield strength standard deviation (|η|=0.78) most effectively lowers failure probability, prioritizing material quality control. Stiffener spacing affects weight reduction more than shell thickness. Research limitations/implications: Limitations include: static collapse only (no dynamic/fatigue), restriction to A516 steel (no composites), omission of detailed manufacturing effects (e.g., welding distortions), and reliance on ten naval experts (broader stakeholder input could improve robustness). Future work should incorporate spectral fatigue and dynamic loading, extend to advanced materials, integrate manufacturing simulations, and apply multi‑fidelity modeling to reduce computational costs. Practical implications – For submarine project managers: (1) early‑stage RBDO can cut weight by up to 23.4%, improving maneuverability and fuel efficiency; (2) yield‑strength quality control is the most cost‑effective safety strategy; (3) manufacturing tolerances must be included in reliability assessments; (4) OMOE enables balanced trade‑offs; (5) the Pareto frontier aids in selecting designs for specific missions. Social implications: The risk‑based framework applies to other pressure vessels (deep‑sea submersibles, offshore platforms, pipelines), enhancing marine safety and environmental protection. Improved reliability lowers catastrophic failure risks (loss of life, oil spills, ecosystem damage). Cost optimization reduces public spending on defense and infrastructure. Emphasis on material quality fosters higher industry standards and workforce skills. Originality/value: Novelty lies in integrating weight, reliability, cost, and operational effectiveness within one probabilistic framework—unlike prior isolated studies. Contributions include advancing risk assessment via Monte Carlo with Kriging; enabling safety‑reliability trade‑offs via RBDO–AHP integration; demonstrating real‑world feasibility; and offering a multi‑stakeholder decision tool. This provides significant value to naval engineers, project managers, and offshore industry decision makers. https://jpom.ui.ac.ir/article_30852.html strategic sourcing teamformation is a fundamental challenge for organizations facing sanctions and economic instability. While previous research has largely focused on supplier selection, the issue of sourcing team building, considering specialized skills, operational resilience, and communication networks simultaneously, has received less attention. This research aims to bridge this research gap and address the specific needs of Iranian companies by designing a novel multi-objective optimization model for strategic sourcing team building. The research is applied in terms of its objective and employs a mixed-method (qualitative-quantitative) approach. In the qualitative phase, criteria and localized indicators for sourcing team building under sanctions were extracted through literature review and interviews with 15 industry and university experts. The quantitative phase involved designing a multi-objective mathematical model with four objectives: (1) maximizing coverage of specialized skills to circumvent sanctions, (2) minimizing team building costs, (3) maximizing localized operational resilience, and (4) maximizing communication network power. The model, considering 10 types of constraints including currency allocation limitations, was implemented in the GAMS software environment and solved using an improved epsilon-constraint method. Implementation of the model in a case study within the Iranian industry demonstrated its ability to: increase coverage of specialized skills to counter sanctions by up to 35%, reduce team building costs by up to 28%, improve the operational resilience index by up to 42%, and increase the team’s communication network power by up to 37%. Sensitivity analysis indicated that the model exhibits good stability and provides efficient solutions under changing conditions. https://jpom.ui.ac.ir/article_30853.html The electricity sector, as one of the fundamental infrastructures underpinning industrial and economic development, has consistently faced challenges related to the imbalance between supply and demand. In recent years, this imbalance has become particularly pronounced during peak seasons—both in summer and winter—resulting in scheduled power outages in the industrial sector and causing substantial economic losses. Given the critical importance of energy management and the need to mitigate the adverse impacts of outages on industrial operations, short-term outage forecasting can serve as a key tool for improving planning processes and enhancing grid resilience. In this study, focusing on industrial feeder data from an industrial city in Iran during the year 1403 (2024–2025), a hybrid machine learning–based model was developed to predict the occurrence of power outages over a short-term horizon. The dataset included electricity load consumption, weather variables (temperature and wind speed), temporal and calendar-related features, as well as lag-based derived variables. The findings demonstrate that machine learning approaches can provide an effective framework for predicting outages in industrial power distribution networks. The proposed model—particularly the LightGBM algorithm—achieved an overall accuracy of approximately 99.9% and an F1-score of 0.989, successfully capturing and reproducing outage occurrence patterns. The ability to forecast outages can function as an early warning indicator and decision-support tool for power distribution companies and industrial managers, enabling more proactive and resilient operational planning. https://jpom.ui.ac.ir/article_30859.html Despite the environmental and economic benefits of the circular economy paradigm, the transition from a linear model to a circular supply chain in manufacturing industries—particularly in developing countries—faces numerous structural barriers. This study aims to identify, refine, and analyze the causal relationships among the key barriers to implementing a circular supply chain in Iran’s automotive parts manufacturing industry. In the first phase, 18 barriers were extracted from the literature and classified into five categories: technical, economic, regulatory, cultural, and operational. Using the fuzzy Delphi method and the judgments of nine senior and middle managers from leading automotive parts companies, these