International Journal of Applied Operational Research

Evaluating high-consumption and unusual subscribers in the smart gas meter network using machine learning and the Internet of Things in the cloud environment

H. Mehrmanesh

With the advancement of new technologies, the use of smart gas meters as a tool for managing energy consumption and optimizing energy resources is expanding. These meters can collect and analyze consumption data in real time with the help of Internet of Things (IoT) and machine learning. The aim of this research is to evaluate and identify high-consumption and abnormal subscribers in the smart gas meter network using machine learning algorithms and Internet of Things technology in a cloud environment. This research seeks to provide solutions to improve energy consumption management and reduce costs by identifying abnormal consumption patterns and providing optimization suggestions to subscribers. The importance of energy consumption management and the implementation of related policies have required governments to identify high-consumption subscribers and separate them from low-consumption subscribers. Accordingly, policies are being developed to fine or punish high-consumption subscribers based on their consumption and even reward low-consumption subscribers. This is possible more efficiently using a smart meter network in which data is transferred in real time on the Internet of Things network and stored in a cloud computing environment.
In this research, in line with this policy, an attempt has been made to design a model to identify and control high-consumption and irregular subscribers in the smart gas meter network. This model includes 5 variables: annual consumption, monthly consumption, consumption period, household size, and subscription type, which were implemented using 4 machine learning algorithms: random forest, decision tree, nearest neighbor, and XG boost.
The results show that the random forest algorithm was able to classify and identify high-use subscribers with 92% accuracy, followed by the XG boost algorithm with 91% accuracy, and then the nearest neighbor and decision tree algorithms with 90% accuracy.
The conclusion of this research shows that the use of machine learning algorithms and IoT technology in the smart gas meter network can help to accurately identify high-consumption and abnormal subscribers. This not only leads to energy consumption optimization and cost reduction, but also enables the implementation of effective policies for energy consumption management.

Development of returns to scale in two-stage network DEA via parametric analysis

A. Mostafaee

Network Data Envelopment Analysis (DEA) has attracted considerable attention in both methodological research and practical applications of performance evaluation. This paper investigates a fundamental class of network DEA models, namely the two-stage network framework. In conventional DEA, where the production process is viewed as a “black box,” returns to scale (RTS) plays a critical role in guiding managerial decisions on whether to expand or contract operations. This study extends the traditional concept of RTS to a two-stage network by examining input variations from three perspectives: stage 1, stage 2, and the overall system. The proposed approach employs parametric analysis to capture how these variations affect the relationships among inputs, intermediate measures, and final outputs. To ensure practical applicability, the method can be implemented through existing linear programming formulations and remains computationally feasible even for larger-scale problems. In addition, we develop a linear programming model that supports central managers in coordinating resource allocation across different stages, thereby achieving system-wide improvements. A numerical example illustrates that RTS classifications at the system and sub-process levels may diverge, offering distinct insights into pathways for enhancing productivity.

Fuzzy cognitive mapping for investigating resilience in Iranol company: An analytical perspective

M. Homayounfar

This study investigates the factors influencing organizational resilience within Iranol company, a major player in Iran’s oil and gas industry. Using a systematic literature review, fuzzy Delphi, and Fuzzy Cognitive Mapping (FCM), the research identifies and prioritizes key resilience indicators. Data were collected from 13 senior experts through pairwise comparison questionnaires, and analysed with FCM Expert software to capture causal interdependencies among factors. The findings reveal that human-centric drivers including HR empowerment, employee participation, organizational readiness, team learning, and training hold the highest centrality in fostering resilience, whereas structural elements such as flexible culture and agile structures play more supportive roles. These results highlight the importance of empowering employees and cultivating a collaborative, learning-oriented environment to strengthen organizational resilience. The study contributes to resilience research by applying FCM to the oil and gas sector, demonstrating its value for modelling complex, feedback-rich systems. Practical recommendations are provided for managers seeking to enhance resilience in volatile environments, while limitations and future research directions are discussed.

