الگوریتمهای تکاملی برای مسئله مکانیابی تخصیص زنجیره تأمین زیست متان | ||
| چشمانداز مدیریت صنعتی | ||
| مقاله 2، دوره 6، شماره 3 - شماره پیاپی 23، آذر 1395، صفحه 29-54 اصل مقاله (996.57 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| نویسندگان | ||
| سهیلا خویشتندار* 1؛ مصطفی زندیه2؛ بهروز دری نوکرانی3؛ سیدامید رعنایی سیادت4 | ||
| 1دانشجوی دکتری، دانشگاه شهید بهشتی. | ||
| 2دانشیار، دانشگاه شهید بهشتی. | ||
| 3استاد، دانشگاه شهید بهشتی. | ||
| 4استادیار، دانشگاه شهید بهشتی. | ||
| چکیده | ||
| زیستمتان بهعنوان یک منبع تجدیدپذیر و سازگار با محیطزیست، نقش مهمی در تأمین انرژی مطابق با معیارهای توسعه پایدار ایفا میکند. در این مقاله، مدل برنامهریزی مختلط عدد صحیح غیرخطی برای تصمیمگیری در مورد مکان بهینه رآکتور تولید زیستمتان و چگونگی تخصیص انواع پسماند بهعنوان ماده اولیه به رآکتور مطالعه میشود. در این مدل هزینه کل راهاندازی سیستم تولید زیستمتان، با درنظرگرفتن محدودیتهای مقدار عرضه انواع پسماند در هر مرکز جمعآوری، مقدار تقاضای انواع پسماند در رآکتور و تعداد نیروی کار موجود، حداقل میشود. الگوریتمهای ژنتیک و تکامل تفاضلی برای حل مدل، توسعه داده شدهاند و نتایج آنها با یکدیگر مقایسه شده است. زمان حل هر دو الگوریتم نسبتاً یکسان است و الگوریتم تکامل تفاضلی، عملکرد بهتری از نظر مقدار تابع هدف نشان میدهد. | ||
| کلیدواژهها | ||
| زنجیره تأمین زیست متان؛ مکان یابی؛ تخصیص؛ برنامه ریزی عدد صحیح مختلط غیرخطی؛ الگوریتم های تکاملی | ||
| عنوان مقاله [English] | ||
| Evolutionary Algorithms for Location Allocation Biomethane Supply Chain Problem | ||
| نویسندگان [English] | ||
| Sohaila Khishtandar1؛ Mostafa Zandieh2؛ Behrooz Dorri Nokarani3؛ Sayed Omid Ranaei Siadat4 | ||
| 1PhD Student, Shahid Beheshti University. | ||
| 2Associate Professor, Shahid Beheshti University. | ||
| 3Professor, Shahid Beheshti University. | ||
| 4Assistant Professor, Shahid Beheshti University. | ||
| چکیده [English] | ||
| As an environment-friendly and renewable energy source, biomethane plays a significant role in the supply of sustainable energy. To determine location of reactor and allocate feedstocks, to the reactor in a biomethane production system by minimizing the supply chain cost, a mathematical model is studied in this article. Constraints, such as the limited workforce, the reactors’ demand on the residues, and the deterioration of the residues in the hubs are considered. Two evolutionary Algorithm, Genetic and differential evolutionary algorithms for solving mixed integer nonlinear programming model is proposed. The speed of obtaining the solution is the same but differential evolutionary algorithm finds better solutions than Genetic Algorithm when applied to the given problems. | ||
| کلیدواژهها [English] | ||
| Biomethane Supply Chain, Facility Location, Allocation, Mixed Integer Nonlinear Programming Model, Evolutionary Algorithms | ||
| مراجع | ||
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1. Akbari, M., Dorri, B., & Zandieh, M. (2012). Schedueling working shifts for multi-skilled workforces with genetic algorithm approach, Journal of industrial management perspective, 7, 87-102, In Persian 2. Alem Tabriz, A., Roghanian, E., & Hosseinzadeh, M. (2011). An optimization model for reverse logistics network under stochastic environment using genetic algorithm, Journal of industrial management perspective, 1, 61-89, In Persian 3. Berenson, M., Levine, D., Szabat, K. A., & Krehbiel, T. C. (2012). Basic business statistics: Concepts and applications: Pearson Higher Education AU. 4. Biogas/Biomethane for use as a transport fuel. (2016, May 4). Accessed in May 13, 2016, European Biofuels Technology Platform, Available from: http://biofuelstp.eu/biogas.html 5. Celli, G., Ghiani, E., Loddo, M., Pilo, F., & Pani, S. (2008). Optimal location of biogas and biomass generation plants. Paper presented at the Universities Power Engineering Conference. 6. Chang, Y.-H. (2010). Adopting co-evolution and constraint-satisfaction concept on genetic algorithms to solve supply chain network design problems. Expert Systems with Applications, 37(10), 6919-6930. 