炼油与生物燃料供应链优化及不确定性研究
|
摘要
随着社会对能源需求的逐步增大,作为当今主要能源的石化燃料也面临着更高的供需要求。同时在石油日益短缺今天,生物燃料由于其环境友好,地域分布均衡,能值高等特点,成为了最有希望成为替代传统石油的新型能源。炼油厂的供应链优化在提升传统石化能源的整体生产效率,降低加工,运输和存储成本等各方面都起着极为重要的作用。与此同时,合理的利用现有炼油厂设施,设计合理的生物燃料供应链网络,可以有效的提升新能源的使用比率,从而进一步满足能源需求,促进社会发展。本文分别从提高炼油供应链和生物燃料供应链运作效率的角度上,对研究现状进行了具体分析,对炼油供应链计划优化,生物燃料供应链设计优化,以及两者供应链集成等问题进行了系统而深入的研究。本文的主要内容和创新点如下：
1.提出了炼油厂供应链计划问题的条件风险价值模型,在最优化经济指标的同时控制风险,为炼油供应链计划问题中的不确定性处理提供了新思路。采用样本平均逼近的方法来确定最佳风险阈值,并且测试了结果的鲁棒性。在场景数量的选择上,采用统计的方法在精确性和计算效率之间做折中。同时,深入挖掘产率波动这一内在不确定性,提出了结合优化与仿真的迭代算法,在条件风险价值的随机规划的框架下,得出满意解。
2.从炼油厂供应链的生产和配送的角度,分析了当前将炼油厂生产调度优化和管道调度优化分开考虑的不足与缺点,提出了生产调度和管道调度集成模型,从整体上优化生产调度,油品调和,管道调度与库存管理。相比传统顺序式的拉式和推式求解策略,该集成模型不仅能保证整体优化问题的可行性,而且能大幅降低整体成本。
3.针对第三代高级碳氢生物燃料的供应链与现有炼油厂设备的关系,首次提出了碳氢生物燃料供应链集成已有炼油厂的设计优化模型。模型同时兼顾生物燃料供应链战略设计与战术计划优化两个因素,综合考虑了生物炼油厂的不同技术方案,加工规模的选择,与传统炼油厂集成策略的确定,以及生物质的分布性、季节性、易腐蚀性,需求的分布性和政府财政补贴等特点。采用模糊可能性规划方法对供应链设计优化模型中的不确定性进行分析优化,决策者可以根据个人偏好选择可能性,必然性和可信性当作评价指标。
4.从最小化单位成本的角度,提出了高级碳氢生物燃料供应链集成已有炼油厂的混合整数分式规划模型。采用了两种特定算法对此优化问题进行求解。同时从最优解,容差,计算时间等方面比较了特定算法与通用的混合整数线性规划求解器在此优化问题上的差别。详细分析了以单位成本最小化与以总体成本最小化为目标的决策所造成的差别。提出了一个兼顾模型鲁棒性和经济性的鲁棒优化方法对供应链不确定性进行处理。
5.首次提出了传统炼油厂和生物燃料集成供应链的长期设计和优化模型,分析两者供应链的互相协作与影响。采用装置级的炼厂计划模型代替简单的节点投入产出模型,提高了模型的分辨率。同时采用随机规划方法对长期设计计划过程中的不确定性进行分析控制。
Enterprise optimization plays key role in both petroleum and biofuel supply chains. Supply chain optimization techniques could cooperate all the entities in petroleum refinery supply chain, and hence improve the production efficiency and reduce the overall cost. Moreover, biofuels have been proposed as part of the solution to climate change and our heavy dependence on fossil fuels. It is important to systematically design a sophisticated biofuel supply chain that takes the advantage of existing petroleum infrastructures. In this thesis, after reviewing recent researches on the supply chain optimization in both petroleum refinery supply chain and biofuel supply chain. We propose several novel models dealing with supply chain optimization problems under uncertainty. These models includes the petroleum supply chain tactical planning, biofuel supply chain strategy design, and the integrated supply chain design and planning. The details are listed as follows:
1. A stochastic programming approach for an optimal refinery planning problem under uncertainties is proposed. The Conditional Value-at-Risk theory is used to deal with demand and yield uncertainties. Sample average approximation approach is employed to determine the suitable risk aversion value. A more accurate product yield distribution based upon Markov chain is introduced. The resulting problem with such endogenous uncertainty is solved using a heuristic iterative algorithm integrating stochastic programming and simulation framework. Furthermore, the scenario number in the stochastic programming model is determined by the statistical analysis, which is a compromise of model accuracy and problem size.
2. From the view of production and distribution functions in petroleum supply chain, we present an integration model for refinery production scheduling and pipeline scheduling. A discrete time mixed-integer linear programming model is considered for scheduling problem of production and blending as the upstream of the refinery supply chain. A multi-product pipeline system using discrete mixed-integer linear programming formulation is adopted for products delivering. In this integrated model, production scheduling, product blending, pipeline scheduling, as well as the inventory management in both refinery and depots are considered in a holistic view. Compared with the traditional sequential solution strategy, the integrated model could guarantee the global feasibility and reduce the total cost.
3. A multiperiod mixed-integer linear programming model is proposed to addresses the optimal design of an advanced hydrocarbon biofuel supply chain integrating with existing petroleum refineries. Three major insertion points from the biofuel supply chain to the petroleum refineries are investigated and analyzed. This model simultaneously optimizes the supply chain design, insertion point selection, and production planning, including diverse conversion pathway, technology, and insertion point selections, biomass seasonality, geographical diversity, biomass degradation, demand distribution and government incentives. In addition, a fuzzy possibilistic programming approach is applied to deal with uncertainties, where possibility, necessity and credibility measures are adopted according to the decision makers' preference. Compared to the traditional biofuel supply chain, the advanced hydrocarbon biofuel supply chain integrating with existing petroleum refinery infrastructure significantly reduces the capital cost and total annualized cost.
4. A mixed-integer linear fractional programming model with unit cost objective is proposed to address the design and planning of advanced hydrocarbon biofuel supply chain integrating with existing petroleum refineries. A robust optimization approach which tradeoffs the performance and conservatism is adopted to deal with the demand and supply uncertainty. Moreover, the unit cost objective makes the final products more cost-competitive. The resulting mixed-integer linear fractional programming model is solved by the tailored optimization algorithm. The results show that the preconversion to petroleum-upgrading pathway is more economical when applying the unit minimization objective.
5. We address the problem of optimal design and strategic planning of the integrated biofuel and petroleum supply chain system in the presence of pricing and quantity uncertainties. To achieve a higher modeling resolution and improve the overall economic performance, we explicitly model equipment units and material streams in the retrofitted petroleum processes and propose a multi-period planning model to coordinate the various activities in the petroleum refineries. Furthermore, in order to develop an integrated supply chain that is reliable in the dynamic marketplace, we employ stochastic programming approach to optimize the expectation under a number of scenarios associated with biomass availability, fuel demand, crude-oil prices and technology evolution. Resutls show the market share of biofuels increases gradually due to the increasing crude oil price and biomass availability.
1.提出了炼油厂供应链计划问题的条件风险价值模型,在最优化经济指标的同时控制风险,为炼油供应链计划问题中的不确定性处理提供了新思路。采用样本平均逼近的方法来确定最佳风险阈值,并且测试了结果的鲁棒性。在场景数量的选择上,采用统计的方法在精确性和计算效率之间做折中。同时,深入挖掘产率波动这一内在不确定性,提出了结合优化与仿真的迭代算法,在条件风险价值的随机规划的框架下,得出满意解。
2.从炼油厂供应链的生产和配送的角度,分析了当前将炼油厂生产调度优化和管道调度优化分开考虑的不足与缺点,提出了生产调度和管道调度集成模型,从整体上优化生产调度,油品调和,管道调度与库存管理。相比传统顺序式的拉式和推式求解策略,该集成模型不仅能保证整体优化问题的可行性,而且能大幅降低整体成本。
3.针对第三代高级碳氢生物燃料的供应链与现有炼油厂设备的关系,首次提出了碳氢生物燃料供应链集成已有炼油厂的设计优化模型。模型同时兼顾生物燃料供应链战略设计与战术计划优化两个因素,综合考虑了生物炼油厂的不同技术方案,加工规模的选择,与传统炼油厂集成策略的确定,以及生物质的分布性、季节性、易腐蚀性,需求的分布性和政府财政补贴等特点。采用模糊可能性规划方法对供应链设计优化模型中的不确定性进行分析优化,决策者可以根据个人偏好选择可能性,必然性和可信性当作评价指标。
4.从最小化单位成本的角度,提出了高级碳氢生物燃料供应链集成已有炼油厂的混合整数分式规划模型。采用了两种特定算法对此优化问题进行求解。同时从最优解,容差,计算时间等方面比较了特定算法与通用的混合整数线性规划求解器在此优化问题上的差别。详细分析了以单位成本最小化与以总体成本最小化为目标的决策所造成的差别。提出了一个兼顾模型鲁棒性和经济性的鲁棒优化方法对供应链不确定性进行处理。
5.首次提出了传统炼油厂和生物燃料集成供应链的长期设计和优化模型,分析两者供应链的互相协作与影响。采用装置级的炼厂计划模型代替简单的节点投入产出模型,提高了模型的分辨率。同时采用随机规划方法对长期设计计划过程中的不确定性进行分析控制。
Enterprise optimization plays key role in both petroleum and biofuel supply chains. Supply chain optimization techniques could cooperate all the entities in petroleum refinery supply chain, and hence improve the production efficiency and reduce the overall cost. Moreover, biofuels have been proposed as part of the solution to climate change and our heavy dependence on fossil fuels. It is important to systematically design a sophisticated biofuel supply chain that takes the advantage of existing petroleum infrastructures. In this thesis, after reviewing recent researches on the supply chain optimization in both petroleum refinery supply chain and biofuel supply chain. We propose several novel models dealing with supply chain optimization problems under uncertainty. These models includes the petroleum supply chain tactical planning, biofuel supply chain strategy design, and the integrated supply chain design and planning. The details are listed as follows:
1. A stochastic programming approach for an optimal refinery planning problem under uncertainties is proposed. The Conditional Value-at-Risk theory is used to deal with demand and yield uncertainties. Sample average approximation approach is employed to determine the suitable risk aversion value. A more accurate product yield distribution based upon Markov chain is introduced. The resulting problem with such endogenous uncertainty is solved using a heuristic iterative algorithm integrating stochastic programming and simulation framework. Furthermore, the scenario number in the stochastic programming model is determined by the statistical analysis, which is a compromise of model accuracy and problem size.
