煉油產業的穩健生產規劃問題--以台灣中油股份有限公司為例

簡易檢索 / 詳目顯示

回結果列表

研究生：	鄭凱文
論文名稱：	煉油產業的穩健生產規劃問題--以台灣中油股份有限公司為例 Robust Production Planning of the Refinery Industry --With the Case of CPC Corporation, Taiwan
指導教授：	王小璠
口試委員:	溫于平巫木誠林義貴謝中奇
學位類別：	博士 Doctor
系所名稱：	工學院 - 工業工程與工程管理學系 Department of Industrial Engineering and Engineering Management
論文出版年：	2011
畢業學年度：	100
語文別：	英文
論文頁數：	123
中文關鍵詞：	煉油產業、連續生產週期系統、容忍度分析、模糊線性規劃、粒子群聚演算法、台灣中油
外文關鍵詞：	Refinery industries, Continuous production system, Tolerance analysis, Fuzzy linear programming(FLP), Particle Swarm Optimization, CPC Corporation, Taiwan
相關次數：	點閱：2 下載：0
分享至:	分享至facebook 分享至twitter

查詢本校圖書館目錄查詢臺灣博碩士論文知識加值系統勘誤回報

台灣油品市場在開放私營企業之後，煉油產業不僅已經進入一個完全自由市場，而且也面臨來自於全世界的競爭與挑戰。在這樣的一種環境裡，存在著許許多多不確定性的因素，然而這些不確定性的因素，如果不加以妥適規劃，將造成台灣整體能源供給之危機。因此，為了增加煉油產業的協調性與流暢性，如何節省生產成本、提升產能、減少存貨、促進運輸的流暢和利潤的提升是當前必然且重要的課題。
原油從油輪運送到港口，卸油至儲槽，再由儲槽輸送到煉油廠提煉，在一連串過程裡，產生了購油成本、運輸成本、儲存成本與生產成本等等，直到成品油輸送到市場販售時，才開始產生收益。本研究旨在探討煉油產業的生產成本、存貨成本，以及在一個連續的生產週期系統裡，油輪提早到達或延遲到達港口卸油時，對煉油廠連續生產流程所造成的影響。因此，如何妥善管控這些生產因素，一套兼具有敏感度與彈性的穩健生產規劃系統，是煉油產業所必須的。基於上述原因，本研究發展一個數學規劃模型，具有最小總生產、存貨和啟動成本的最佳生產架構，並以容忍度分析來處理這些不確定性的環境因素。另一方面，本研究亦基於模糊集合理論，提出一套模糊線性規劃(FLP)模型，將不確定性直接考慮在模式中，而非作求解後的分析，以對於不確定性的需求和成本提出一個最大化的利潤生產策略。
本研究將以台灣中油股份有限公司為例，當模型的規模很大時，將以柔性演算法來尋求最佳近似解。在眾多演算法中，特別是粒子群聚最佳化演算法(PSO)在搜尋最佳解時有相當好的表現。因此，本研究將分別以精確解與近似解就演算效率與精確度加以比較，提出最適解法以驗證本研究所提出的模型，俾能提供更有用、更精確的資訊，朝向在一個不確定因素的環境裡，發展有效控制成本和利潤極大化的煉油產業策略。

