International Journal of Applied Operational Research

Leveling factors affecting the smart circular supply chain

K. Yakideh

Given the rapid population growth and limited resources, a circular supply chain is vital for the survival of an organization in a competitive environment. Because the demand is increasing with population growth, and if the supply chain is linear, we will eventually face a lot of waste and ecosystem destruction, in a world where changes are increasing rapidly and the culture of consumerism is being promoted, we are faced with a lot of new products every day in the face of this volume of sudden change, supply chain intelligence can significantly contribute to the circularization of the supply chain. The use of tools and equipment that have become available to us with the 4th Industrial Revolution facilitates a circular supply chain, Therefore, organizations must understand the dimensions and effective factors to achieve readiness in a smart circular supply chain. Few studies have been conducted in the country on the readiness of the smart circular supply chain. Therefore, in this study,26 sub-dimensions of smart circular supply chain readiness were considered. The purpose of this research is to create a new theoretical perspective for categorizing the sub-dimensions of the readiness of the Smart circular supply chain by using the opinion of experts to look at the root of the problem with a different perspective. Finally, a cognitive map of the mental image of the experts was drawn and presented by integrating the agreement of the experts' opinions into a new category at three levels.

Evaluating performance of national oil and gas production facilities: A fuzzy network approach to manage undesirable outputs

M. Taleghani

Exploitation centers are pivotal in multiple industries, especially in the oil and gas sector, contributing significantly to national revenue through exports. The oil and gas extracted are vital to many industrial sectors and end consumers. However, heavy crude oil exploitation and refining operations have undergone substantial changes to meet market demands and comply with environmental regulations. This paper presents a fuzzy network model designed to assess the efficiency of the country's oil and gas exploitation centers, considering undesirable outputs and weak disposability, focusing specifically on the oil exploitation centers of Khuzestan province. Network data envelopment analysis was employed to evaluate the efficiency of these centers, with toxic gases such as CO2 and SO2 identified as undesirable outputs at each stage. The analysis of nine centers revealed that none achieved an efficiency score of one. The primary reasons for this inefficiency were due to the use of outdated equipment resulting from sanctions and the failure to use liquefied and natural gases instead of diesel and gasoline in the machinery used for exploiting and refining crude oil. The model was then extended to the oil exploitation centers of Khuzestan province as a case study, validating its functionality. The results demonstrated the model's ability to effectively evaluate the efficiency of current units. Based on these findings, the adoption of renewable energy and the installation of appropriate filters in the equipment were suggested.

Evaluating national energy efficiency using hybrid DEA-Cross efficiency and machine learning models

O. Valizade

Energy efficiency is critical for the attainment of sustainable development, as it optimizes resource utilization and reduces environmental impacts. This study evaluates the energy efficiency of 28 countries from 1995 to 2021 using a hybrid methodology, Data Envelopment Analysis (DEA)-Cross-Efficiency and machine learning models. DEA was utilized to compute efficiency scores by analyzing inputs including population and total energy consumption, with output such as total energy production. The scores underwent additional analysis employing six machine learning models: LightGBM, XGBoost, KNN, Random Forest, Decision Tree, and SVR. This approach aimed to reveal intricate relationships between the inputs and efficiency ratings, in addition to forecasting future efficiency trends. LightGBM demonstrated outstanding performance, achieving R² = 0.9820, MSE = 0.0008, and MAE = 0.0155. This performance can be attributed to its capacity to manage large datasets, optimize memory utilization, and implement sophisticated tree-based algorithms for precise predictions. Analysis of feature importance indicated that gas and coal production per capita are significant factors influencing energy efficiency. The findings offer policymakers practical insights for optimizing resources and highlight the effectiveness of machine learning in improving conventional efficiency evaluations. In the assessment of the countries, Australia and Canada exhibited the highest energy efficiency scores, indicative of their proficient resource management and energy policies. These insights provide a framework for other nations to implement comparable strategies aimed at enhancing energy efficiency and fostering sustainable development.

Identification and prioritization of factors influencing the purchase of insurance portfolios with AHP approach

K. Shahroudi

The main objective of this research is to identify and prioritize the factors influencing the purchase of insurance portfolios using the AHP approach. The research is applied and descriptive in nature, and the data were collected in Excel tables and are quantitative. The data used in this study were obtained from the Cooperative Insurance Company, related to the purchases of 33,671 customers. The nature of this research is data-driven, and the main basis of the research is to discover knowledge from the database of the Cooperative Insurance Company. In this study, transactions related to Cooperative Insurance customers were examined. After identifying the factors influencing the purchase of insurance portfolios, these factors were validated by 15 experts using the Delphi technique and then prioritized using Expert Choice software. The findings indicated that delay, frequency, monetary value, and customer relationship length are effective in examining the behavior of policyholders. It was determined that the criterion of monetary value, with a score of (0.473), holds the highest importance according to experts in weighting, ranking first, followed by frequency with a score of (0.216), delay with a score of (0.169), and customer relationship length with a score of (0.142) in fourth place.

Multi-criteria decision making for evaluating the performance of tourism service units

P. Niksefat

This study investigates the application of multi-criteria decision-making methods, namely the simple weighted method and the ranking technique based on Similarity is the ideal solution in the field of evaluating cultural tourism attractions. In the field of tourism, the use of multi-criteria analysis methods has not yet found a widespread practical position, while these methods have a great ability to rank options based on a set of objective and subjective criteria. The main goal of using these techniques is to facilitate strategic decision-making, prioritization, and solving complex problems in cultural and tourism planning. The results obtained from applying these methods show that these techniques have achieved almost similar results, which indicates their accuracy and reliability in the decision-making process. This alignment in the results has led to strengthening the validity of multi-criteria analysis models in the field of cultural tourism.

A new approach to marginal rate analysis in DEA with a focus on maintaining profitability

S. Masrouri

Analyzing the effects of marginal changes in input and output variables—referred to as throughputs on economic outcomes is a critical concern in both economic theory and practice. Marginal Rates (MR) play a key role in assessing the sensitivity of economic systems to such variations. This study enhances a recently introduced Data Envelopment Analysis (DEA) model, originally developed for profitability assessment, by incorporating marginal rate analysis within its framework. A binary-variable-based methodology is proposed to examine the marginal rates, enabling the simultaneous evaluation of how minor variations in one throughput affect others. By leveraging the concept of profitability in DEA, the proposed approach provides a comprehensive explanation of how decision-making units (DMUs) attain and sustain profitability. The proposed Mixed MR model offers a robust analytical tool for examining the interdependencies between performance indicators within efficient units. An empirical application involving branches of an Iranian bank demonstrates the effectiveness of the method in revealing the influence of individual indicators on one another in efficient operational contexts.