7. Cochran, W. G., & Cox, G. M. (1957). Experimental designs, 2, Oxford, England: John Wiley & Sons. 8. De Meyer, A., Cattrysse, D., Rasinmäki, J., & Van Orshoven, J. (2014). Methods to optimise the design and management of biomass-for-bioenergy supply chains: A review. Renewable and Sustainable Energy Reviews, 31, 657-670. 9. Deb, K. (2000). An efficient constraint handling method for genetic algorithms. Computer Methods in Applied Mechanics and Engineering, 186(2–4), 311-338. 10. Feoktistov, V. (2006). Differential evolution, 5. New York: Springer. 11. Franco, C., Bojesen, M., Hougaard, J. L., & Nielsen, K. (2015). A fuzzy approach to a multiple criteria and Geographical Information System for decision support on suitable locations for biogas plants. Applied Energy, 140, 304-315. 12. Ghojavand, H., Zandieh, M., & Dorri, B. (2011). Meta-heuristic algorithms for integrated logistics network model, Journal of industrial management perspective, 3, 99-119, In Persian 13. Höhn, J., Lehtonen, E., Rasi, S., & Rintala, J. (2014). A Geographical Information System (GIS) based methodology for determination of potential biomasses and sites for biogas plants in southern Finland. Applied Energy, 113, 1-10. 14. Kannan, G., Sasikumar, P., & Devika, K. (2010). A genetic algorithm approach for solving a closed loop supply chain model: A case of battery recycling. Applied Mathematical Modelling, 34(3), 655-670. 15. Khuri, A. I., & Mukhopadhyay, S. (2010). Response surface methodology. Wiley Interdisciplinary Reviews: Computational Statistics, 2(2), 128-149. 16. Kwietniewska, E., & Tys, J. (2014). Process characteristics, inhibition factors and methane yields of anaerobic digestion process, with particular focus on microalgal biomass fermentation. Renewable and Sustainable Energy Reviews, 34, 491-500. 17. Majumdar, A., & Ghosh, D. (2015). Genetic Algorithm Parameter Optimization using Taguchi Robust Design for Multi-response Optimization of Experimental and Historical Data. International Journal of Computer Applications, 127(5), 26-32. 18. Mayerle, S. F., & Neiva de Figueiredo, J. (2016). Designing optimal supply chains for anaerobic bio-digestion/energy generation complexes with distributed small farm feedstock sourcing. Renewable Energy, 90, 46-54. 19. Sawatdeenarunat, C., Surendra, K. C., Takara, D., Oechsner, H., & Khanal, S. K. (2015). Anaerobic digestion of lignocellulosic biomass: Challenges and opportunities. Bioresource Technology, 178, 178-186. 20. Sharma, B., Ingalls, R. G., Jones, C. L., & Khanchi, A. (2013). Biomass supply chain design and analysis: Basis, overview, modeling, challenges, and future. Renewable and Sustainable Energy Reviews, 24, 608-627. 21. Sheskin, D. J. (2003). Handbook of parametric and nonparametric statistical procedures. Boka raton: crc Press. 22. Storn, R., & Price, K. (1997). Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces. Journal of Global Optimization, 11(4), 341-359. 23. Taguchi, G. (1986). Introduction to quality engineering: designing quality into products and processes. Tokyo, Japan: White Plains: Asian Productivity Organization/UNIPUB. 24. Talbi, E.-G. (2009). Metaheuristics: from design to implementation, 74: John Wiley & Sons. 25. Wu, B., Sarker, B. R., & Paudel, K. P. (2015). Sustainable energy from biomass: Biomethane manufacturing plant location and distribution problem. Applied Energy, 158, 597-608. 26. Yılmaz Balaman, Ş., & Selim, H. (2014). A network design model for biomass to energy supply chains with anaerobic digestion systems. Applied Energy, 130, 289-304. 27. Yue, D., You, F., & Snyder, S. W. (2014). Biomass-to-bioenergy and biofuel supply chain optimization: Overview, key issues and challenges. Computers & Chemical Engineering, 66, 36-56. | ||
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