After the oil market in Taiwan has been opened to the private enterprise, the oil refinery industries in Taiwan have not only entered a completely free market, but also faced the challenge from world competition. In such an environment, there exist varieties of uncertain factors which have caused the failure of traditional production planning model. Therefore, to increase compatibility, it is essential to increase efficiency in the inventory, production, transportation and profit.
Refineries begin to gain revenues when their products are sold in the market. This research examines the possible costs of additional inventories or recurrent times of refineries derived from early or late shipment in a continuous production system. Meanwhile, we shall develop a responsive and flexible production planning system to cope with uncertain manufacturing factors. Then, based on the developed mathematical programming model, an optimal production structure with the minimum total cost of production, inventory and start-up is proposed. Tolerance analysis is conducted to cope with the uncertain environment. Furthermore, in order to clarify the uncertain demand and cost, Fuzzy Set Theory is adopted to develop a fuzzy linear programming (FLP) model, so that a maximal profit production strategy is proposed with highest degree of satisfaction.
Then, we would conduct a case study on CPC Corporation, Taiwan (CPC). For the purpose of more general application, the issues of large-scale cases have been taken into account so that if the model scale is too large, the corporation can use a heuristic approach to find the near optimal solutions. In this research, we shall adopt the soft computing technique, in particular, the Particle Swarm Optimization (PSO) algorithm, to propose time-effective solution procedure for the proposed models.
It is expected that the results of this research will be able to provide useful information towards developing cost and profit-effective oil refinery strategies in an uncertain environment.

中文摘要………………………………………………………………I
Abstract………………………………………………………………II
誌謝……………………………………………………………………III
Figure Captions……………………………………………………VII
Table Captions………………………………………………………VIII
Notations Index…………………………………………………………X
CHAPTER 1 INTRODUCTION………………………………………………1
1.1 Background and Motivation………………………………………1
1.2 Objectives…………………………………………………………2
1.3 Research Framework………………………………………………3
CHAPTER 2 LITERATURE REVIEW    ………………………………………4
2.1 Production Planning for Refinery Industry…………………4
2.2 Methodology…………………………………………………………6
2.2.1 Linear Programming Model……………………………………7
2.2.2 Mixed-Integer Linear Programming Model…………………9
2.2.3 Fuzzy Set Theory………………………………………………11
2.2.4 Fuzzy Linear Programming Model……………………………12
2.3 Solution Procedure………………………………………………14
2.3.1 Particle Swarm Optimization (PSO) Algorithm…………14
2.3.2 Penalty Function Approach (PFA)…………………………16
2.4 Summary and Conclusion…………………………………………17
CHAPTER 3 A DETERMINISTIC PRODUCTION MODEL FOR
REFINERY INDUSTRY WITH MINIMUM COST……………………………19
3.1 Production Plan and Scheduling………………………………20
3.2 Modeling of the Run-Mode Scheduling………………………22
3.2.1 The Deterministic Production Model………………………24
3.2.2 Properties of the Model……………………………………26
3.3 Tolerance Analysis and Parametric Programming…………28
3.4 Summary and Conclusion…………………………………………29
CHAPTER 4 A PRODUCTION MODEL FOR AN UNCERTAIN
REFINERY INDUSTRY WITH MAXIMUM PROFIT…………………………30
4.1 Investigation and Resolution of Uncertain Environment……………………………………………………………31
4.2 The Proposed FLP Model for Refinery Industry……………34
4.2.1 Definition of the Notations………………………………34
4.2.2 An Aggregated Purchasing-to-Selling Model……………35
4.3 Solution Procedure………………………………………………37
4.3.1 Method of Defuzzification…………………………………37
4.3.2 Transformation…………………………………………………39
4.4 Summary and Conclusion…………………………………………41
CHAPTER 5 PARTICLE SWARM OPTIMIZATION ALGORITHM
WITH PENALTY FUNCTION APPROACH……………………………………43
5.1 Particle Swarm Optimization (PSO) Algorithm……………43
5.1.1 The Convergence Criterion of PSO…………………………44
5.2 Dynamic Local and Global Conjoint
Particle Swarm Optimization (DCPSO) Algorithm………………45
5.2.1 The Proposed DCPSO Algorithm………………………………46
5.2.2 Pseudo Code of DCPSO…………………………………………48
5.3 Validation and Evaluation……………………………………49
5.3.1 Experimental Results and Comparison……………………51
5.4 Penalty Function Approach (PFA)……………………………60
5.4.1 The Proposed Penalty Function Strategy for PSO………62
5.4.2 Illustrative Examples………………………………………64
5.5 Summary and Conclusion…………………………………………70
CHAPTER 6 CASE STUDIES－THE CASE OF CPC IN TAIWAN…………72
6.1 A Deterministic Production Planning………………………72
6.1.1 Problem Statement and Data Analysis……………………72
6.1.2 Scenario Analysis……………………………………………73
6.2 Implementation and Evaluation of Three Scenarios………79
6.3 Summary and Conclusion…………………………………………79
6.4 The Production Planning in an Uncertain Environment…80
6.4.1 Problem Statement and Data Analysis……………………80
6.5 Impact Analysis on the Demand Forecast and Cost Estimation………………………………………………………………82
6.5.1 Impact Analysis of Price Fluctuation to the
Uncertain Demands and Profit………………………………………82
6.5.2 Impact Analysis of Fluctuating Production Quantity to the Uncertain Cost Coefficients…………………………………85
6.6 The Mechanism of Floating Oil Price………………………87
6.7 Summary and Conclusion…………………………………………88
6.8 Summary and Conclusion on Case Study………………………89
CHAPTER 7 CONCLUSIONS………………………………………………91
References………………………………………………………………94
Appendix A The Proof of Compact for the Crisp Model………100
Appendix B The Inventory Levels and Production Quantities for Current Production Plan and Basic Plan………………101
Appendix C Issues of Risk Management and Control…………110
Appendix D The CPC's Production Data
from the Year of 2009………………………………………………114
Appendix E The Results of Profit Variation vs. Selling Price Variation for Each Product Calculated by PSO and DCPSO……………………………………………………………………121

