بهینه سازی شبکه زنجیره تامین زیست توده در چهار سطح
محورهای موضوعی :
مدیریت صنعتی
Davod Dehghan
1
,
Kiamars Fathi Hafshejani
2
,
Jalal Haghighat Monfared
3
1 - Department of Industrial Management, Qom Branch, Islamic Azad University, Qom, Iran
2 - Assistant Professor, Department of Industrial Management, South Tehran Branch, Islamic Azad University, Tehran, Iran
3 - Assistant Professor, Department of Industrial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran
تاریخ دریافت : 1402/03/17
تاریخ پذیرش : 1402/06/06
تاریخ انتشار : 1402/07/01
کلید واژه:
"شبکه زنجیره تامین زیست توده" ,
"الگوریتم ژنتیک",
"خروجیهای مطلوب و نامطلوب",
"الگوریتم MOPSO",
چکیده مقاله :
آلایندگی بر اثر دفن زیست توده ها، امکان تولید انرژی پاک از زیست تودهها و تقاضای زیاد برای دریافت انرژی، بهینه سازی شبکه زنجیره تامین زیست توده را مهم و ضروری ساخته است. هدف این مقاله، بهینه سازی شبکه زنجیره تامین زیست توده در چهار سطح به منظور کاهش هزینه های اقتصادی و زیست محیطی است. مهم ترین شکاف پژوهشی برطرف شده در این مقاله، تعیین خروجیهای مطلوب و نامطلوب زیست توده ها در مراکز تفکیک سازی و در نظرگرفتن حالت چند دوره ای، چندمحصولی با وسایل حمل و نقل ناهمگن است. مدل پژوهش، دو هدفه برنامهریزی خطی عدد صحیح مختلط با عدم قطعیت و اختلال است بدین منظور چهار سناریو، طراحی گردید. مدل با روش الگوریتم ژنتیک و MOPSO و با نرم افزار پایتون حل گردید.اعتبارسنجی مدل، در یک مورد مطالعه ای واقعی در استان فارس بررسی شده است. مدل پیشنهادی توانسته است پایداری و تاب آوری را همزمان، پیاده سازی نماید که موجب کاهش هزینه ها، کاهش انتشار کربن و افزایش تجاری شدن تولید انرژی از زیست توده ها شده است از اینرو سبب افزایش تمایل سرمایه گذاران به سرمایه گذاری در این شبکه از زنجیره تامین میشود مدل پیشنهادی میزان تولید انرژی را 1/2 درصد نسبت به زمانی که خروجیهای مطلوب و نامطلوب در نظر گرفته نشوند کمتر تشان می دهد یعنی به واقعیت بسیار نزدیکتر می کند. با انجام تحلیل حساسیت بر روی داده های واقعی، کارایی مدل اثبات گردید.
چکیده انگلیسی:
Pollution due to biomass burial, the possibility of producing clean energy from biomass and the high demand for energy have made the optimization of the biomass supply chain network important and necessary. The purpose of this article is to optimize biomass supply chain network at four levels in order to reduce economic and environmental costs. The most important gap in research, resolved in this article, is the determination of the desirable and undesirable outputs of the masses in the centers. Separating and considering the multi-period, multi-product mode with heterogeneous transport means. The research model is a two-objective linear programming of a correct number mixed with uncertainty and disturbance, four scenarios were designed for this purpose. The model was solved with genetic algorithm and MOPSO method and with Python software. Validation of the model was investigated in a real case study in Fars province has been The proposed model has been able to implement sustainability and resilience at the same time, which has reduced costs, reduced carbon emissions, and increased the commercialization of energy production from biomass, thus increasing the willingness of investors to invest in this network. It is supplied from the supply chain. The proposed model makes the amount of energy production 2.1% lower than when the favorable and unfavorable outputs are not considered, which means it is much closer to reality. By performing sensitivity analysis on real data, the efficiency of the model was proved
منابع و مأخذ:
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Roeva, O., & Chorukova, E. (2022). Metaheuristic Algorithms to Optimal Parameters Estimation of a Model of Two-Stage Anaerobic Digestion of Corn Steep Liquor. Applied Sciences, 13(1), 199.
Saghaei, M., Ghaderi, H., & Soleimani, H. (2020). Design and optimization of biomass electricity supply chain with uncertainty in material quality, availability and market demand. Energy, 197, 117165.