2. From the view of production and distribution functions in petroleum supply chain, we present an integration model for refinery production scheduling and pipeline scheduling. A discrete time mixed-integer linear programming model is considered for scheduling problem of production and blending as the upstream of the refinery supply chain. A multi-product pipeline system using discrete mixed-integer linear programming formulation is adopted for products delivering. In this integrated model, production scheduling, product blending, pipeline scheduling, as well as the inventory management in both refinery and depots are considered in a holistic view. Compared with the traditional sequential solution strategy, the integrated model could guarantee the global feasibility and reduce the total cost.
3. A multiperiod mixed-integer linear programming model is proposed to addresses the optimal design of an advanced hydrocarbon biofuel supply chain integrating with existing petroleum refineries. Three major insertion points from the biofuel supply chain to the petroleum refineries are investigated and analyzed. This model simultaneously optimizes the supply chain design, insertion point selection, and production planning, including diverse conversion pathway, technology, and insertion point selections, biomass seasonality, geographical diversity, biomass degradation, demand distribution and government incentives. In addition, a fuzzy possibilistic programming approach is applied to deal with uncertainties, where possibility, necessity and credibility measures are adopted according to the decision makers' preference. Compared to the traditional biofuel supply chain, the advanced hydrocarbon biofuel supply chain integrating with existing petroleum refinery infrastructure significantly reduces the capital cost and total annualized cost.
4. A mixed-integer linear fractional programming model with unit cost objective is proposed to address the design and planning of advanced hydrocarbon biofuel supply chain integrating with existing petroleum refineries. A robust optimization approach which tradeoffs the performance and conservatism is adopted to deal with the demand and supply uncertainty. Moreover, the unit cost objective makes the final products more cost-competitive. The resulting mixed-integer linear fractional programming model is solved by the tailored optimization algorithm. The results show that the preconversion to petroleum-upgrading pathway is more economical when applying the unit minimization objective.
5. We address the problem of optimal design and strategic planning of the integrated biofuel and petroleum supply chain system in the presence of pricing and quantity uncertainties. To achieve a higher modeling resolution and improve the overall economic performance, we explicitly model equipment units and material streams in the retrofitted petroleum processes and propose a multi-period planning model to coordinate the various activities in the petroleum refineries. Furthermore, in order to develop an integrated supply chain that is reliable in the dynamic marketplace, we employ stochastic programming approach to optimize the expectation under a number of scenarios associated with biomass availability, fuel demand, crude-oil prices and technology evolution. Resutls show the market share of biofuels increases gradually due to the increasing crude oil price and biomass availability.
引文
[1]金云,朱和.中国炼油工业发展现状与趋势[J].国际石油经济,2013,05)：24-35+110.
[2]张前荣.《能源发展“十二五”规划》解读及政策建议[J].宏观经济管理,2013,07)：23-5.
[3]马士华,林勇,陈志祥.供应链管理[M].北京：机械工业出版社,2000.
[4]Verderame P M, Elia J A, Li J, Floudas C A. Planning and Scheduling under Uncertainty:A Review Across Multiple Sectors [J]. Ind Eng Chem Res,2010,49(9): 3993-4017.
[5]Shah N K, Li Z, Ierapetritou M G. Petroleum Refining Operations:Key Issues, Advances, and Opportunities [J]. Ind Eng Chem Res,2010,50(3):1161-70.
[6]Van Den Heever S A, Grossmann I E. An iterative aggregation/disaggregation approach for the solution of a mixed-integer nonlinear oilfield infrastructure planning model [J]. Ind Eng Chem Res,2000,39(6):1955-71.
[7]Goel V, Grossmann I E. A stochastic programming approach to planning of offshore gas field developments under uncertainty in reserves [J]. Comput Chem Eng, 2004,28(8):1409-29.
[8]Tarhan B, Grossmann I E, Goel V. Stochastic programming approach for the planning of offshore oil or gas field infrastructure under decision-dependent uncertainty [J]. Ind Eng Chem Res,2009,48(6):3078-97.
[9]Neiro S M S, Pinto J M. A general modeling framework for the operational planning of petroleum supply chains [J]. Comput Chem Eng,2004,28(6-7):871-96.
[10]Gothe-Lundgren M, T. Lundgren J, A. Persson J. An optimization model for refinery production scheduling [J]. Intl J Production Econ,2002,78(3):255-70.
[11]Al-Qahtani K, Elkamel A. Multisite facility network integration design and coordination:An application to the refining industry [J]. Comput Chem Eng,2008,
32(10):2189-202. [12] Al-Qahtani K, Elkamel A. Multisite Refinery and Petrochemical Network Design:Optimal Integration and Coordination [J]. Ind Eng Chem Res,2008,48(2): 814-26.
[13]Kim Y, Yun C, Park S B, Park S, Fan L T. An integrated model of supply network and production planning for multiple fuel products of multi-site refineries [J]. Comput Chem Eng,2008,32(11):2529-35.
[14]Li W, Hui C-W, Li A. Integrating CDU, FCC and product blending models into refinery planning [J]. Comput Chem Eng,2005,29(9):2010-28.
[15]Alhajri I, Elkamel A, Albahri T, Douglas P. A nonlinear programming model for refinery planning and optimisation with rigorous process models and product quality specifications [J]. International Journal of Oil, Gas and Coal Technology,2008,1(3): 283-307.
[16]Alattas A M, Grossmann I E, Palou-Rivera I. Integration of Nonlinear Crude Distillation Unit Models in Refinery Planning Optimization [J]. Ind Eng Chem Res, 2011,50(11):6860-70.
[17]Alattas A M, Grossmann I E, Palou-Rivera I. Refinery Production Planning: Multiperiod MINLP with Nonlinear CDU Model [J]. Ind Eng Chem Res,2012,
[18]Jia Z, Ierapetritou M. Efficient short-term scheduling of refinery operations based on a continuous time formulation [J]. Comput Chem Eng,2004,28(6-7):1001-19.
[19]Lee H, Pinto J M, Grossmann I E, Park S. Mixed-Integer Linear Programming Model for Refinery Short-Term Scheduling of Crude Oil Unloading with Inventory Management [J]. Ind Eng Chem Res,1996,35(5):1630-41.
[20]Wenkai L, Hui C-W, Hua B, Tong Z. Scheduling Crude Oil Unloading, Storage, and Processing [J]. Ind Eng Chem Res,2002,41(26):6723-34.
[21]Reddy P C P, Karimi I A, Srinivasan R. Novel solution approach for optimizing crude oil operations [J]. AlChE J,2004,50(6):1177-97.
[22]Reddy P C P, Karimi I A, Srinivasan R. A new continuous-time formulation for scheduling crude oil operations [J]. Chem Eng Sci,2004,59(6):1325-41.
[23]Pan M, Li X, Qian Y. New approach for scheduling crude oil operations [J]. Chem Eng Sci,2009,64(5):965-83.
[24]Li J, Misener R, Floudas C A. Continuous-time modeling and global optimization approach for scheduling of crude oil operations [J]. AlChE J,2012,58(1): 205-26.
[25]Mendez C A, Grossmann I E, Harjunkoski I, Kabore P. A simultaneous optimization approach for off-line blending and scheduling of oil-refinery operations [J]. Comput Chem Eng,2006,30(4):614-34.
[26]Luo C, Rong G. Hierarchical Approach for Short-Term Scheduling in Refineries [J]. Ind Eng Chem Res,2007,46(11):3656-68.
[27]Shah N, Saharidis G K D, Jia Z, Ierapetritou M G. Centralized-decentralized optimization for refinery scheduling [J]. Comput Chem Eng,2009,33(12):2091-105.
[28]Shah N K, Ierapetritou M G. Short-term scheduling of a large-scale oil-refinery operations:Incorporating logistics details [J]. AlChE J,2011,57(6):1570-84.
[29]Rejowski R, Pinto J M. Scheduling of a multiproduct pipeline system [J]. Comput Chem Eng,2003,27(8-9):1229-46.
[30]Cafaro D C, Cerda J. Optimal scheduling of multiproduct pipeline systems using a non-discrete MILP formulation [J]. Comput Chem Eng,2004,28(10):2053-68.
[31]Rejowski R, Pinto J M. Efficient MILP formulations and valid cuts for multiproduct pipeline scheduling [J]. Comput Chem Eng,2004,28(8):1511-28.
[32]Relvas S, Matos H A, Barbosa-Povoa A P F D, Fialho J, Pinheiro A S. Pipeline Scheduling and Inventory Management of a Multiproduct Distribution Oil System [J]. Ind Eng Chem Res,2006,45(23):7841-55.
[33]Cafaro D C, Cerda J. Efficient Tool for the Scheduling of Multiproduct Pipelines and Terminal Operations [J]. Ind Eng Chem Res,2008,47(24):9941-56.
[34]Cafaro D C, Cerda J. Dynamic scheduling of multiproduct pipelines with multiple delivery due dates [J]. Comput Chem Eng,2008,32(4-5):728-53.
[35]Relvas S, Matos H A, Barbosa-Povoa A P F D, Fialho J. Reactive Scheduling Framework for a Multiproduct Pipeline with Inventory Management [J]. Ind Eng Chem Res,2007,46(17):5659-72.
[36]Rejowski Jr R, Pinto J M. A novel continuous time representation for the scheduling of pipeline systems with pumping yield rate constraints [J]. Comput Chem Eng,2008,32(4-5):1042-66.
[37]MirHassani S A, Ghorbanalizadeh M. The multi-product pipeline scheduling system [J]. Comput Math Appl,2008,56(4):891-7.
[38]MirHassani S A, Fani Jahromi H. Scheduling multi-product tree-structure pipelines [J]. Comput Chem Eng,2011,35(1):165-76.
[39]Herran A, de la Cruz J M, de Andres B. A mathematical model for planning transportation of multiple petroleum products in a multi-pipeline system [J]. Comput Chem Eng,2010,34(3):401-13.
[40]Cafaro D C, Cerda J. Operational scheduling of refined products pipeline networks with simultaneous batch injections [J]. Comput Chem Eng,2010,34(10): 1687-704.
[41]Cafaro V G, Cafaro D C, Mendez C A, Cerda J. Detailed Scheduling of Operations in Single-Source Refined Products Pipelines [J]. Ind Eng Chem Res,2011, 50(10):6240-59.
[42]Cafaro D C, Cerda J. Rigorous scheduling of mesh-structure refined petroleum pipeline networks [J]. Comput Chem Eng,2012,38(0):185-203.
