Industrial Management Journal
Year:2025 | Volume:17 | Issue:4|Papers Count:6

Objective: This study aims to evaluate the greenness and efficiency of the Iranian petrochemical supply chain, a sector that plays a vital role in both economic performance and environmental sustainability. Despite its importance, limited studies have comprehensively analyzed this industry’s efficiency using multi-dimensional and uncertainty-sensitive approaches. Methods: To address this issue, an integrated Network Data Envelopment Analysis (NDEA) framework combined with the Fuzzy Delphi Method was developed to assess the performance of ten leading petrochemical companies in Iran. Seventeen evaluation criteria were identified and validated, and the companies were analyzed under optimistic and pessimistic scenarios to capture a balanced and realistic view of their efficiency.   Results: The findings revealed that only a few companies were efficient under both scenarios, while others exhibited inefficiencies due to high environmental costs, excessive employment, and poor-quality management systems. Sensitivity analysis showed that reducing undesirable outputs and optimizing dual-role variables significantly improves performance. Efficient companies should also focus on sustaining competitiveness by optimizing their pessimistic efficiency scores. Conclusion: The results suggest that the proposed NDEA–Delphi approach provides a comprehensive and realistic tool for assessing the green efficiency of industrial supply chains. This framework can support decision-makers in identifying improvement areas, reducing resource waste, and developing environmentally responsible operational strategies in the petrochemical sector

Objective: Globally, road traffic accidents cause significant humanitarian, social, and economic costs, resulting in the need to have efficient and fast response mechanisms. Data-based tools can improve humanitarian aid's speed and equity using mathematical modeling, especially optimization, stochastic, fuzzy, and System Dynamics methods. This paper provides a systematic review of the role of these models in helping with post-accident humanitarian strategies and determining key factors that can affect the success of such models due to uncertainty.Methods: A systematic review was performed under PRISMA guidelines using the PICOS framework. Scopus and Web of Science literature were analyzed, focusing on peer-reviewed studies applying mathematical modeling to humanitarian response in road-accident contexts. Models were categorized by data type (stochastic, deterministic, fuzzy), method (exact vs. heuristic), and capability in managing uncertainty and feedback. Special attention was given to System Dynamics, which captures nonlinear feedback loops and time delays in prevention and response systems. Results: Recent research highlights a shift toward predictive analytics, IoT, and machine learning to improve humanitarian logistics. Stochastic and fuzzy models effectively address real-world uncertainties, while dynamic and feedback-based models, particularly SD, outperform static ones by enhancing resource allocation, reducing response times, and strengthening decision-making.Conclusion: The mathematical modeling (in particular, with integration into the System Dynamics) demonstrates the possibility of humanitarian aid optimization in road accident handling. The paper highlights evidence-based, adaptive, and feedback-driven solutions through real-time information and uncertainty modeling to develop resilient, efficient, and scientific information-informed emergency response systems.

Objective: This research presents a new method for scheduling troubleshooting operations of station regulators in natural gas distribution stations, focusing on the importance index of equipment, reliability, and risk management.Methods: Using reliability-based maintenance principles and the expertise of professionals from Isfahan Gas Company, we selected 166 regulators from 112 pressure reduction stations in Isfahan. We assessed the importance index of each station and evaluated the potential consequences of its failure risks, followed by calculating its reliability metrics. The results were grouped using the K-means clustering method. Ultimately, we identified the optimal time frame for conducting troubleshooting operations. Results: In this study, 166 regulators were grouped into three clusters. The average time required to perform troubleshooting activities varied among the clusters. For the first cluster, the average time was determined to be 48 hours. The second cluster had an average troubleshooting time of 544 hours, while the third cluster had an average of 829 hours. Currently, the average time for troubleshooting regulators is 720 hours.Conclusion: This paper presents the following contributions: 1. Identification of the station importance index based on the gas supply mission to subscribers and end consumers. 2. Localization of the method for estimating risks and consequences arising from station equipment failures. 3. Assessment of equipment reliability. 4. Clustering of key regulatory equipment in the case study.