Toba, A. L., Paudel, R., Lin, Y., Mendadhala, R. V., & Hartley, D. S. (2023). Integrated Land Suitability Assessment for Depots Siting in a Sustainable Biomass Supply Chain. Sensors, 23(5), 2421.
Veland, S., Howitt, R., Dominey-Howes, D., Thomalla, F., & Houston, D. (2013). Procedural vulnerability: Understanding environmental change in a remote indigenous community. Global Environmental Change, 23(1), 314-326.
Yahya, N. S. M., Ng, L. Y., & Andiappan, V. (2021). Optimisation and planning of biomass supply chain for new and existing power plants based on carbon reduction targets. Energy, 237, 121488.
Zarei, M., Shams, M. H., Niaz, H., Won, W., Lee, C. J., & Liu, J. J. (2022). Risk-based multistage stochastic mixed-integer optimization for biofuel supply chain management under multiple uncertainties. Renewable Energy, 200, 694-705.
Zeinodin Zadeh, S., Amiri, M., Olfat, L., & Pishvaee, M. S. (2023). Modelling a routing-location-inventory problem in the sustainable poultry and livestock medicine supply chain under uncertainty, considering discount. Production and Operations Management, 14(1), 39-64.
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Abdi, A., & Hajiaghaei-Keshteli, M. (2019). Multi-Objective Closed-loop Supply Chain Considering Vehicles and Solving by New Approaches in Metaheuristics. Journal of Modeling in Engineering, 17(59), 67-85.
Akdogan, A. A., & Demirtas, O. (2014). Managerial role in strategic supply chain management. Procedia-Social and Behavioral Sciences, 150, 1020-1029.
Al-Hakimi, M. A., & Borade, D. B. (2020). The impact of entrepreneurial orientation on the supply chain resilience. Cogent Business & Management, 7(1), 1847990.
Aranguren, M., Castillo-Villar, K. K., & Aboytes-Ojeda, M. (2021). A two-stage stochastic model for co-firing biomass supply chain networks. Journal of Cleaner Production, 319, 128582.
Christopher, M.and Peck, H. (2004), "Building the Resilient Supply Chain", The International Journal of Logistics Management,15 (2), 1-14. https://doi.org/10.1108/09574090410700275
Ebrahim Qazvini, Z., Haji, A., & Mina, H. (2021). A fuzzy solution approach to supplier selection and order allocation in green supply chain considering the location-routing problem. Scientia Iranica, 28(1), 446-464.
Ehmann, A., Thumm, U., & Lewandowski, I. (2018). Fertilizing potential of separated biogas digestates in annual and perennial biomass production systems. Frontiers in Sustainable Food Systems, 2, 12.
Ferronato, N., Alarcón, G. P. P., Lizarazu, E. G. G., & Torretta, V. (2021). Assessment of municipal solid waste collection in Bolivia: Perspectives for avoiding uncontrolled disposal and boosting waste recycling options. Resources, Conservation and Recycling, 167, 105234.
Fritz, M., Ruel, S., Kallmuenzer, A. & Harms, R. (2021). Sustainability Management in Supply Chain: The Role of Familiness. Technological Forecasting & Social Change, 173(2021)121078 https://doi.org/10.1016/j.techfore.2021.121078
Ghaderi, H., Pishvaee, M. S., & Moini, A. (2016). Biomass supply chain network design: An optimization-oriented review and analysis. Industrial crops and products, 94, 972-1000.
Gholami, A., Bonakdari, H., Ebtehaj, I., & Khodashenas, S. R. (2020). Reliability and sensitivity analysis of robust learning machine in prediction of bank profile morphology of threshold sand rivers. Measurement, 153, 107411.
Guo, C., Hu, H., Wang, S., Rodriguez, L. F., Ting, K. C., & Lin, T. (2022). Multiperiod stochastic programming for biomass supply chain design under spatiotemporal variability of feedstock supply. Renewable Energy, 186, 378-393.
Kamalahmadi, M., & Parast, M. M. (2016). A review of the literature on the principles of enterprise and supply chain resilience: Major findings and directions for future research. International journal of production economics, 171, 116-133.
Khalifehzadeh, S., Fakhrzad, M. B., Mehrjerdi, Y. Z., & Hosseini_Nasab, H. (2019). Two effective metaheuristic algorithms for solving a stochastic optimization model of a multi-echelon supply chain. Applied Soft Computing, 76, 545-563.