[43]Cafaro V G, Cafaro D C, Mendez C A, Cerda J. Detailed Scheduling of Single-Source Pipelines with Simultaneous Deliveries to Multiple Offtake Stations [J]. Ind Eng Chem Res,2012,
[44]Magatao L, Arruda L V R, Neves Jr F. A mixed integer programming approach for scheduling commodities in a pipeline [J]. Comput Chem Eng,2004,28(1-2):171-85.
[45]Boschetto S N, Magatao L, Brondani W M, Neves-Jr F v, Arruda L V R, Barbosa-Povoa A P F D, Relvas S. An Operational Scheduling Model to Product Distribution through a Pipeline Network [J]. Ind Eng Chem Res,2010,49(12):5661-82.
[46]Magatao S N B, Magatao L, Polli H L, Neves F, Arruda L V R, Relvas S, Povoa A B. Planning and sequencing product distribution in a real-world pipeline network: An MILP decomposition approach [J]. Ind Eng Chem Res,2012,
[47]闫强,陈毓川,王安建,王高尚,于汶加,陈其慎.我国新能源发展障碍与应对：全球现状评述[J].地球学报,2010,05)：759-67.
[48]Huber G W, Conna A. Synergies between Bio- and Oil Refineries for the Production of Fuels from Biomass [J]. Angew Chem Int Ed,2007,46(38):7184-201.
[49]DOE. Replacing the Whole Barrel to reduce U.S. Dependence on Oil, U.S. Department of Energy [M].2012.
[50]周中仁,吴文良.生物质能研究现状及展望[J].农业工程学报,2005,12)：12-5.
[51]Yue D, You F, Snyder S W. Biomass-to-Biofuel Supply Chain Optimization: Overview, Key Issues and Challenges [J]. Comput Chem Eng,2013,0):
[52]An H, Wilhelm W E, Searcy S W. Biofuel and petroleum-based fuel supply chain research:A literature review [J]. Biomass Bioenerg,2011,35(9):3763-74.
[53]Anex R P, Aden A, Kazi F K, Fortman J, Swanson R M, Wright M M, Satrio J A, Brown R C, Daugaard D E, Platon A, Kothandaraman G, Hsu D D, Dutta A. Techno-economic comparison of biomass-to-transportation fuels via pyrolysis, gasification, and biochemical pathways [J]. Fuel,2010,89(SUPPL.1):S29-S35.
[54]DOE. Biochemical conversion:Using hydrolysis, fermentation, and catalysis to make fuels and chemicals [M]//ENERGY U S D O.2011.
[55]DOE. Thermochemical conversion:Using heat and chemistry to make fuels and bioproducts [M]//ENERGY U S D O.2011.
[56]NABC, Advanced Biofuels Processing and Integration, National Advenced Biofuels Consortium [R/OL],2012, http://www.nabcprojects.org/biofuels.html
[57]Sokhansanj S, Kumar A, Turhollow A F. Development and implementation of integrated biomass supply analysis and logistics model (IBSAL) [J]. Biomass Bioenerg, 2006,30(10):838-47.
[58]Elia J A, Baliban R C, Xiao X, Floudas C A. Optimal energy supply network determination and life cycle analysis for hybrid coal, biomass, and natural gas to liquid (CBGTL) plants using carbon-based hydrogen production [J]. Comput Chem Eng,2011, 35(8):1399-430.
[59]Bowling I M, Ponce-Ortega J M a, El-Halwagi M M. Facility Location and Supply Chain Optimization for a Biorefinery [J]. Ind Eng Chem Res,2011,50(10): 6276-86.
[60]Dunnett A, Adjiman C, Shah N. A spatially explicit whole-system model of the lignocellulosic bioethanol supply chain:an assessment of decentralised processing potential [J]. Biotechnol Biofuels,2008,1(1):1-17.
[61]Zamboni A, Shah N, Bezzo F. Spatially Explicit Static Model for the Strategic Design of Future Bioethanol Production Systems.1. Cost Minimization [J]. Energy Fuels,2009,23(10):5121-33.
[62]Kim J, Realff M J, Lee J H, Whittaker C, Furtner L. Design of biomass processing network for biofuel production using an MILP model [J]. Biomass Bioenerg, 2011,35(2):853-71.
[63]You F, Tao L, Graziano D J, Snyder S W. Optimal design of sustainable cellulosic biofuel supply chains:Multiobjective optimization coupled with life cycle assessment and input-output analysis [J]. AlChE J,2012,58(4):1157-80.
[64]Giarola S, Zamboni A, Bezzo F. Spatially explicit multi-objective optimisation for design and planning of hybrid first and second generation biorefineries [J]. Comput Chem Eng,2011,35(9):1782-97.
[65]You F, Wang B. Life Cycle Optimization of Biomass-to-Liquid Supply Chains with Distributed-Centralized Processing Networks [J]. Ind Eng Chem Res,2011, 50(17):10102-27.
[66]Gebreslassie B H, Yao Y, You F. Design under uncertainty of hydrocarbon biorefinery supply chains:Multiobjective stochastic programming models, decomposition algorithm, and a Comparison between CVaR and downside risk [J]. AlChE J,2012,58(7):2155-79.
[67]Corsano G, Vecchietti A R, Montagna J M. Optimal design for sustainable bioethanol supply chain considering detailed plant performance model [J]. Comput Chem Eng,2011,35(8):1384-98.
[68]Akgul O, Shah N, Papageorgiou L G. Economic optimisation of a UK advanced biofuel supply chain [J]. Biomass Bioenerg,2012,41(0):57-72.
[69]Elia J A, Baliban R C, Floudas C A. Nationwide energy supply chain analysis for hybrid feedstock processes with significant CO2 emissions reduction [J]. AlChE J, 2012,58(7):2142-54.
[70]Tong K, Gleeson M J, Rong G, You F. Optimal design of advanced drop-in hydrocarbon biofuel supply chain integrating with existing petroleum refineries under uncertainty [J]. Biomass Bioenerg,2014,60(0):108-20.
[71]Tong K, You F, Rong G. Robust design and operations of hydrocarbon biofuel supply chain integrating with existing petroleum refineries considering unit cost objective [J]. Comput Chem Eng,2014,
[72]Tong K, Gong J, Yue D, You F. Stochastic Programming Approach to Optimal Design and Operations of Integrated Hydrocarbon Biofuel and Petroleum Supply Chains [J]. ACS Sustain Chem Eng,2014,2(1):49-61.
[73]Li Z, Ierapetritou M. Process scheduling under uncertainty:Review and challenges [J]. Comput Chem Eng,2008,32(4-5):715-27.
[74]Tsiakis P, Shah N, Pantelides C C. Design of Multi-echelon Supply Chain Networks under Demand Uncertainty [J]. Ind Eng Chem Res,2001,40(16):3585-604.
[75]Dempster M A H, Pedron N H, Medova E, Scott J, Sembos A. Planning logistics operations in the oil industry [J]. J Oper Res Soc,2000,1271-88.
[76]Li W, Hui C-W, Li P, Li A-X. Refinery Planning under Uncertainty [J]. Ind Eng Chem Res,2004,43(21):6742-55.
[77]Luo C, Rong G. A Strategy for the Integration of Production Planning and Scheduling in Refineries under Uncertainty [J]. Chin J Chem Eng,2009,17(1):113-27.
[78]Gu H, Rong G. A Two-stage Discriminating Framework for Making Supply Chain Decisions under Uncertainties [J]. Chem Biochem Eng Q,2010,24(1):P51-6. [79] Lababidi H M S, Ahmed M A, Alatiqi I M, Al-Enzi A F. Optimizing the Supply
Chain of a Petrochemical Company under Uncertain Operating and Economic Conditions [J]. Ind Eng Chem Res,2003,43(1):63-73.
[80]Khor C S, Elkamel A, Ponnambalam K, Douglas P L. Two-stage stochastic programming with fixed recourse via scenario planning with economic and operational risk management for petroleum refinery planning under uncertainty [J]. Chem Eng Process,2008,47(9-10):1744-64.
[81]Yang J, Gu H, Rong G. Supply Chain Optimization for Refinery with Considerations of Operation Mode Changeover and Yield Fluctuations [J]. Ind Eng Chem Res,2009,49(1):276-87.
[82]Wang J, Rong G. Robust Optimization Model for Crude Oil Scheduling under Uncertainty [J]. Ind Eng Chem Res,2009,49(4):1737-48.
[83]Huchzermeier A, Cohen M A. Valuing operational flexibility under exchange rate risk [J]. Oper Res,1996,44(1):100-13.
[84]Kouvelis P, Rosenblatt M J. A mathematical programming model for global supply chain management:Conceptual approach and managerial insights [M]. Supply chain management:Models, applications, and research directions. Springer.2005:245-77.
[85]Kim J, Realff M J, Lee J H. Optimal design and global sensitivity analysis of biomass supply chain networks for biofuels under uncertainty [J]. Comput Chem Eng, 2011,35(9):1738-51.
[86]Dal-Mas M, Giarola S, Zamboni A, Bezzo F. Strategic design and investment capacity planning of the ethanol supply chain under price uncertainty [J]. Biomass Bioenerg,2011,35(5):2059-71.
[87]Alex Marvin W, Schmidt L D, Benjaafar S, Tiffany D G, Daoutidis P. Economic optimization of a lignocellulosic biomass-to-ethanol supply chain [J]. Chem Eng Sci, 2012,67(1):68-79.
[88]Birge J R, Louveaux F V. Introduction to stochastic programming [M]. New York:Springer,1997.
[89]Ben-Tal A, Nemirovski A. Robust optimization-methodology and applications [J]. Math Program,2002,92(3):453-80.
[90]Charnes A, Cooper W W. Chance-Constrained Programming [J]. Manage Sci, 1959,6(1):73-9.
[91]Zimmermann H J. Fuzzy set theory--and its applications [M]. Springer,2001.
[92]You F, Wassick J M, Grossmann I E. Risk management for a global supply chain planning under uncertainty:Models and algorithms [J]. AlChE J,2009,55(4): 931-46.
[93]Sahinidis N V. Optimization under uncertainty:state-of-the-art and opportunities [J]. Comput Chem Eng,2004,28(6-7):971-83.
[94]Barbaro A, Bagajewicz M J. Managing financial risk in planning under uncertainty [J]. AlChE J,2004,50(5):963-89.
[95]Rockafellar R T. Optimization of Conditional Value at Risk [J]. J Risk,2000, 2(21-41.
[96]Rockafellar R T, Uryasev S. Conditional value-at-risk for general loss distributions [J]. J Bank Financ,2002,26(7):1443-71.
[97]Dakin R J. A tree-search algorithm for mixed integer programming problems [J]. Comput J,1965,8(3):250-5.
[98]Stubbs R A, Mehrotra S. A branch-and-cut method for 0-1 mixed convex programming [J]. Math Program,1999,86(3):515-32.