A fast and scalable heuristic for makespan minimization in permutation flowshop scheduling

A. Olalekan Olasupo

The permutation flowshop scheduling problem (PFSP) is a classical NP-hard problem in production and operations management, where the objective is to minimize the makespan across multiple machines. Although established heuristics such as NEH, Gupta, and CDS are widely applied, their performance often declines in large-scale instances due to increased computational time and reduced scalability. This study proposes a fast heuristic based on a modified Johnson’s rule applied pairwise between the first machine and each subsequent machine. For each pair, Johnson’s two-machine algorithm generates a sequence, which is then evaluated on the full set of machines, and the best-performing sequence is selected as the final solution. Computational experiments on randomly generated instances of different sizes demonstrate that the proposed method achieves competitive makespan performance while significantly reducing CPU time compared to NEH and CDS, and providing better scalability than Gupta. Statistical validation using the Wilcoxon signed-rank test confirms that the proposed heuristic outperforms Gupta in solution quality and is considerably faster than NEH and CDS in execution time. These findings establish the proposed heuristic as a computationally efficient and statistically reliable approach for solving large-scale PFSPs, providing a valuable tool for production scheduling in industrial operations.

Enhancing Human Resource Productivity Through a BWM-BSC Framework: A Case Study

M. R. Dehghani

Organizations need to utilize their available resources efficiently and effectively to survive and succeed. Human resources, as one of the most important and valuable organizational assets, play a key role in enhancing productivity. Various factors influence human resource productivity, and the significance of each factor differs across organizations. Therefore, identifying and prioritizing the factors affecting human resource productivity is essential so that organizations can plan and set goals to improve the most critical factors. In this study, after reviewing the literature and conducting expert team sessions, 29 factors influencing human resource productivity were identified and finalized in Fars Combined Cycle Power Plant, one of the thermal power plants. These factors were then classified into the four perspectives of the Balanced Scorecard (BSC). Subsequently, the Best-Worst Method (BWM), as a trustable method with a high consistency rate for solving Multi-Criteria Decision-Making (MADM) problems, was used to weight and prioritize the identified factors. The results indicated that the five most important factors affecting human resource productivity are: the existence of a proper salary and compensation system, in-service training, attention to employee needs to increase motivation, work ethics, commitment and responsibility of employees, and timely payment of salaries and benefits

Circular Supply Chains and Reverse Logistics-Challenges, Solutions, Operational Insights, and Global Impacts on Sustainable Development: A Systematic Review

M. Salehi Sarbijan

The transition from the unsustainable linear “take-make-dispose” model is crucial for achieving the UN Sustainable Development Goals (SDGs), especially given the environmental footprint of critical sectors like global healthcare. This systematic review, adhering to PRISMA guidelines and PICO framework, appraises challenges, solutions, and impacts of Circular Supply Chains (CSCs) and Reverse Logistics (RL) by synthesizing 26 high-quality articles published since 2010. Findings reveal pervasive, multidimensional barriers across technical (e.g., waste stream complexity, scalability), economic (e.g., high start-up costs, weak incentives), policy (e.g., legislative fragmentation, infrastructure deficits in developing nations), and socio-cultural dimensions (e.g., poor social acceptance). Conversely, the review confirms technological innovation as a central enabler, with quantitative evidence showing significant operational gains: RFID in healthcare reduced lost items daily and decreased infectious waste, while PLA recycling yielded a 59.87% production increase and 22.87% cost reduction. The analysis highlights an urgent need for research expansion beyond the current focus on Europe and Asia to address substantial knowledge gaps in Latin American, African, and Middle Eastern contexts. In conclusion, effective CSC implementation necessitates strategic priorities including robust digital infrastructure investment, policy coherence, enhanced stakeholder engagement, and targeted financial support for SMEs to overcome systemic impediments and fully realize the economic and environmental benefits critical for global sustainability.