                                

Abilov, A. G., Zeybek, Z., Tuzunalp. O., Telatar, Z., 2002. Fuzzy temperature control of industrial refineries furnaces through combined feedforward/feedback multivariable cascade systems. Chemical Engineering and Processing 41, 87–98.
Alatas, B., Akin, E., 2009. Chaotically encoded particle swarm optimization algorithm and its applications. Chaos, Solitons & Fractals 41(2), 939–950.
Amos, F., Ronnqvist, M., Gill, G., 1997. Modeling the pooling problem at the New Zealand refining company. Journal of the Operational Research Society 48, 767-778.
Angeline, P. J., 1998. Using selection to improve particle swarm optimization, in: IEEE International Conference on Evolutionary Computation, Anchorage, Alaska, 231–238.
Aronofsky, J. S., Willians, A. C., 1962. The use of linear programming and mathematical models in underground oil production. Management Science 8, 394–407.
Ballintijn, K., 1993. Optimization in refinery scheduling: Modeling and solution, in: Ciriani, T.A. and Leachman, L.R.C. (Eds.), Optimization in Industry, Wiley, New York, 191-199.
Bazaraa, M.S., Jarvis, J.J., Sherali, H.D., 2005. Linear programming and network flows. Wiley, New Jersey, 290-314.
Bellman, R. E., Zadeh, L. A., 1970. Decision-making in a fuzzy environment. Management Science 17 (4), 141–164.
Bergh, F., 2002. An analysis of particle swarm optimizers. Ph. D. Thesis. Department of computer science. University of Pretoria, Pretoria, South Afica.
Bohannon, J. M., 1970. A linear programming model for optimum development of multi-reservoir pipeline systems. Journal of Petroleum Technology 22, 1429– 1436.
Cain, G. M., Shehata, M. T., 1982. A computerized system to optimize daily oil and gas production in Kuwait. Journal of Petroleum Technology 34, 487–494.
Cao, C., Gu, X., 2006. Chance constrained programming with fuzzy parameters for refinery crude oil scheduling problem. Fuzzy System and Knowledge Discovery 4223, 1010–1019.
Carlos, A., 2002. Theoretical and numerical constraint handling techniques used with evolutionary algorithms : a survey of the state of the art. Computer methods in applied mechanics and engineering 191, 1245–1287.
Chakraborty, A., 2009. A globally convergent mathematical model for synthesizing topologically constrained water recycle networks. Computers & Chemical Engineering 33 (7), 1279–1288.
Charnes, A., Cooper, W. W., Mellon, B., 1952. Blending aviation gasoline: a study in programming interdependent activities in an integrated oil company. Econometrica 20, 135–149.
Coelho, L. S., 2008. A quantum particle swarm optimizer with chaotic mutation operator. Chaos, Solitons & Fractals 37, 1409–1418.
Constantino, M., 1995. A polyhedral approach to production planning models: start-up costs and times, upper and lower bounds on production, Ph.D. Thesis, Faculte des Science, University Catholique de Louvain, Belgium.
Dinesh, K. K., Santosh, K. G., Deoki, N. S., 2007. Multi-objective optimization of fuel oil blending using the jumping gene adaptation of genetic algorithm. Fuel Processing Technology 88 (1), 51–63.
Duffuaa, S. O., Al-Zayer, J. A., Al-Marhoun, M. A., Alsaleh, M. A., 1992. A linear programming model to evaluate gas availability for vital industries in Saudi Arabia. Journal of the Operational Research Society 43 (11), 1035–1045.
