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All Journal International Journal of Financial, Accounting, and Management Annals of Management and Organization Research
Abbasi, Morteza
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Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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Optimum estimation and forecasting of gasoline consumption in Iran's national oil refining and distribution company Bazyar, Afshar; Abbasi, Morteza
International Journal of Financial, Accounting, and Management Vol. 6 No. 4 (2025): March
Publisher : Goodwood Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/ijfam.v6i4.2492

Abstract

Purpose: This paper presents an accurate estimation and forecasting of gasoline consumption. This is vital for the policy and decision-making process in the energy sector. Method: A hybrid data-driven model based on Artificial Neural Network (ANN) and an autoregressive integrated moving average (ARIMA) approach was developed for optimum estimation and forecasting of gasoline consumption. The proposed hybrid ARIMA-ANN approach considers six lagged variables and one forecasted value provided by the ARIMA process. The ANN trains and tests data with a multi-layer perceptron (MLP) approach, which has the lowest Mean Absolute Percentage Error (MAPE). To show the applicability and superiority of the proposed hybrid approach, daily available data were collected for 7 years (2015–2021) in Iran. Results: The acquired results show a high accuracy of about 94.27%  using the proposed hybrid ARIMA-ANN method. The results of the proposed model are compared with respect to the regression models and the ARIMA process. Conclusions: Analyzing consumption patterns can provide insights into consumer behavior, enabling NIORDC to tailor its services and marketing strategies more effectively. Limitations: Eliminating subsidies from gasoline prices has led to the appearance of noisy data in gasoline consumption in Iran's National Oil Refining and Distribution Company.   Contribution: The outcome of this paper justifies the capability of the proposed hybrid ARIMA-ANN approach in accurate forecasting of gasoline consumption.
Multi-objective planning for a multi-echelon supply chain using parameter-tuned meta-heuristics Bazyar, Afshar; Abbasi, Morteza
Annals of Management and Organization Research Vol. 7 No. 1 (2025): August
Publisher : goodwood publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/amor.v7i1.2542

Abstract

Purpose: This study presents a tri-objective model for the integrated planning of production and distribution within a multi-level supply chain network that encompasses multiple product types and time periods. Research methodology: The supply chain network includes manufacturer plants (MPs), distribution centers (DCs), retailers, and final customers. The proposed model aims to minimize total supply chain costs, ensure timely delivery of products to customers, and reduce the lost demand rate. Classified as a linear integer programming problem, which is NP-Hard, the model’s complexity is addressed using two multi-objective meta-heuristic approaches based on the Pareto method: the Non-Dominated Sorting Genetic Algorithm (NSGA-II) and the Non-Dominated Ranking Genetic Algorithm (NRGA). The Taguchi method is employed to optimize the input parameters of these algorithms. Results: The performance of the proposed solution methods is evaluated through various test problems of different dimensions. Statistical analyses confirm the effectiveness and reliability of both algorithms in achieving the defined objectives. Conclusions: The findings highlight that multi-objective meta-heuristic approaches, when parameter-tuned appropriately, provide efficient and practical solutions for integrated supply chain planning, offering a balance among cost, service level, and demand fulfillment. Limitations: The study acknowledges the inherent complexity of the problem and the dependency of meta-heuristic outputs on parameter settings, which may influence solution robustness. Contribution: This research contributes to the literature by providing a robust framework for optimizing production and distribution in complex supply chain networks, delivering insights into the application of advanced algorithmic strategies in operational planning.
Multi-objective planning for a multi-echelon supply chain using parameter-tuned meta-heuristics Bazyar, Afshar; Abbasi, Morteza
Annals of Management and Organization Research Vol. 7 No. 1 (2025): August
Publisher : goodwood publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/amor.v7i1.2542

Abstract

Purpose: This study presents a tri-objective model for the integrated planning of production and distribution within a multi-level supply chain network that encompasses multiple product types and time periods. Research methodology: The supply chain network includes manufacturer plants (MPs), distribution centers (DCs), retailers, and final customers. The proposed model aims to minimize total supply chain costs, ensure timely delivery of products to customers, and reduce the lost demand rate. Classified as a linear integer programming problem, which is NP-Hard, the model’s complexity is addressed using two multi-objective meta-heuristic approaches based on the Pareto method: the Non-Dominated Sorting Genetic Algorithm (NSGA-II) and the Non-Dominated Ranking Genetic Algorithm (NRGA). The Taguchi method is employed to optimize the input parameters of these algorithms. Results: The performance of the proposed solution methods is evaluated through various test problems of different dimensions. Statistical analyses confirm the effectiveness and reliability of both algorithms in achieving the defined objectives. Conclusions: The findings highlight that multi-objective meta-heuristic approaches, when parameter-tuned appropriately, provide efficient and practical solutions for integrated supply chain planning, offering a balance among cost, service level, and demand fulfillment. Limitations: The study acknowledges the inherent complexity of the problem and the dependency of meta-heuristic outputs on parameter settings, which may influence solution robustness. Contribution: This research contributes to the literature by providing a robust framework for optimizing production and distribution in complex supply chain networks, delivering insights into the application of advanced algorithmic strategies in operational planning.
Co-Authors Bazyar, Afshar