Lambert, Douglas M. (2008). A Global View of Supply Chain Management. University of Auckland Business Review, 10(2), 30-35.
Liu, X., Tian, G., Fathollahi-Fard, A. M., & Mojtahedi, M. (2020). Evaluation of ship’s green degree using a novel hybrid approach combining group fuzzy entropy and cloud technique for the order of preference by similarity to the ideal solution theory. Clean Technologies and Environmental Policy, 22, 493-512.
Lo, S. L. Y., How, B. S., Leong, W. D., Teng, S. Y., Rhamdhani, M. A., & Sunarso, J. (2021). Techno-economic analysis for biomass supply chain: A state-of-the-art review. Renewable and Sustainable Energy Reviews, 135, 110164.
Menesy, A. S., Sultan, H. M., Habiballah, I. O., Masrur, H., Khan, K. R., & Khalid, M. (2023). Optimal Configuration of a Hybrid Photovoltaic/Wind Turbine/Biomass/Hydro-Pumped Storage-Based Energy System Using a Heap-Based Optimization Algorithm. Energies, 16(9), 3648.
Mohammed, A., Harris, I., Soroka, A., & Nujoom, R. (2019). A hybrid MCDM-fuzzy multi-objective programming approach for a G-resilient supply chain network design. Computers & Industrial Engineering, 127, 297-312.
Mousavi Ahranjani, P., Ghaderi, S. F., Azadeh, A., & Babazadeh, R. (2020). Robust design of a sustainable and resilient bioethanol supply chain under operational and disruption risks. Clean technologies and environmental policy, 22, 119-151.
Nasiri, M. M., Mousavi, H., & Nosrati-Abarghooee, S. (2023). A green location-inventory-routing optimization model with simultaneous pickup and delivery under disruption risks. Decision Analytics Journal, 6, 100161.
Nilsson, F., & Göransson, M. (2021). Critical factors for the realization of sustainable supply chain innovations-model development based on a systematic literature review. Journal of Cleaner Production, 296, 126471.
Pakzad-Moghaddam, S. H., Mina, H., & Mostafazadeh, P. (2019). A novel optimization booster algorithm. Computers & Industrial Engineering, 136, 591-613.
Prado, A., Chiquier, S., Fajardy, M., & Mac Dowell, N. (2023). Assessing the impact of carbon dioxide removal on the power system. Iscience, 26(4).
Rasekh, A., Hamidzadeh, F., Sahebi, H., & Pishvaee, M. S. (2023). A sustainable network design of a hybrid biomass supply chain by considering the water–energy–carbon nexus. Energy Science & Engineering, 11(3), 1107-1132.
Roeva, O., & Chorukova, E. (2022). Metaheuristic Algorithms to Optimal Parameters Estimation of a Model of Two-Stage Anaerobic Digestion of Corn Steep Liquor. Applied Sciences, 13(1), 199.
Saghaei, M., Ghaderi, H., & Soleimani, H. (2020). Design and optimization of biomass electricity supply chain with uncertainty in material quality, availability and market demand. Energy, 197, 117165.
Toba, A. L., Paudel, R., Lin, Y., Mendadhala, R. V., & Hartley, D. S. (2023). Integrated Land Suitability Assessment for Depots Siting in a Sustainable Biomass Supply Chain. Sensors, 23(5), 2421.
Veland, S., Howitt, R., Dominey-Howes, D., Thomalla, F., & Houston, D. (2013). Procedural vulnerability: Understanding environmental change in a remote indigenous community. Global Environmental Change, 23(1), 314-326.
Yahya, N. S. M., Ng, L. Y., & Andiappan, V. (2021). Optimisation and planning of biomass supply chain for new and existing power plants based on carbon reduction targets. Energy, 237, 121488.
Zarei, M., Shams, M. H., Niaz, H., Won, W., Lee, C. J., & Liu, J. J. (2022). Risk-based multistage stochastic mixed-integer optimization for biofuel supply chain management under multiple uncertainties. Renewable Energy, 200, 694-705.
Zeinodin Zadeh, S., Amiri, M., Olfat, L., & Pishvaee, M. S. (2023). Modelling a routing-location-inventory problem in the sustainable poultry and livestock medicine supply chain under uncertainty, considering discount. Production and Operations Management, 14(1), 39-64.