[99]Kelley J, James E. The cutting-plane method for solving convex programs [J]. J Soc Ind Appl Math,1960,8(4):703-12.
[100]CPLEX I B M I.12.2 User's Manual [M]. IBM.2010.
[101]Ashford R, Daniel R. LP-Model:Xpress-LP's model builder [J]. IMA Journal of Management Mathematics,1986,1(3):163-76.
[102]Optimization G. Gurobi optimizer reference manual [J]. URL: http://www.gurobi.com,2012,
[103]Benders J F. Partitioning procedures for solving mixed-variables programming problems [J]. Numerische mathematik,1962,4(1):238-52.
[104]Guignard M, Kim S. Lagrangean decomposition:a model yielding stronger Lagrangean bounds [J]. Math Program,1987,39(2):215-28.
[105]Iyer R R, Grossmann I E. A Bilevel Decomposition Algorithm for Long-Range Planning of Process Networks [J]. Ind Eng Chem Res,1998,37(2):474-81.
[106]You F, Grossmann I E. Multicut Benders decomposition algorithm for process supply chain planning under uncertainty [J]. Ann Oper Res,2013,210(1):191-211.
[107]Fisher M L. The Lagrangian relaxation method for solving integer programming problems [J]. Manage Sci,1981,27(1):1-18.
[108]Chu Y, You F. Integration of production scheduling and dynamic optimization for multi-product CSTRs:Generalized Benders decomposition coupled with global mixed-integer fractional programming [J]. Comput Chem Eng,2013,58(0):315-33.
[109]Chu Y, You F. Integration of Scheduling and Dynamic Optimization of Batch Processes under Uncertainty:Two-Stage Stochastic Programming Approach and Enhanced Generalized Benders Decomposition Algorithm [J]. Ind Eng Chem Res,2013, 52(47):16851-69.
[110]Chu Y, You F. Integrated Scheduling and Dynamic Optimization of Complex Batch Processes with General Network Structure Using a Generalized Benders Decomposition Approach [J]. Ind Eng Chem Res,2013,52(23):7867-85.
[111]van den Heever S A, Grossmann I E, Vasantharajan S, Edwards K. A Lagrangean Decomposition Heuristic for the Design and Planning of Offshore Hydrocarbon Field Infrastructures with Complex Economic Objectives [J]. Ind Eng Chem Res,2001,40(13):2857-75.
[112]Jackson J R, Grossmann I E. Temporal decomposition scheme for nonlinear multisite production planning and distribution models [J]. Ind Eng Chem Res,2003, 42(13):3045-55.
[113]You F, Grossmann I E. Mixed-Integer Nonlinear Programming Models and Algorithms for Large-Scale Supply Chain Design with Stochastic Inventory Management [J]. Ind Eng Chem Res,2008,47(20):7802-17.
[114]You F, Grossmann I E. Integrated multi-echelon supply chain design with inventories under uncertainty:MINLP models, computational strategies [J]. AlChE J, 2010,56(2):419-40.
[115]Terrazas-Moreno S, Trotter P A, Grossmann I E. Temporal and spatial Lagrangean decompositions in multi-site, multi-period production planning problems with sequence-dependent changeovers [J]. Comput Chem Eng,2011,35(12):2913-28.
[116]You F, Grossmann I E, Wassick J M. Multisite Capacity, Production, and Distribution Planning with Reactor Modifications:MILP Model, Bilevel Decomposition Algorithm versus Lagrangean Decomposition Scheme [J]. Ind Eng Chem Res,2010,50(9):4831-49.
[117]Terrazas-Moreno S, Grossmann I E. A multiscale decomposition method for the optimal planning and scheduling of multi-site continuous multiproduct plants [J]. Chem Eng Sci,2011,66(19):4307-18.
[118]Khor C S, Elkamel A, Douglas P L. Stochastic refinery planning with risk management [J]. Pet Sci Technol,2008,26(14):1726-40.
[119]Jonsbraten T W. Optimization models for petroleum field exploitation [D]. Norway; Stavanger College,1998d.
[120]Jonsbraten T, Wets R, Woodruff D. A class of stochastic programs withdecision dependent random elements [J]. Ann Oper Res,1998,82(83-106.
[121]Goel V, Grossmann I E, El-Bakry A S, Mulkay E L. A novel branch and bound algorithm for optimal development of gas fields under uncertainty in reserves [J]. Comput Chem Eng,2006,30(6-7):1076-92.
[122]Balasubramanian J, Grossmann I E. Approximation to Multistage Stochastic Optimization in Multiperiod Batch Plant Scheduling under Demand Uncertainty [J]. Ind Eng Chem Res,2004,43(14):3695-713.
[123]Tarhan B, Grossmann I E. A multistage stochastic programming approach with strategies for uncertainty reduction in the synthesis of process networks with uncertain yields [J]. Comput Chem Eng,2008,32(4-5):766-88.
[124]Gupta V, Grossmann I E. Solution strategies for multistage stochastic programming with endogenous uncertainties [J]. Comput Chem Eng,2011,35(11): 2235-47.
[125]Mulvey J M, Vanderbei R J, Zenios S A. Robust Optimization of Large-Scale Systems [J]. Oper Res,1995,43(2):264-81.
[126]Ahmed S, Sahinidis N V. Robust Process Planning under Uncertainty [J]. Ind Eng Chem Res,1998,37(5):1883-92.
[127]Eppen G D, Martin R K, Schrage L. A Scenario Approach to Capacity Planning [J]. Oper Res,1989,37(4):517-27.
[128]Verderame P M, Floudas C A. Operational Planning of Large-Scale Industrial Batch Plants under Demand Due Date and Amount Uncertainty:Ⅱ. Conditional Value-at-Risk Framework [J]. Ind Eng Chem Res,2009,49(1):260-75.
[129]Carneiro M C, Ribas G P, Hamacher S. Risk Management in the Oil Supply Chain:A CVaR Approach [J]. Ind Eng Chem Res,2010,49(7):3286-94.
[130]Verderame P M, Floudas C A. Multisite Planning under Demand and Transportation Time Uncertainty:Robust Optimization and Conditional Value-at-Risk Frameworks [J]. Ind Eng Chem Res,2010,50(9):4959-82.
[131]Verderame P M, Floudas C A. Integration of Operational Planning and Medium-Term Scheduling for Large-Scale Industrial Batch Plants under Demand and Processing Time Uncertainty [J]. Ind Eng Chem Res,2010,49(10):4948-65.
[132]Shapiro A, Homem-de-Mello T. A simulation-based approach to two-stage stochastic programming with recourse [J]. Math Program,1998,81(3):301-25.
[133]Law A M, Kelton W D. simulation modeling & analysis [M].3rd ed. New York: McGraw-Hill,2000.
[134]Mele F D, Guillen G, Espuna A, Puigjaner L. A Simulation-Based Optimization Framework for Parameter Optimization of Supply-Chain Networks [J]. Ind Eng Chem Res,2006,45(9):3133-48.
[135]Karuppiah R, Martin M, Grossmann I E. A simple heuristic for reducing the number of scenarios in two-stage stochastic programming [J]. Comput Chem Eng,2010, 34(8):1246-55.
[136]Wang W, Ahmed S. Sample average approximation of expected value constrained stochastic programs [J]. Oper Res Lett,2008,36(5):515-9.
[137]Grinstead C M, Snell J L. Introduction to Probability [M].2nd ed. RI: Providence,1997.
[138]Solak S. Efficient solution procedures for multistage stochastic formualtions of two problem classes [D]; Georgia Institute of Technology,2007.
[139]Grossmann I. Enterprise-wide optimization:A new frontier in process systems engineering [J]. AlChE J,2005,51(7):1846-57.
[140]Floudas C A, Lin X. Continuous-time versus discrete-time approaches for scheduling of chemical processes:a review [J]. Comput Chem Eng,2004,28(11):2109-29.
[141]Jia Z, Ierapetritou M, Kelly J D. Refinery Short-Term Scheduling Using Continuous Time Formulation:Crude-Oil Operations [J]. Ind Eng Chem Res,2003, 42(13):3085-97.
[142]Maravelias C T, Sung C. Integration of production planning and scheduling: Overview, challenges and opportunities [J]. Comput Chem Eng,2009,33(12):1919-
[143]Li Z, Ierapetritou M G. Integrated production planning and scheduling using a decomposition framework [J]. Chem Eng Sci,2009,64(16):3585-97.
[144]Nie Y, Biegler L T, Wassick J M. Integrated scheduling and dynamic optimization of batch processes using state equipment networks [J]. AlChE J,2012, 58(11):3416-32.
[145]Guyonnet P, Grant F H, Bagajewicz M J. Integrated Model for Refinery Planning, Oil Procuring, and Product Distribution [J]. Ind Eng Chem Res,2008,48(1): 463-82.
[146]Kuo T-H, Chang C-T. Application of a Mathematic Programming Model for Integrated Planning and Scheduling of Petroleum Supply Networks [J]. Ind Eng Chem Res,2008,47(6):1935-54.
[147]杨佳丽,荣冈.结合质量传递及约束的炼油生产计划优化[J].化工学报,2010,01)：124-31.
[148]Jones W, Paddock K. Transport by pipeline [J]. Mod Petrol Technol,1982,
[149]DOE. Biomass program multi-year program plan [M]//ENERGY U S D O. 2013.
[150]DOE. U.S. Billion-Ton Update:Biomass Supply for a Bioenergy and Bioproducts Industry, U.S. Department of Energy [M].2011.
[151]Liu B, Iwamura K. Chance constrained programming with fuzzy parameters [J]. Fuzzy Set Syst,1998,94(2):227-37.
[152]Zadeh L A. Fuzzy sets [J]. Inform Control,1965,8(3):338-53.
[153]Baoding L, Kakuzo I. A note on chance constrained programming with fuzzy coefficients [J]. Fuzzy Set Syst,1998,100(1-3):229-33.
[154]Tan R R, Aviso K B, Barilea I U, Culaba A B, Cruz Jr J B. A fuzzy multi-regional input-output optimization model for biomass production and trade under resource and footprint constraints [J]. Appl Energ,2012,90(1):154-60.
[155]Tay D H S, Ng D K S, Sammons N E, Eden M R. Fuzzy Optimization Approach for the Synthesis of a Sustainable Integrated Biorefinery [J]. Ind Eng Chem Res,2011, 50(3):1652-65.
[156]Wright M M, Daugaard D E, Satrio J A, Brown R C. Techno-economic analysis of biomass fast pyrolysis to transportation fuels [J]. Fuel,2010,89(S2-S10.