Objective: The Supply chain plays a key role in adapting the organization to variable conditions and an uncertain future. The selection of appropriate suppliers can significantly increase the competitiveness and ability of a business in the market. One of the essential factors in supply chain optimization is controlling and managing inventory cost. This paper aims to simultaneously optimize supplier selection and order allocation while considering inventory control using a fractional programming approach.  Methodology: The methodology integrates quantitative analytical techniques in a multi-phase approach. First, the most frequent supplier selection criteria are identified with a literature review. The Delphi method was used to select the supplier selection criteria. In the next step, fuzzy Shannon entropy determines criterion weights. Then, fuzzy EDAS calculates supplier performance scores. Finally, fractional programming facilitates supplier selection and order allocation. Results: The most frequent supplier selection criteria were extracted from the literature review. In the Delphi technique, experts ultimately agreed on six key criteria: price, quality, delivery, flexibility, responsiveness, and financial stability. The results of the Shannon entropy analysis indicate that flexibility, with a weight of 0. 20, holds the highest relative importance among the criteria. The suppliers score obtained from the fuzzy EDAS method is used as one of the parameters of the mathematical model. Conclusion: The proposed hybrid MADM approach and mathematical model have been validated using empirical data obtained from Sirjan Steel Company. The result shows that the hybrid MADM approach and fractional programming have high accuracy in selecting the best supplier.

Objective: This study introduces a location-routing model tailored for parcel delivery in large, sparsely populated regions with limited infrastructure. It aims to minimize system costs by optimizing hub placement, city-to-hub assignments, routing paths, and fleet composition. The model accounts for real-world complexities such as diverse vehicle types, flexible delivery time windows, and multiple pickup/delivery paths, offering a strategic planning tool for logistics operations in challenging environments.   Methodology: To solve this NP-hard problem, the researchers reformulated a mixed-integer nonlinear program (MINLP) into a more computationally efficient mixed-integer programming (MIP) model. For larger instances, they developed a two-stage hybrid metaheuristic: the first stage uses an Artificial Bee Colony (ABC) algorithm to explore hub locations and initial allocations, while the second stage applies Simulated Annealing (SA) with local search to optimize routing and assignments. Validation was performed using CPLEX for small instances and benchmarked against a published SA-based method across 75 test scenarios and two real-world case studies from an Iranian parcel delivery company. Results: The hybrid method achieved optimal or near-optimal solutions faster than CPLEX for minor problems and outperformed the SA benchmark for larger ones, improving solution quality by 4% and reducing routes by 11%. The model also increased 24-hour deliveries by 4% without raising costs. The SA phase alone contributed a 1. 6% cost reduction by restructuring the network. Case studies confirmed the model’s practical value, consistently identifying robust hub configurations across diverse network scales and operational strategies. Conclusion: This study presents a strategic planning tool for parcel delivery in challenging geographic and infrastructural conditions. It enables logistics managers to minimize operational costs while maintaining stable hub configurations during network expansion. A case study in Iran highlights its long-term value: a four-hub network with a 680 km line-haul limit offers superior nationwide coverage compared to a three-hub setup with a 510 km limit focused on major cities.

Objective: This study addresses the fabless manufacturing business model's increasing relevance and complexity of decision-making. The primary aim is to develop and evaluate a simulation model for analyzing competitive strategies and optimizing managerial decisions in fabless supply chains.   Methodology: An agent-based simulation approach was employed to model interactions between fabless companies and manufacturing factories. The decision-making process for manufacturing partners was based on three key criteria: quality, cost, and availability. The simulation was implemented using AnyLogic software and analyzed under competitive and non-competitive market scenarios. Validation was conducted using real-world data to ensure model accuracy and applicability. Results: The study reveals that the weighting of criteria—quality, cost, and availability—significantly affects company performance in fabless manufacturing supply chains. Companies prioritizing quality tend to gain long-term advantages, while those focusing on cost may achieve short-term profits but struggle with sustainability. Competition complicates the balance of these criteria, leading to increased system-wide costs. These findings emphasize the need for nuanced strategies in dynamic markets. Conclusion: The developed simulation model offers a robust quantitative framework for analyzing and optimizing decision-making in fabless manufacturing supply chains. It is a valuable decision-support tool for managers, enabling them to adopt optimal strategies that reduce costs, enhance product quality, and improve customer satisfaction in dynamic and competitive market conditions.