Elhossini, A., Areibi, S., Dony, R., 2010. Strength Pareto particle swarm optimization and hybrid EA-PSO for multi-objective optimization. Evolutionary Computation 18, 127–156.
Erdogmus, P., 2010. Particle swarm optimization performance on special linear programming problems. Scientific Research and Essays 5(12), 1506-1518.
Garvin, W. W., Crandall, H. W., John, J. B., Spellman, R. A., 1957. Application of linear programming in the oil industry. Management Science 3 (4), 407–430.
Gothe-Lundgren, M., Lundgren, J.T., Persson, J.A., 2002. An optimization model for refinery production scheduling. Department of Mathematics, Linkoping University, Linkoping Sweden, International Journal of Production Economics 78, 205-270.
He, S., Wu, Q. H., Wen, J. Y., Saunders, J. R., Paton, R. C., 2004. A particle swarm optimizer with passive congregation. BioSystems 78, 135–147.
Hop, N. V., 2007. Solving linear programming problems under fuzziness and randomness environment using attainment values. Information Sciences 177, 2971–2984.
Hsieh, S., Chiang, C. C., 2001. Manufacturing-to-sale planning model for fuel oil production. International Journal of Advanced Manufacturing Technology 18, 303-311.
Karmarkar, U.S., Schrage, L., 1985. The deterministic dynamic product cycling problem. Operations Research 33, 326-345.
Kennedy, J., Eberhart, R., 1995. Particle swarm optimization. In: Proc. of IEEE International Conference on Neural Networks, Perth, Australia, 1942–1948.
Kennedy, J., 2000. Stereotyping: Improving particle swarm performance with cluster analysis. In: Proceedings of the IEEE International Conference on Evolutionary Computation, 1507–1512.
Kennedy, J., Mendes, R., 2002. Population structure and particle swarm performance. In: Proceedings of the 2002 Congress on Evolutionary Computation CEC 2002, IEEE Press, 1671–1676.
Kitayama, S., Arakawa, M., Yamazaki, K., 2006. Penalty function approach for the mixed discrete nonlinear problems by particle swarm optimization. Structural and Multidisciplinary Optimization 32(3), 192–202.
Lambert, A.J.D., 2009. Industrial metabolism: materials and energy flow studies. In: H.F. Wang ed., Web-based green products life cycle management systems-reverse supply chain utilization. Information Science Reference, IGI Global, 16–50.
Lee, A. S., Aronofsky, J. S., 1958. A linear programming model for scheduling crude oil production. Journal of Petroleum Technology 10 (7), 51–54.
Lee, H., Pinto, J. M., 1996. Grossmann, I.E. and Park, S., Mixed-integer linear programming model for refinery short-term scheduling of crude oil unloading with inventory management, Industrial Engineering Chemistry and Research 35, 1630–1641.
Lei, X. J., Shi, Z. K., Wang, L. J., 2007. Optimization and simulation of a MO problem solved by GAs and PSO. In: Third International Conference on Natural Computation, Haikou, Hainan, China, 4, 345–350.
Li, A. G., 2004. Particle swarms cooperative optimizer. Journal of Fudan University (Natural Science), Shanghai, China, 43, 923–925.
Liu, B., Wang, L., Jin, Y. H., Tang, F., Huang, D. X., 2005. Improved particle swarm optimization combined with chaos. Chaos, Solitons & Fractals 25, 1261–1271.
Lovbjerg, M., Rasmussen, T. K., Krink, T., 2000. Hybrid particle swarm optimization with breeding and subpopulations. In: IEEE International Conference on Evolutionary Computation, San Diego, 1217–1222.