[157]Gebreslassie B H, Slivinsky M, Wang B, You F. Life cycle optimization for sustainable design and operations of hydrocarbon biorefinery via fast pyrolysis, hydrotreating and hydrocracking [J]. Comput Chem Eng,2013,50(0):71-91.
[158]Wang B, Gebreslassie B H, You F. Sustainable Design and Synthesis of Hydrocarbon Biorefinery via Gasification Pathway:Integrated Life Cycle Assessment and Technoeconomic Analysis with Multiobjective Superstructure Optimization [J]. Comput Chem Eng,2013,52(0):55-76.
[159]Wright M M, Brown R C, Boateng A A. Distributed processing of biomass to bio-oil for subsequent production of Fischer-Tropsch liquids [J]. Biofuel Bioprod Bior, 2008,2(3):229-38.
[160]Zadeh L A. Fuzzy sets as a basis for a theory of possibility [J]. Fuzzy Set Syst, 1999,100, Supplement 1(0):9-34.
[161]Inuiguchi M, Ramik J. Possibilistic linear programming:a brief review of fuzzy mathematical programming and a comparison with stochastic programming in portfolio selection problem [J]. Fuzzy Set Syst,2000,111(1):3-28.
[162]NREL, [R/OL], U.S. National Renewable Energy Laboratory,2012, http://www.nrel.gov/gis/data_biomass.html
[163]Gentry B, Blankinship D, Wainwright E. Oracle crystal ball user manual.11.1. Denver, USA:Orcale [M]. Inc.2008.
[164]EIA, Energy Information Administrator [R/OL],2012, http://www.eia.gov/
[165]Wikipedia, List of Airport in Illinois [R/OL],2012, http://en.wikixpedia.org/wiki/List of airports in Illinois
[166]Rick, Longitude and Latitude Calculations [R/OL],1999, http://mathforum.org/library/drmath/view/51879.html
[167]Pootakham T, Kumar A. Bio-oil transport by pipeline:A techno-economic assessment [J]. Bioresour Technol,2010,101(18):7137-43.
[168]Rosenthal R. GAMS:A User's Manual [J]. GAMS Development Corporation, Washington, DC,2012,
[169]Yue D, Kim M A, You F. Design of Sustainable Product Systems and Supply Chains with Life Cycle Optimization Based on Functional Unit:General Modeling Framework, Mixed-Integer Nonlinear Programming Algorithms and Case Study on Hydrocarbon Biofuels [J]. ACS Sustain Chem Eng,2013,1(8):1003-14.
[170]Zhong Z, You F. Globally convergent exact and inexact parametric algorithms for solving large-scale mixed-integer fractional programs and applications in process systems engineering [J]. Comput Chem Eng,2014,61(0):90-101.
[171]Yue D, Guillen-Gosalbez G, You F. Global optimization of large-scale mixed-integer linear fractional programming problems:A reformulation-linearization method and process scheduling applications [J]. AlChE J,2013,59(11):4255-72.
[172]You F, Castro P M, Grossmann I E. Dinkelbach's algorithm as an efficient method to solve a class of MINLP models for large-scale cyclic scheduling problems [J]. Comput Chem Eng,2009,33(11):1879-89.
[173]Yue D, You F. Sustainable scheduling of batch processes under economic and environmental criteria with MINLP models and algorithms [J]. Comput Chem Eng, 2013,54(0):44-59.
[174]Marvin W A, Schmidt L D, Daoutidis P. Biorefinery Location and Technology Selection Through Supply Chain Optimization [J]. Ind Eng Chem Res,2012,52(9): 3192-208.
[175]Giarola S, Bezzo F, Shah N. A risk management approach to the economic and environmental strategic design of ethanol supply chains [J]. Biomass Bioenerg,2013, 58(0):31-51.
[176]Bertsimas D, Sim M. Robust discrete optimization and network flows [J]. Math Program,2003,98(1-3):49-71.
[177]Zhang Q, Gong J, Skwarczek M, Yue D, You F. Sustainable process design and synthesis of hydrocarbon biorefinery through fast pyrolysis and hydroprocessing [J]. AlChE J,2014,60(3):980-94.
[178]Duran M A, Grossmann I E. An outer-approximation algorithm for a class of mixed-integer nonlinear programs [J]. Math Program,1986,36(3):307-39.
[179]Bussieck M R, Drud A. SBB:A new solver for mixed integer nonlinear programming [J]. Recent advances in nonlinear mixed integer optimization, INFORMS Fall, Invited talk,2001,
[180]Sahinidis N V. BARON:A general purpose global optimization software package [J]. J Global Optim,1996,8(2):201-5.
[181]Glover F. Improved linear integer programming formulations of nonlinear integer problems [J]. Manage Sci,1975,22(4):455-60.
[182]Li Z, Ierapetritou M G. Robust Optimization for Process Scheduling Under Uncertainty [J]. Ind Eng Chem Res,2008,47(12):4148-57.
[183]Soyster A L. Technical Note-Convex Programming with Set-Inclusive Constraints and Applications to Inexact Linear Programming [J]. Oper Res,1973,21 (5): 1154-7.
[184]Ben-Tal A, Nemirovski A. Robust solutions of uncertain linear programs [J]. Oper Res Lett,1999,25(1):1-13.
[185]Al-Qahtani K Y, Elkamel A. Planning and Integration of Refinery and Petrochemical Operations [M]. Wiley, com,2011.
[186]Marker T:UOP LLC,2005.
[187]Jones S B, Valkenburg C, Walton C W, Elliott D C, Holladay J E, Stevens D J, Kinchin C, Czernik S. Production of gasoline and diesel from biomass via fast pyrolysis, hydrotreating and hydrocracking:a design case [M]. Pacific Northwest National Laboratory Richland, WA,2009.
[188]Tong K, Yiping F, Gang R. Integrated Model for Refinery Production and Pipeline System Scheduling [C].Proceedings of the 22th European Symposium of Computer Aided Process Engineering, London, UK,2012.
[189]Tong K, Feng Y, Rong G. Planning under Demand and Yield Uncertainties in an Oil Supply Chain [J]. Ind Eng Chem Res,2011,51(2):814-34.
[190]DOE, U.S. Department of Energy [R/OL],2012, http://www.afdc.energy.gov/laws/
[191]U.S. Department of Agriculture Statistics [R/OL],2012, http://www.nass.usda.gov/
[192]EISA. Energy Independence and Security Act of 2007 [M]. Public Law 110-140,110th Congress.2007.
[193]赵小强.炼厂生产调度问题研究[D]；浙江大学,2005.
[194]罗春鹏.炼油企业生产调度研究[D]；浙江大学,2008.
[195]顾海杰.不确定性条件下炼油厂供应链优化问题研究[D]；浙江大学,2009.
[196]王继帅.带有不确定性的石化企业供应链优化研究[D]；浙江大学,2011.
[197]Martin M, Grossmann I E. Optimal use of hybrid feedstock, switchgrass and shale gas for the simultaneous production of hydrogen and liquid fuels [J]. Energy, 2013,55(0):378-91.
[198]Cafaro D C, Grossmann I E. Alternate approximation of concave cost functions for process design and supply chain optimization problems [J]. Comput Chem Eng, 2014,60(0):376-80.
[2]张前荣.《能源发展“十二五”规划》解读及政策建议[J].宏观经济管理,2013,07)：23-5.
[3]马士华,林勇,陈志祥.供应链管理[M].北京：机械工业出版社,2000.
[4]Verderame P M, Elia J A, Li J, Floudas C A. Planning and Scheduling under Uncertainty:A Review Across Multiple Sectors [J]. Ind Eng Chem Res,2010,49(9): 3993-4017.
[5]Shah N K, Li Z, Ierapetritou M G. Petroleum Refining Operations:Key Issues, Advances, and Opportunities [J]. Ind Eng Chem Res,2010,50(3):1161-70.
[6]Van Den Heever S A, Grossmann I E. An iterative aggregation/disaggregation approach for the solution of a mixed-integer nonlinear oilfield infrastructure planning model [J]. Ind Eng Chem Res,2000,39(6):1955-71.
[7]Goel V, Grossmann I E. A stochastic programming approach to planning of offshore gas field developments under uncertainty in reserves [J]. Comput Chem Eng, 2004,28(8):1409-29.
[8]Tarhan B, Grossmann I E, Goel V. Stochastic programming approach for the planning of offshore oil or gas field infrastructure under decision-dependent uncertainty [J]. Ind Eng Chem Res,2009,48(6):3078-97.
[9]Neiro S M S, Pinto J M. A general modeling framework for the operational planning of petroleum supply chains [J]. Comput Chem Eng,2004,28(6-7):871-96.
[10]Gothe-Lundgren M, T. Lundgren J, A. Persson J. An optimization model for refinery production scheduling [J]. Intl J Production Econ,2002,78(3):255-70.
[11]Al-Qahtani K, Elkamel A. Multisite facility network integration design and coordination:An application to the refining industry [J]. Comput Chem Eng,2008,
32(10):2189-202. [12] Al-Qahtani K, Elkamel A. Multisite Refinery and Petrochemical Network Design:Optimal Integration and Coordination [J]. Ind Eng Chem Res,2008,48(2): 814-26.
[13]Kim Y, Yun C, Park S B, Park S, Fan L T. An integrated model of supply network and production planning for multiple fuel products of multi-site refineries [J]. Comput Chem Eng,2008,32(11):2529-35.
[14]Li W, Hui C-W, Li A. Integrating CDU, FCC and product blending models into refinery planning [J]. Comput Chem Eng,2005,29(9):2010-28.
[15]Alhajri I, Elkamel A, Albahri T, Douglas P. A nonlinear programming model for refinery planning and optimisation with rigorous process models and product quality specifications [J]. International Journal of Oil, Gas and Coal Technology,2008,1(3): 283-307.
[16]Alattas A M, Grossmann I E, Palou-Rivera I. Integration of Nonlinear Crude Distillation Unit Models in Refinery Planning Optimization [J]. Ind Eng Chem Res, 2011,50(11):6860-70.
[17]Alattas A M, Grossmann I E, Palou-Rivera I. Refinery Production Planning: Multiperiod MINLP with Nonlinear CDU Model [J]. Ind Eng Chem Res,2012,
[18]Jia Z, Ierapetritou M. Efficient short-term scheduling of refinery operations based on a continuous time formulation [J]. Comput Chem Eng,2004,28(6-7):1001-19.
[19]Lee H, Pinto J M, Grossmann I E, Park S. Mixed-Integer Linear Programming Model for Refinery Short-Term Scheduling of Crude Oil Unloading with Inventory Management [J]. Ind Eng Chem Res,1996,35(5):1630-41.