barriers were reduced to 10 critical barriers. In the second phase, the Pythagorean Fuzzy DEMATEL method was employed to analyze the causal structure among the identified barriers and to distinguish cause factors from effect factors while capturing uncertainty in expert evaluations. The findings reveal that the barrier “considering remanufacturability in the initial design of parts” has the highest causal influence (D−R) and plays a pivotal leveraging role within the system. In contrast, the barrier “difficulty and risk associated with disassembly and reassembly of parts” was identified as the most affected (effect) factor. The results indicate that redesigning product architecture, strengthening targeted policy support, and facilitating financial resources are essential prerequisites for accelerating the transition toward a circular supply chain in the automotive parts sector. By providing a causal network map of structural barriers, this study offers a strategic analytical framework to assist policymakers and industry managers in prioritizing corrective actions https://jpom.ui.ac.ir/article_30872.html In real-world production systems, simultaneous decision-making regarding production, quality control, and preventive maintenance in multi-echelon supply chains with defective items constitutes a fundamental operational management challenge. Despite extensive development of Economic Production Quantity (EPQ) models, a literature review reveals a significant research gap in integrated modeling of three-echelon supply chains. Most studies consider only isolated aspects of industrial realities, such as defective items, inspection errors, or preventive maintenance, neglecting the simultaneous analysis of these factors within a comprehensive analytical framework. To address this research gap, the main contribution of this paper is the development of an integrated EPQ model for a three-echelon supply chain, comprising the manufacturer, the buyer of defective items, and the buyer of non-defective items. The model simultaneously considers multi-machine production, 100% inspection with Type I and Type II errors, preventive maintenance, and the categorization of defective items into repairable and non-repairable. In the proposed model, costs associated with production, maintenance of non-defective and defective items, inspection, repair, disposal, production downtime due to maintenance, and penalties for inspection errors are explicitly included in the objective function. The model aims to minimize the total system cost by determining the optimal production cycle length, machine-wise production quantities, and inventory levels at each echelon. The model is solved analytically, and the optimal production cycle length is derived directly through differentiation of the cost function. https://jpom.ui.ac.ir/article_30873.html Introduction: The healthcare supply chain, due to exposure to numerous uncertainties, requires achieving viability through the realization of agility, sustainability, and resilience. Given the lack of research identifying supply chain competencies for achieving viability, the aim of this study is to identify and analyze supply chain competencies for achieving a viable supply chain in Iran's healthcare sector. Methodology: This research was conducted using a mixed-method approach. In the qualitative phase, through interviews with experts and thematic analysis, the necessary competencies were identified and categorized. In the quantitative phase, fuzzy cognitive mapping was employed to determine the relationships and the degree of influence and susceptibility of each supply chain competency. Findings: Based on the results, 38 supply chain competencies were identified within 9 main categories. The findings also indicated that "financial competency and sustainable resource provision" and "collaboration and inter-organizational co-creation competency" have the highest degree of influence on other competencies, respectively. Furthermore, "financial competency and sustainable resource provision" and "risk management and supply chain resilience competency" had the highest degree of centrality in the relationship network. Scenario analysis also emphasized the pivotal role of "financial competency and sustainable resource provision" and "logistics and distribution management competency" in achieving viability. Conclusion: The findings clearly emphasize the need for sustainable financial provision through instruments such as the "National Sustainable Health Fund." Additionally, an "Integrated National Information and Procurement Management System" is essential for strengthening logistics and distribution management competencies. https://jpom.ui.ac.ir/article_30877.html Effective management of emergency evacuation, as a cornerstone of urban resilience, plays a decisive role in mitigating casualties and loss of life during natural and man-made disasters. Optimizing traffic flow under such critical conditions, particularly in densely populated urban environments, is essential to prevent network gridlock. To this end, this study proposes a framework to enhance emergency evacuation performance by utilizing the operational capacity of fuel stations as a control variable to regulate vehicle entry into the evacuation network. Four evacuation scenarios were developed and evaluated using a traffic assignment model: (i) uncontrolled evacuation, (ii) system-optimal route assignment, (iii) demand entry rate control, and (iv) the proposed fuel station capacity control. In the proposed scenario, the operational capacity of fuel stations is modulated using a control coefficient, and its effect on the trade-off between station waiting times and network congestion is thoroughly investigated. The model was applied to the Sioux Falls road network. Results indicate that the average evacuation time under the uncontrolled scenario was 47.49 hours, while system-optimal route assignment reduced this to 3.26 hours. Furthermore, the optimal demand release rate yielded an evacuation time of 1.24 hours. Notably, the proposed fuel station capacity control scenario achieved the best overall performance, reducing the evacuation time to 2.72 hours. These findings demonstrate that fuel stations can serve as effective flow regulation nodes, playing a pivotal role in managing evacuation demand and significantly alleviating network congestion.