Luo, Y. Q., Yuan, X. G., 2008. Global optimization for the synthesis of integrated water systems with particle swarm optimization algorithm. Chinese Journal of Chemical Engineering 16 (1), 11–15.
Luo, C. P., Rong, G., 2009. A strategy for the integration of production planning and scheduling in refineries under uncertainty. Chinese Journal of Chemical Engineering 17 (1), 113–127.
Man, K. F., Tang, K. S., Kwong, S., 1999. Genetic algorithms, 2nd Eds, Spronger- Verlag London Berlin Heidelberg.
Marsden, J.E., Hoffman, M.J., 1993. Elementary classical analysis, 2nd Eds,
Freeman, W.H. and Company, New York.
Moengin, P., 2010. Polynomial penalty method for solving linear programming problems. IAENG International Journal of Applied Mathematics 40(3), 40_3_06.
Motlaghi, S. Jalali, F. and Nili Ahmadabadi, M., 2008. An expert system design for a crude oil distillation column with the neural networks model and the process optimization using genetic algorithm framework. Expert Systems with Applications 35 (4), 1540–1545.
Nickabadi, A., Ebadzadeh, M. M., Safabakhsh, R., 2011. A novel particle swarm optimization algorithm with adaptive inertia weight. Applied Soft Computing 11 (4), 3658–3670.
Panne, V. D., Cornelis, C., 1992. The organization of interaction between oil refineries. Journal of the Operational Research Society 43 (12), 1159–1171.
Pedrycz, W., 1994. Why triangular membership functions? Fuzzy Sets and Systems 64: 21–30.
Persson, J.A., Gothe-Lundgren, M., 2005. Shipment planning at oil refineries using column generation and valid inequalities. European Journal of Operational Research 163 (3), 631–652.
Pinto, J.M., Moro, L.F.L., 2000. A planning model for petroleum refineries. Brazilian Journal of Chemical Engineering 1 (17), 4–7.
Rahul B. Kasat, D. Kunzru, D. N. Saraf, and Santosh, K. Gupta, 2002. Multiobjective optimization of industrial FCC units using elitist nondominated sorting genetic algorithm. Industrial & Engineering Chemical Research 41 (19), 4765–4776.
Reklaitis, R.V., 1996. Overview of scheduling planning of batch process operations, in: Reklaitis, G.V., Sunol, A.K., Rippin,D.W.T. and Hortacsu, O., (Eds.), Batch Processing Systems Engineering, Springer, Berlin, NATO ASI Series F, 660-705.
Riverol, C., Pilipovik, M. V., Carosi, C., 2006. Assessing the water requirements in refineries using possibilistic programming. Chemical Engineering and Processing 45, 533–537.
Runarsson, T. P., Xin, Y., 2000. Stochastic ranking for constrained evolutionary optimization. Evolutionary Computation, IEEE Transactions on (4)3: 284–294.
Shah, N., 1996. Mathematical programming techniques for crude oil scheduling. Computers and Chemical Engineering 20, 1227-1232.
Shi, Y., Eberhart, R. C., 1998. A new particle swarm optimizer. In: IEEE World Congress on Computational Intelligence, 69–73.
Shi, Y., Eberhart, R. C., 1998. Parameter selection in particle swarm optimization. In: Evolutionary Programming VII: Proceedings of the Seventh Annual Conference on Evolutionary Programming, New York, 591–600.
Sincak, P., Vascak, J., Kvasnicka, V., Pospichal, J., 2002. Intelligent technologies theory and applications: New trends in intelligent technologies, IOS press, Ohmsha, Amsterdam. Freeman, W.H. and Company, New York.
