A Switchgrass-based Bioethanol Supply Chain Network Design Model under Auto-Regressive Moving Average Demand
Publish Year: 1395
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:
JR_JREE-3-3_001
تاریخ نمایه سازی: 10 آبان 1402
Abstract:
Switchgrass is known as one of the best second-generation lignocellulosic biomasses for bioethanol production. Designing efficient switchgrass-based bioethanol supply chain (SBSC) is an essential requirement for commercializing the bioethanol production from switchgrass. This paper presents a mixed integer linear programming (MILP) model to design SBSC in which bioethanol demand is under auto-regressive moving average (ARMA) time series models. In this paper, how a SBSC design is affected by ARMA time series structure of bioethanol demand is studied. A case study based on North Dakota state in the United States demonstrates application of the proposed approach in designing the optimal SBSC. Moreover, SBSC optimal design is forecasted for the time horizon of ۲۰۱۳ to ۲۰۲۰ with the bioethanol demand acquired from the ARMA models to provide insights for designing and minimizing total cost of SBSC in the future efficiently. Finally, in order to validate the proposed approach, a reproduction behavior test is done. Also, a comparative analysis based on a SBSCND model from the recent literature is elaborated to show the performance of the proposed approach.
Keywords:
switchgrass , bioethanol supply chain , network design , mixed integer linear programming , Auto-Regressive Moving Average Time Series
Authors
Hamid Ghaderi
School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
Mona Asadi
School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
Saeed Shavalpour
School of Progress Engineering, Iran University of Science and Technology, Tehran, Iran
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