[20]Wenkai L, Hui C-W, Hua B, Tong Z. Scheduling Crude Oil Unloading, Storage, and Processing [J]. Ind Eng Chem Res,2002,41(26):6723-34.
[21]Reddy P C P, Karimi I A, Srinivasan R. Novel solution approach for optimizing crude oil operations [J]. AlChE J,2004,50(6):1177-97.
[22]Reddy P C P, Karimi I A, Srinivasan R. A new continuous-time formulation for scheduling crude oil operations [J]. Chem Eng Sci,2004,59(6):1325-41.
[23]Pan M, Li X, Qian Y. New approach for scheduling crude oil operations [J]. Chem Eng Sci,2009,64(5):965-83.
[24]Li J, Misener R, Floudas C A. Continuous-time modeling and global optimization approach for scheduling of crude oil operations [J]. AlChE J,2012,58(1): 205-26.
[25]Mendez C A, Grossmann I E, Harjunkoski I, Kabore P. A simultaneous optimization approach for off-line blending and scheduling of oil-refinery operations [J]. Comput Chem Eng,2006,30(4):614-34.
[26]Luo C, Rong G. Hierarchical Approach for Short-Term Scheduling in Refineries [J]. Ind Eng Chem Res,2007,46(11):3656-68.
[27]Shah N, Saharidis G K D, Jia Z, Ierapetritou M G. Centralized-decentralized optimization for refinery scheduling [J]. Comput Chem Eng,2009,33(12):2091-105.
[28]Shah N K, Ierapetritou M G. Short-term scheduling of a large-scale oil-refinery operations:Incorporating logistics details [J]. AlChE J,2011,57(6):1570-84.
[29]Rejowski R, Pinto J M. Scheduling of a multiproduct pipeline system [J]. Comput Chem Eng,2003,27(8-9):1229-46.
[30]Cafaro D C, Cerda J. Optimal scheduling of multiproduct pipeline systems using a non-discrete MILP formulation [J]. Comput Chem Eng,2004,28(10):2053-68.
[31]Rejowski R, Pinto J M. Efficient MILP formulations and valid cuts for multiproduct pipeline scheduling [J]. Comput Chem Eng,2004,28(8):1511-28.
[32]Relvas S, Matos H A, Barbosa-Povoa A P F D, Fialho J, Pinheiro A S. Pipeline Scheduling and Inventory Management of a Multiproduct Distribution Oil System [J]. Ind Eng Chem Res,2006,45(23):7841-55.
[33]Cafaro D C, Cerda J. Efficient Tool for the Scheduling of Multiproduct Pipelines and Terminal Operations [J]. Ind Eng Chem Res,2008,47(24):9941-56.
[34]Cafaro D C, Cerda J. Dynamic scheduling of multiproduct pipelines with multiple delivery due dates [J]. Comput Chem Eng,2008,32(4-5):728-53.
[35]Relvas S, Matos H A, Barbosa-Povoa A P F D, Fialho J. Reactive Scheduling Framework for a Multiproduct Pipeline with Inventory Management [J]. Ind Eng Chem Res,2007,46(17):5659-72.
[36]Rejowski Jr R, Pinto J M. A novel continuous time representation for the scheduling of pipeline systems with pumping yield rate constraints [J]. Comput Chem Eng,2008,32(4-5):1042-66.
[37]MirHassani S A, Ghorbanalizadeh M. The multi-product pipeline scheduling system [J]. Comput Math Appl,2008,56(4):891-7.
[38]MirHassani S A, Fani Jahromi H. Scheduling multi-product tree-structure pipelines [J]. Comput Chem Eng,2011,35(1):165-76.
[39]Herran A, de la Cruz J M, de Andres B. A mathematical model for planning transportation of multiple petroleum products in a multi-pipeline system [J]. Comput Chem Eng,2010,34(3):401-13.
[40]Cafaro D C, Cerda J. Operational scheduling of refined products pipeline networks with simultaneous batch injections [J]. Comput Chem Eng,2010,34(10): 1687-704.
[41]Cafaro V G, Cafaro D C, Mendez C A, Cerda J. Detailed Scheduling of Operations in Single-Source Refined Products Pipelines [J]. Ind Eng Chem Res,2011, 50(10):6240-59.
[42]Cafaro D C, Cerda J. Rigorous scheduling of mesh-structure refined petroleum pipeline networks [J]. Comput Chem Eng,2012,38(0):185-203.
[43]Cafaro V G, Cafaro D C, Mendez C A, Cerda J. Detailed Scheduling of Single-Source Pipelines with Simultaneous Deliveries to Multiple Offtake Stations [J]. Ind Eng Chem Res,2012,
[44]Magatao L, Arruda L V R, Neves Jr F. A mixed integer programming approach for scheduling commodities in a pipeline [J]. Comput Chem Eng,2004,28(1-2):171-85.
[45]Boschetto S N, Magatao L, Brondani W M, Neves-Jr F v, Arruda L V R, Barbosa-Povoa A P F D, Relvas S. An Operational Scheduling Model to Product Distribution through a Pipeline Network [J]. Ind Eng Chem Res,2010,49(12):5661-82.
[46]Magatao S N B, Magatao L, Polli H L, Neves F, Arruda L V R, Relvas S, Povoa A B. Planning and sequencing product distribution in a real-world pipeline network: An MILP decomposition approach [J]. Ind Eng Chem Res,2012,
[47]闫强,陈毓川,王安建,王高尚,于汶加,陈其慎.我国新能源发展障碍与应对：全球现状评述[J].地球学报,2010,05)：759-67.
[48]Huber G W, Conna A. Synergies between Bio- and Oil Refineries for the Production of Fuels from Biomass [J]. Angew Chem Int Ed,2007,46(38):7184-201.
[49]DOE. Replacing the Whole Barrel to reduce U.S. Dependence on Oil, U.S. Department of Energy [M].2012.
[50]周中仁,吴文良.生物质能研究现状及展望[J].农业工程学报,2005,12)：12-5.
[51]Yue D, You F, Snyder S W. Biomass-to-Biofuel Supply Chain Optimization: Overview, Key Issues and Challenges [J]. Comput Chem Eng,2013,0):
[52]An H, Wilhelm W E, Searcy S W. Biofuel and petroleum-based fuel supply chain research:A literature review [J]. Biomass Bioenerg,2011,35(9):3763-74.
[53]Anex R P, Aden A, Kazi F K, Fortman J, Swanson R M, Wright M M, Satrio J A, Brown R C, Daugaard D E, Platon A, Kothandaraman G, Hsu D D, Dutta A. Techno-economic comparison of biomass-to-transportation fuels via pyrolysis, gasification, and biochemical pathways [J]. Fuel,2010,89(SUPPL.1):S29-S35.
[54]DOE. Biochemical conversion:Using hydrolysis, fermentation, and catalysis to make fuels and chemicals [M]//ENERGY U S D O.2011.
[55]DOE. Thermochemical conversion:Using heat and chemistry to make fuels and bioproducts [M]//ENERGY U S D O.2011.
[56]NABC, Advanced Biofuels Processing and Integration, National Advenced Biofuels Consortium [R/OL],2012, http://www.nabcprojects.org/biofuels.html
[57]Sokhansanj S, Kumar A, Turhollow A F. Development and implementation of integrated biomass supply analysis and logistics model (IBSAL) [J]. Biomass Bioenerg, 2006,30(10):838-47.
[58]Elia J A, Baliban R C, Xiao X, Floudas C A. Optimal energy supply network determination and life cycle analysis for hybrid coal, biomass, and natural gas to liquid (CBGTL) plants using carbon-based hydrogen production [J]. Comput Chem Eng,2011, 35(8):1399-430.
[59]Bowling I M, Ponce-Ortega J M a, El-Halwagi M M. Facility Location and Supply Chain Optimization for a Biorefinery [J]. Ind Eng Chem Res,2011,50(10): 6276-86.
[60]Dunnett A, Adjiman C, Shah N. A spatially explicit whole-system model of the lignocellulosic bioethanol supply chain:an assessment of decentralised processing potential [J]. Biotechnol Biofuels,2008,1(1):1-17.
[61]Zamboni A, Shah N, Bezzo F. Spatially Explicit Static Model for the Strategic Design of Future Bioethanol Production Systems.1. Cost Minimization [J]. Energy Fuels,2009,23(10):5121-33.
[62]Kim J, Realff M J, Lee J H, Whittaker C, Furtner L. Design of biomass processing network for biofuel production using an MILP model [J]. Biomass Bioenerg, 2011,35(2):853-71.
[63]You F, Tao L, Graziano D J, Snyder S W. Optimal design of sustainable cellulosic biofuel supply chains:Multiobjective optimization coupled with life cycle assessment and input-output analysis [J]. AlChE J,2012,58(4):1157-80.
[64]Giarola S, Zamboni A, Bezzo F. Spatially explicit multi-objective optimisation for design and planning of hybrid first and second generation biorefineries [J]. Comput Chem Eng,2011,35(9):1782-97.
[65]You F, Wang B. Life Cycle Optimization of Biomass-to-Liquid Supply Chains with Distributed-Centralized Processing Networks [J]. Ind Eng Chem Res,2011, 50(17):10102-27.
[66]Gebreslassie B H, Yao Y, You F. Design under uncertainty of hydrocarbon biorefinery supply chains:Multiobjective stochastic programming models, decomposition algorithm, and a Comparison between CVaR and downside risk [J]. AlChE J,2012,58(7):2155-79.
[67]Corsano G, Vecchietti A R, Montagna J M. Optimal design for sustainable bioethanol supply chain considering detailed plant performance model [J]. Comput Chem Eng,2011,35(8):1384-98.
[68]Akgul O, Shah N, Papageorgiou L G. Economic optimisation of a UK advanced biofuel supply chain [J]. Biomass Bioenerg,2012,41(0):57-72.
[69]Elia J A, Baliban R C, Floudas C A. Nationwide energy supply chain analysis for hybrid feedstock processes with significant CO2 emissions reduction [J]. AlChE J, 2012,58(7):2142-54.
[70]Tong K, Gleeson M J, Rong G, You F. Optimal design of advanced drop-in hydrocarbon biofuel supply chain integrating with existing petroleum refineries under uncertainty [J]. Biomass Bioenerg,2014,60(0):108-20.
[71]Tong K, You F, Rong G. Robust design and operations of hydrocarbon biofuel supply chain integrating with existing petroleum refineries considering unit cost objective [J]. Comput Chem Eng,2014,
[72]Tong K, Gong J, Yue D, You F. Stochastic Programming Approach to Optimal Design and Operations of Integrated Hydrocarbon Biofuel and Petroleum Supply Chains [J]. ACS Sustain Chem Eng,2014,2(1):49-61.
