مدل ریاضی انتخاب قرارداد و تعیین میزان خرید گاز طبیعی مایع با در نظر گرفتن تخفیف کلی و نموی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مدیریت صنعتی،واحد تهران جنوب، دانشگاه آزاد اسلامی،تهران، ایران

2 گروه مهندسی صنایع،واحد فیروزکوه،دانشگاه آزاد اسلامی، فیروزکوه، ایران

3 گروه مهندسی صنایع،واحد تهران جنوب،دانشگاه آزاد اسلامی، تهران، ایران

4 گروه مدیریت صنعتی،واحد تهران جنوب،دانشگاه آزاد اسلامی، تهران، ایران

چکیده

هدف از این تحقیق کمک به خریداران گاز طبیعی مایع جهت انتخاب قراردادهای مختلف و سطح میزان خرید در دورههای زمانی مختلف میباشد. برخی از این قراردادها دارای تخفیف کلی و برخی نموی هستند. عوامل مهم در قراردادها نرخ تبخیر، کیفیت و هزینههای عملیاتی میباشند. با استفاده از تکنیک لینمپ وزن و بهترین سطح هر یک از عوامل فوق تعیین میشود. سپس با یک مدل ریاضی عدد صحیح مختلط، انتخاب قرارداد، میزان بهینه خرید از هر قرارداد و نوع تخفیف استفاده شده در هر دوره با درنظر گرفتن میزان خرید از بازار فعلی توسط خریدار، مشخص میگردد. مدل عددی بر اساس دادههای واقعی بوده و مدل ریاضی بر اساس نرمافزار گمز حل شده است. نتایج نشان می دهد که از 12 قرارداد پیشنهادی به خریدار، فقط 5 قرارداد انتخاب شده است که 2 قرارداد از تخفیف نموی و 3 قرارداد از تخفیف کلی استفاده کردهاند.

کلیدواژه‌ها


عنوان مقاله [English]

Mathematical model of contract selection and determining the amount of liquefied natural gas purchase taking into account the general and developmental discount

نویسندگان [English]

  • Amir Karbasi Yazdi 1
  • Alireza rashidi 2
  • Sedigh raissi 3
  • mahmood modiri 4
1 Department of Industrial Management, South Tehran Branch, Islamic Azad University, Tehran, Iran
2 Department of Industrial Engineering, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran
3 Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
4 Department of Industrial Management, South Tehran Branch, Islamic Azad University, Tehran, Iran
چکیده [English]

The purpose of this study is to help buyers of liquefied natural gas to select different contracts and the level of purchase in different time periods. Some of these contracts have a general discount and some have a promotion. Important factors in contracts are evaporation rate, quality and operating costs. Using Linamp technique, the weight and the best level of each of the above factors are determined. Then, with a mathematical model of mixed integer, the contract selection, the optimal purchase amount of each contract and the type of discount used in each period are determined by considering the purchase amount from the current market by the buyer. The numerical model is based on real data and the mathematical model is solved based on Gomez software. The results show that out of 12 contracts offered to the buyer, only 5 contracts have been selected, of which 2 contracts have used the development discount and 3 contracts have used the general discount.

کلیدواژه‌ها [English]