Sullivan, J., 1982. A computer model for planning the development of an offshore gas field. Journal of Petroleum Technology 34, 1555–1564.
Tan, R. R. Col-Long, K. J., Foo, D. C. Y., Hul, S., Ng, D. K. S., 2008. A methodology for the design of efficient resource conservation networks using adaptive swarm intelligence. Journal of Cleaner Power 16 (7), 822–833.
Van den Bergh, F., Engelbrecht, A. P., 2001. Effects of swarm size on cooperative particle swarm optimizers. In: Proceedings of the Third Genetic and Evolution Computation Conference (GECCO), San Francisco, USA.
Van den Bergh, F., Engelbrecht, A. P., 2001. Training product unit networks using cooperative particle swarm optimizer. In: Proceedings of the Third Genetic and Evolution Computation Conference (GECCO), San Francisco, USA.
Vasant, P., Nagarajan, R. and Yaacob, S., 2005. Fuzzy linear programming with vague objective coefficients in an uncertain environment. Journal of the Operational Research Society 56 (5):597–603.
Velez-Langs, O., 2005. Genetic algorithms in oil industry: An overview. Journal of Petroleum Science and Engineering 47, 15–22.
Wang, H. F., Hsu, F. C., 2009. An integrated operation module for individual risk management. European Journal of Operational Research 198, 610–617.
Wang, H. F., Wu, K. Y., 2003. Modeling and analysis for multi-product and multi-resource production scheduling. International Journal of Intelligent Manufacturing 14, 297–309.
Wang, H. F., Wu, K. Y., 2005. Preference approach to fuzzy linear inequalities and optimizations. Fuzzy Optimization and Decision Making 4, 7–23.
Wanga, H. F., Zhenga, K. W., 2010. An optimal run-mode scheduling for oil refinery
production–a case study on Taiwan CPC Corporation. Production Planning & Control 21(3), 301–318.
Wangb, H. F., Zhengb, K. W., 2010. An alternative dynamic local and global conjoint particle swarm optimization algorithm. Submitted to Applied Mathematics and Computation.
Wang, H. F., Zheng, K. W., 2011. Application of fuzzy linear programming to aggregate production plan of a refinery industry in Taiwan. Accepted by Journal of the Operational Research Society, 21st September 2011.
Zadeh, L. A., 1965. Fuzzy sets. Information and Control 8, 338–353.
Zadeh, L. A., 1978. Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems 1, 3–28.
Zhang, B.J., Hua, B., 2007. Effective MILP model for oil refinery-wide production planning and better energy utilization. Journal of Cleaner Production 15(5), 439–448.
Zhang, J. S., Wang, Z. X., 2009. A hybrid particle swarm optimization algorithm and its application in hydrogen management. In: ICIA International Conference on Information and Automation. Zhuhai, Macau, China, 1521–1525.
Zhang, Y. L., Ma, L.H., Zhang, L.Y., Qian, J.X., 2003. On the Convergence Analysis and Parameter Selection in Panicle Swarm Optimization. In: Proceedings International Conference on Machine Learning and Cybernetics. Zhejiang University, Hangzhou, China, 1802–1807.
Zhao, X. G., Wang, Y., 2009. Gasoline Blending Scheduling Based on Uncertainty. In: International Conference on Computational Intelligence and Natural Computing. Wuhan, China, 2, 82–87.
Zimmerman, H. J., 1978. Fuzzy programming and linear programming with several objective functions. Fuzzy Sets and Systems 1, 115–129.

全文公開日期本全文未授權公開 (校內網路)
全文公開日期本全文未授權公開 (校外網路)

簡易檢索 / 詳目顯示

相關論文