[73]Li Z, Ierapetritou M. Process scheduling under uncertainty:Review and challenges [J]. Comput Chem Eng,2008,32(4-5):715-27.
[74]Tsiakis P, Shah N, Pantelides C C. Design of Multi-echelon Supply Chain Networks under Demand Uncertainty [J]. Ind Eng Chem Res,2001,40(16):3585-604.
[75]Dempster M A H, Pedron N H, Medova E, Scott J, Sembos A. Planning logistics operations in the oil industry [J]. J Oper Res Soc,2000,1271-88.
[76]Li W, Hui C-W, Li P, Li A-X. Refinery Planning under Uncertainty [J]. Ind Eng Chem Res,2004,43(21):6742-55.
[77]Luo C, Rong G. A Strategy for the Integration of Production Planning and Scheduling in Refineries under Uncertainty [J]. Chin J Chem Eng,2009,17(1):113-27.
[78]Gu H, Rong G. A Two-stage Discriminating Framework for Making Supply Chain Decisions under Uncertainties [J]. Chem Biochem Eng Q,2010,24(1):P51-6. [79] Lababidi H M S, Ahmed M A, Alatiqi I M, Al-Enzi A F. Optimizing the Supply
Chain of a Petrochemical Company under Uncertain Operating and Economic Conditions [J]. Ind Eng Chem Res,2003,43(1):63-73.
[80]Khor C S, Elkamel A, Ponnambalam K, Douglas P L. Two-stage stochastic programming with fixed recourse via scenario planning with economic and operational risk management for petroleum refinery planning under uncertainty [J]. Chem Eng Process,2008,47(9-10):1744-64.
[81]Yang J, Gu H, Rong G. Supply Chain Optimization for Refinery with Considerations of Operation Mode Changeover and Yield Fluctuations [J]. Ind Eng Chem Res,2009,49(1):276-87.
[82]Wang J, Rong G. Robust Optimization Model for Crude Oil Scheduling under Uncertainty [J]. Ind Eng Chem Res,2009,49(4):1737-48.
[83]Huchzermeier A, Cohen M A. Valuing operational flexibility under exchange rate risk [J]. Oper Res,1996,44(1):100-13.
[84]Kouvelis P, Rosenblatt M J. A mathematical programming model for global supply chain management:Conceptual approach and managerial insights [M]. Supply chain management:Models, applications, and research directions. Springer.2005:245-77.
[85]Kim J, Realff M J, Lee J H. Optimal design and global sensitivity analysis of biomass supply chain networks for biofuels under uncertainty [J]. Comput Chem Eng, 2011,35(9):1738-51.
[86]Dal-Mas M, Giarola S, Zamboni A, Bezzo F. Strategic design and investment capacity planning of the ethanol supply chain under price uncertainty [J]. Biomass Bioenerg,2011,35(5):2059-71.
[87]Alex Marvin W, Schmidt L D, Benjaafar S, Tiffany D G, Daoutidis P. Economic optimization of a lignocellulosic biomass-to-ethanol supply chain [J]. Chem Eng Sci, 2012,67(1):68-79.
[88]Birge J R, Louveaux F V. Introduction to stochastic programming [M]. New York:Springer,1997.
[89]Ben-Tal A, Nemirovski A. Robust optimization-methodology and applications [J]. Math Program,2002,92(3):453-80.
[90]Charnes A, Cooper W W. Chance-Constrained Programming [J]. Manage Sci, 1959,6(1):73-9.
[91]Zimmermann H J. Fuzzy set theory--and its applications [M]. Springer,2001.
[92]You F, Wassick J M, Grossmann I E. Risk management for a global supply chain planning under uncertainty:Models and algorithms [J]. AlChE J,2009,55(4): 931-46.
[93]Sahinidis N V. Optimization under uncertainty:state-of-the-art and opportunities [J]. Comput Chem Eng,2004,28(6-7):971-83.
[94]Barbaro A, Bagajewicz M J. Managing financial risk in planning under uncertainty [J]. AlChE J,2004,50(5):963-89.
[95]Rockafellar R T. Optimization of Conditional Value at Risk [J]. J Risk,2000, 2(21-41.
[96]Rockafellar R T, Uryasev S. Conditional value-at-risk for general loss distributions [J]. J Bank Financ,2002,26(7):1443-71.
[97]Dakin R J. A tree-search algorithm for mixed integer programming problems [J]. Comput J,1965,8(3):250-5.
[98]Stubbs R A, Mehrotra S. A branch-and-cut method for 0-1 mixed convex programming [J]. Math Program,1999,86(3):515-32.
[99]Kelley J, James E. The cutting-plane method for solving convex programs [J]. J Soc Ind Appl Math,1960,8(4):703-12.
[100]CPLEX I B M I.12.2 User's Manual [M]. IBM.2010.
[101]Ashford R, Daniel R. LP-Model:Xpress-LP's model builder [J]. IMA Journal of Management Mathematics,1986,1(3):163-76.
[102]Optimization G. Gurobi optimizer reference manual [J]. URL: http://www.gurobi.com,2012,
[103]Benders J F. Partitioning procedures for solving mixed-variables programming problems [J]. Numerische mathematik,1962,4(1):238-52.
[104]Guignard M, Kim S. Lagrangean decomposition:a model yielding stronger Lagrangean bounds [J]. Math Program,1987,39(2):215-28.
[105]Iyer R R, Grossmann I E. A Bilevel Decomposition Algorithm for Long-Range Planning of Process Networks [J]. Ind Eng Chem Res,1998,37(2):474-81.
[106]You F, Grossmann I E. Multicut Benders decomposition algorithm for process supply chain planning under uncertainty [J]. Ann Oper Res,2013,210(1):191-211.
[107]Fisher M L. The Lagrangian relaxation method for solving integer programming problems [J]. Manage Sci,1981,27(1):1-18.
[108]Chu Y, You F. Integration of production scheduling and dynamic optimization for multi-product CSTRs:Generalized Benders decomposition coupled with global mixed-integer fractional programming [J]. Comput Chem Eng,2013,58(0):315-33.
[109]Chu Y, You F. Integration of Scheduling and Dynamic Optimization of Batch Processes under Uncertainty:Two-Stage Stochastic Programming Approach and Enhanced Generalized Benders Decomposition Algorithm [J]. Ind Eng Chem Res,2013, 52(47):16851-69.
[110]Chu Y, You F. Integrated Scheduling and Dynamic Optimization of Complex Batch Processes with General Network Structure Using a Generalized Benders Decomposition Approach [J]. Ind Eng Chem Res,2013,52(23):7867-85.
[111]van den Heever S A, Grossmann I E, Vasantharajan S, Edwards K. A Lagrangean Decomposition Heuristic for the Design and Planning of Offshore Hydrocarbon Field Infrastructures with Complex Economic Objectives [J]. Ind Eng Chem Res,2001,40(13):2857-75.
[112]Jackson J R, Grossmann I E. Temporal decomposition scheme for nonlinear multisite production planning and distribution models [J]. Ind Eng Chem Res,2003, 42(13):3045-55.
[113]You F, Grossmann I E. Mixed-Integer Nonlinear Programming Models and Algorithms for Large-Scale Supply Chain Design with Stochastic Inventory Management [J]. Ind Eng Chem Res,2008,47(20):7802-17.
[114]You F, Grossmann I E. Integrated multi-echelon supply chain design with inventories under uncertainty:MINLP models, computational strategies [J]. AlChE J, 2010,56(2):419-40.
[115]Terrazas-Moreno S, Trotter P A, Grossmann I E. Temporal and spatial Lagrangean decompositions in multi-site, multi-period production planning problems with sequence-dependent changeovers [J]. Comput Chem Eng,2011,35(12):2913-28.
[116]You F, Grossmann I E, Wassick J M. Multisite Capacity, Production, and Distribution Planning with Reactor Modifications:MILP Model, Bilevel Decomposition Algorithm versus Lagrangean Decomposition Scheme [J]. Ind Eng Chem Res,2010,50(9):4831-49.
[117]Terrazas-Moreno S, Grossmann I E. A multiscale decomposition method for the optimal planning and scheduling of multi-site continuous multiproduct plants [J]. Chem Eng Sci,2011,66(19):4307-18.
[118]Khor C S, Elkamel A, Douglas P L. Stochastic refinery planning with risk management [J]. Pet Sci Technol,2008,26(14):1726-40.
[119]Jonsbraten T W. Optimization models for petroleum field exploitation [D]. Norway; Stavanger College,1998d.
[120]Jonsbraten T, Wets R, Woodruff D. A class of stochastic programs withdecision dependent random elements [J]. Ann Oper Res,1998,82(83-106.
[121]Goel V, Grossmann I E, El-Bakry A S, Mulkay E L. A novel branch and bound algorithm for optimal development of gas fields under uncertainty in reserves [J]. Comput Chem Eng,2006,30(6-7):1076-92.
[122]Balasubramanian J, Grossmann I E. Approximation to Multistage Stochastic Optimization in Multiperiod Batch Plant Scheduling under Demand Uncertainty [J]. Ind Eng Chem Res,2004,43(14):3695-713.
[123]Tarhan B, Grossmann I E. A multistage stochastic programming approach with strategies for uncertainty reduction in the synthesis of process networks with uncertain yields [J]. Comput Chem Eng,2008,32(4-5):766-88.
[124]Gupta V, Grossmann I E. Solution strategies for multistage stochastic programming with endogenous uncertainties [J]. Comput Chem Eng,2011,35(11): 2235-47.
[125]Mulvey J M, Vanderbei R J, Zenios S A. Robust Optimization of Large-Scale Systems [J]. Oper Res,1995,43(2):264-81.
[126]Ahmed S, Sahinidis N V. Robust Process Planning under Uncertainty [J]. Ind Eng Chem Res,1998,37(5):1883-92.
[127]Eppen G D, Martin R K, Schrage L. A Scenario Approach to Capacity Planning [J]. Oper Res,1989,37(4):517-27.
[128]Verderame P M, Floudas C A. Operational Planning of Large-Scale Industrial Batch Plants under Demand Due Date and Amount Uncertainty:Ⅱ. Conditional Value-at-Risk Framework [J]. Ind Eng Chem Res,2009,49(1):260-75.
[129]Carneiro M C, Ribas G P, Hamacher S. Risk Management in the Oil Supply Chain:A CVaR Approach [J]. Ind Eng Chem Res,2010,49(7):3286-94.
[130]Verderame P M, Floudas C A. Multisite Planning under Demand and Transportation Time Uncertainty:Robust Optimization and Conditional Value-at-Risk Frameworks [J]. Ind Eng Chem Res,2010,50(9):4959-82.