  • Liquefied natural gas
  • Linamp method
  • Contract selection
  • Discount model
 [1]. Conti, J. J., Holtberg, P. D., Beamon, J. A., Schaal, A. M., Ayoub, J. C., & Turnure, J. T. (2014). Annual energy outlook 2014. US Energy Information Administration
[2]. Conti, J., Holtberg, P., Doman, L. E., Smith, K. A., Sullivan, J. O., Vincent, K. R., … Kearney, D. R. (2011). International energy outlook 2011. US Energy Information Administration, Technical Report No. DOE/EIA-0484.
[3]. Songhurst, B. (2014). LNG plant cost escalation.Oxford Institute for Energy Studies. Oxford University
[4]. Valentine, S. V. (2011). Emerging symbiosis: Renewable energy and energy security. Renewable and Sustainable Energy Reviews, 15(9), 4572–4578.
[5]. Goldthau, A., & Boersma, T. (2014). The 2014 Ukraine-Russia crisis: Implications for energy markets and scholarship. Energy Research & Social Science, 3, 13–15.
[6]. Honore, A. (2011). European natural gas demand, supply, and pricing: cycles, seasons, and the impact of LNG price arbitrage. OUP Catalogue.
[7]. Rühl, C. (2010). Global energy after the crisis: Prospects and priorities. Foreign Affairs, 63–75.
[8]. Stern, J. (2014). International gas pricing in Europe and Asia: A crisis of fundamentals. Energy Policy, 64, 43–48.
[9]. Khalilpour, R., & Karimi, I. A. (2012). Contract selection under uncertainty. LNG buyers' perspective. Proceedings of the 11th International Symposium on Process Systems Engineering, 15-19
[10]. Calfa, B. A., & Grossmann, I. E. (2015). Optimal procurement contract selection with price optimization under uncertainty for process networks. Computers & Chemical Engineering, 82, 330–343.
[11]. Guigues, V., Sagastizábal, C., & Zubelli, J. P. (2014). Robust management and pricing of liquefied natural gas contracts with cancelation options. Journal of Optimization Theory and Applications, 161(1), 179-198. 
[12]. Maxwell, D., & Zhu, Z. (2011). Natural gas prices, LNG transport costs, and the dynamics of LNG imports. Energy Economics, 33(2), 217-226. 
[13]. Khalilpour, R., & Karimi, I. A. (2011). Selection of liquefied natural gas (LNG) contracts for minimizing procurement cost. Industrial & engineering chemistry research, 50(17), 10298-10312.
[14]. Ruester, S. (2009). Changing contract structures in the international liquefied natural gas market: a first empirical analysis. Revue d'économie industrielle, (127), 89-112.
[15]. Von Hirschhausen, C., & Neumann, A. (2008). Long-term contracts and asset specificity revisited: An empirical analysis of producer–importer relations in the natural gas industry. Review of Industrial Organization, 32(2), 131-143.
[16]. Gärttner, J., Flath, C. M., & Weinhardt, C. (2018). Portfolio and contract design for demand response resources. European Journal of Operational Research, 266(1), 340-353.
[17]. Qin, Q., Liang, F., Li, L., & Wei, Y. M. (2017). Selection of energy performance contracting business models: A behavioral decision-making approach. Renewable and Sustainable Energy Reviews, 72, 422-433
[18]. Hu, K.-J., & Vincent, F. Y. (2016). An integrated approach for the electronic contract manufacturer selection problem. Omega, 62, 68–81.
[19]. Oliveira, F. S., Ruiz, C., & Conejo, A. J. (2013). Contract design and supply chain coordination in the electricity industry. European Journal of Operational Research, 227(3), 527–537.
[20]. Bonnans, J. F., Cen, Z., & Christel, T. (2012). Energy contracts management by stochastic programming techniques. Annals of Operations Research, 1–24.
[21]. Gao, X. N., & Tian, J. (2018). Multi-period incentive contract design in the agent emergency supplies reservation strategy with asymmetric information. Computers & Industrial Engineering, 120, 94-102
[22]. Zheng, B., Yang, C., Yang, J., & Zhang, M. (2017). Pricing, collecting and contract design in a reverse supply chain with incomplete information. Computers & Industrial Engineering, 111, 109-122
[23]. Cai, J., Hu, X., Tadikamalla, P. R., & Shang, J. (2017). Flexible contract design for VMI supply chain with service-sensitive demand: Revenue-sharing and supplier subsidy. European Journal of Operational Research, 261(1), 143-153.‏
[24]. Sluis, S., & De Giovanni, P. (2016). The selection of contracts in supply chains: An empirical analysis. Journal of Operations Management, 41, 1–11.
[25]. Saha, A., Kar, S., & Maiti, M. (2015). Multi-item fuzzy-stochastic supply chain models for long-term contracts with a profit sharing scheme. Applied Mathematical Modelling, 39(10), 2815–2828.
[26]. Vlachos, I. (2013). Designing effective contracts within the buyer-seller context: a DEMATEL and ANP study.
[27]. Perrigne, I., & Vuong, Q. (2011). Nonparametric identification of a contract model with adverse selection and moral hazard. Econometrica, 79(5), 1499–1539.
[28]. Talluri, S., & Lee, J. Y. (2010). Optimal supply contract selection. International Journal of Production Research, 48(24), 7303–7320.
[29]. Palanisamy, R., Verville, J., & Taskin, N. (2015). The critical success factors (CSFs) for Enterprise Software contract negotiations: An empirical analysis. Journal of Enterprise Information Management, 28(1), 34–59.
[30]. Whyte, A., & Macpherson, E. (2011). Standard Forms of Contract Selection Criteria: A Qualitative Analysis of the Western Australian Construction Industry. In RICS Construction and Property Conference (p. 651).
[31]. Srinivasan, V., & Shocker, A. D. (1973). Linear programming techniques for multidimensional analysis of preferences. Psychometrika, 38(3), 337–369.
[32].اصغر پور ،م.1387. تصمیم گیری های چند معیاره. انتشارات دانشگاه تهران .ص253
[33]. Songhurst, B. (2014). LNG plant cost escalation.Oxford Institute for Energy Studies. Oxford University
[34]. Dobrota, Đ., Lalić, B., & Komar, I. (2013). Problem of boil-off in LNG supply chain. Transactions on maritime science, 2(02), 91-100
[35]. Chiu, C., Choi, T., & Tang, C. S. (2011). Price, Rebate, and Returns Supply Contracts for Coordinating Supply Chains with Price‐Dependent Demands. Production and Operations Management, 20(1), 81–91.
[36]. Hu, K.-J., & Vincent, F. Y. (2016). An integrated approach for the electronic contract manufacturer selection problem. Omega, 62, 68–81.
[37]. Unsihuay-Vila, C., Marangon-Lima, J. W., de Souza, A. C. Z., Perez-Arriaga, I. J., & Balestrassi, P. P. (2010). A model to long-term, multiarea, multistage, and integrated expansion planning of electricity and natural gas systems. IEEE Transactions on Power Systems, 25(2), 1154–1168.
[38]. Wang, W. (2010). A model for maintenance service contract design, negotiation and optimization. European Journal of Operational Research, 201(1), 239–246.
[39]. Yoo, S. H., Kim, D., & Park, M.-S. (2015). Pricing and return policy under various supply contracts in a closed-loop supply chain. International Journal of Production Research, 53(1), 106–126
[40]. Wuchang, W., Yuxing, L., Fafeng, S., & Leilei, Z. (2010). Controlling factors of internal pressure and evaporation rate in a huge LNG storage tank [J]. Natural Gas Industry, 7, 033.