[131]Verderame P M, Floudas C A. Integration of Operational Planning and Medium-Term Scheduling for Large-Scale Industrial Batch Plants under Demand and Processing Time Uncertainty [J]. Ind Eng Chem Res,2010,49(10):4948-65.
[132]Shapiro A, Homem-de-Mello T. A simulation-based approach to two-stage stochastic programming with recourse [J]. Math Program,1998,81(3):301-25.
[133]Law A M, Kelton W D. simulation modeling & analysis [M].3rd ed. New York: McGraw-Hill,2000.
[134]Mele F D, Guillen G, Espuna A, Puigjaner L. A Simulation-Based Optimization Framework for Parameter Optimization of Supply-Chain Networks [J]. Ind Eng Chem Res,2006,45(9):3133-48.
[135]Karuppiah R, Martin M, Grossmann I E. A simple heuristic for reducing the number of scenarios in two-stage stochastic programming [J]. Comput Chem Eng,2010, 34(8):1246-55.
[136]Wang W, Ahmed S. Sample average approximation of expected value constrained stochastic programs [J]. Oper Res Lett,2008,36(5):515-9.
[137]Grinstead C M, Snell J L. Introduction to Probability [M].2nd ed. RI: Providence,1997.
[138]Solak S. Efficient solution procedures for multistage stochastic formualtions of two problem classes [D]; Georgia Institute of Technology,2007.
[139]Grossmann I. Enterprise-wide optimization:A new frontier in process systems engineering [J]. AlChE J,2005,51(7):1846-57.
[140]Floudas C A, Lin X. Continuous-time versus discrete-time approaches for scheduling of chemical processes:a review [J]. Comput Chem Eng,2004,28(11):2109-29.
[141]Jia Z, Ierapetritou M, Kelly J D. Refinery Short-Term Scheduling Using Continuous Time Formulation:Crude-Oil Operations [J]. Ind Eng Chem Res,2003, 42(13):3085-97.
[142]Maravelias C T, Sung C. Integration of production planning and scheduling: Overview, challenges and opportunities [J]. Comput Chem Eng,2009,33(12):1919-
[143]Li Z, Ierapetritou M G. Integrated production planning and scheduling using a decomposition framework [J]. Chem Eng Sci,2009,64(16):3585-97.
[144]Nie Y, Biegler L T, Wassick J M. Integrated scheduling and dynamic optimization of batch processes using state equipment networks [J]. AlChE J,2012, 58(11):3416-32.
[145]Guyonnet P, Grant F H, Bagajewicz M J. Integrated Model for Refinery Planning, Oil Procuring, and Product Distribution [J]. Ind Eng Chem Res,2008,48(1): 463-82.
[146]Kuo T-H, Chang C-T. Application of a Mathematic Programming Model for Integrated Planning and Scheduling of Petroleum Supply Networks [J]. Ind Eng Chem Res,2008,47(6):1935-54.
[147]杨佳丽,荣冈.结合质量传递及约束的炼油生产计划优化[J].化工学报,2010,01)：124-31.
[148]Jones W, Paddock K. Transport by pipeline [J]. Mod Petrol Technol,1982,
[149]DOE. Biomass program multi-year program plan [M]//ENERGY U S D O. 2013.
[150]DOE. U.S. Billion-Ton Update:Biomass Supply for a Bioenergy and Bioproducts Industry, U.S. Department of Energy [M].2011.
[151]Liu B, Iwamura K. Chance constrained programming with fuzzy parameters [J]. Fuzzy Set Syst,1998,94(2):227-37.
[152]Zadeh L A. Fuzzy sets [J]. Inform Control,1965,8(3):338-53.
[153]Baoding L, Kakuzo I. A note on chance constrained programming with fuzzy coefficients [J]. Fuzzy Set Syst,1998,100(1-3):229-33.
[154]Tan R R, Aviso K B, Barilea I U, Culaba A B, Cruz Jr J B. A fuzzy multi-regional input-output optimization model for biomass production and trade under resource and footprint constraints [J]. Appl Energ,2012,90(1):154-60.
[155]Tay D H S, Ng D K S, Sammons N E, Eden M R. Fuzzy Optimization Approach for the Synthesis of a Sustainable Integrated Biorefinery [J]. Ind Eng Chem Res,2011, 50(3):1652-65.
[156]Wright M M, Daugaard D E, Satrio J A, Brown R C. Techno-economic analysis of biomass fast pyrolysis to transportation fuels [J]. Fuel,2010,89(S2-S10.
[157]Gebreslassie B H, Slivinsky M, Wang B, You F. Life cycle optimization for sustainable design and operations of hydrocarbon biorefinery via fast pyrolysis, hydrotreating and hydrocracking [J]. Comput Chem Eng,2013,50(0):71-91.
[158]Wang B, Gebreslassie B H, You F. Sustainable Design and Synthesis of Hydrocarbon Biorefinery via Gasification Pathway:Integrated Life Cycle Assessment and Technoeconomic Analysis with Multiobjective Superstructure Optimization [J]. Comput Chem Eng,2013,52(0):55-76.
[159]Wright M M, Brown R C, Boateng A A. Distributed processing of biomass to bio-oil for subsequent production of Fischer-Tropsch liquids [J]. Biofuel Bioprod Bior, 2008,2(3):229-38.
[160]Zadeh L A. Fuzzy sets as a basis for a theory of possibility [J]. Fuzzy Set Syst, 1999,100, Supplement 1(0):9-34.
[161]Inuiguchi M, Ramik J. Possibilistic linear programming:a brief review of fuzzy mathematical programming and a comparison with stochastic programming in portfolio selection problem [J]. Fuzzy Set Syst,2000,111(1):3-28.
[162]NREL, [R/OL], U.S. National Renewable Energy Laboratory,2012, http://www.nrel.gov/gis/data_biomass.html
[163]Gentry B, Blankinship D, Wainwright E. Oracle crystal ball user manual.11.1. Denver, USA:Orcale [M]. Inc.2008.
[164]EIA, Energy Information Administrator [R/OL],2012, http://www.eia.gov/
[165]Wikipedia, List of Airport in Illinois [R/OL],2012, http://en.wikixpedia.org/wiki/List of airports in Illinois
[166]Rick, Longitude and Latitude Calculations [R/OL],1999, http://mathforum.org/library/drmath/view/51879.html
[167]Pootakham T, Kumar A. Bio-oil transport by pipeline:A techno-economic assessment [J]. Bioresour Technol,2010,101(18):7137-43.
[168]Rosenthal R. GAMS:A User's Manual [J]. GAMS Development Corporation, Washington, DC,2012,
[169]Yue D, Kim M A, You F. Design of Sustainable Product Systems and Supply Chains with Life Cycle Optimization Based on Functional Unit:General Modeling Framework, Mixed-Integer Nonlinear Programming Algorithms and Case Study on Hydrocarbon Biofuels [J]. ACS Sustain Chem Eng,2013,1(8):1003-14.
[170]Zhong Z, You F. Globally convergent exact and inexact parametric algorithms for solving large-scale mixed-integer fractional programs and applications in process systems engineering [J]. Comput Chem Eng,2014,61(0):90-101.
[171]Yue D, Guillen-Gosalbez G, You F. Global optimization of large-scale mixed-integer linear fractional programming problems:A reformulation-linearization method and process scheduling applications [J]. AlChE J,2013,59(11):4255-72.
[172]You F, Castro P M, Grossmann I E. Dinkelbach's algorithm as an efficient method to solve a class of MINLP models for large-scale cyclic scheduling problems [J]. Comput Chem Eng,2009,33(11):1879-89.
[173]Yue D, You F. Sustainable scheduling of batch processes under economic and environmental criteria with MINLP models and algorithms [J]. Comput Chem Eng, 2013,54(0):44-59.
[174]Marvin W A, Schmidt L D, Daoutidis P. Biorefinery Location and Technology Selection Through Supply Chain Optimization [J]. Ind Eng Chem Res,2012,52(9): 3192-208.
[175]Giarola S, Bezzo F, Shah N. A risk management approach to the economic and environmental strategic design of ethanol supply chains [J]. Biomass Bioenerg,2013, 58(0):31-51.
[176]Bertsimas D, Sim M. Robust discrete optimization and network flows [J]. Math Program,2003,98(1-3):49-71.
[177]Zhang Q, Gong J, Skwarczek M, Yue D, You F. Sustainable process design and synthesis of hydrocarbon biorefinery through fast pyrolysis and hydroprocessing [J]. AlChE J,2014,60(3):980-94.
[178]Duran M A, Grossmann I E. An outer-approximation algorithm for a class of mixed-integer nonlinear programs [J]. Math Program,1986,36(3):307-39.
[179]Bussieck M R, Drud A. SBB:A new solver for mixed integer nonlinear programming [J]. Recent advances in nonlinear mixed integer optimization, INFORMS Fall, Invited talk,2001,
[180]Sahinidis N V. BARON:A general purpose global optimization software package [J]. J Global Optim,1996,8(2):201-5.
[181]Glover F. Improved linear integer programming formulations of nonlinear integer problems [J]. Manage Sci,1975,22(4):455-60.
[182]Li Z, Ierapetritou M G. Robust Optimization for Process Scheduling Under Uncertainty [J]. Ind Eng Chem Res,2008,47(12):4148-57.
[183]Soyster A L. Technical Note-Convex Programming with Set-Inclusive Constraints and Applications to Inexact Linear Programming [J]. Oper Res,1973,21 (5): 1154-7.
[184]Ben-Tal A, Nemirovski A. Robust solutions of uncertain linear programs [J]. Oper Res Lett,1999,25(1):1-13.
[185]Al-Qahtani K Y, Elkamel A. Planning and Integration of Refinery and Petrochemical Operations [M]. Wiley, com,2011.
[186]Marker T:UOP LLC,2005.
[187]Jones S B, Valkenburg C, Walton C W, Elliott D C, Holladay J E, Stevens D J, Kinchin C, Czernik S. Production of gasoline and diesel from biomass via fast pyrolysis, hydrotreating and hydrocracking:a design case [M]. Pacific Northwest National Laboratory Richland, WA,2009.
[188]Tong K, Yiping F, Gang R. Integrated Model for Refinery Production and Pipeline System Scheduling [C].Proceedings of the 22th European Symposium of Computer Aided Process Engineering, London, UK,2012.
[189]Tong K, Feng Y, Rong G. Planning under Demand and Yield Uncertainties in an Oil Supply Chain [J]. Ind Eng Chem Res,2011,51(2):814-34.
[190]DOE, U.S. Department of Energy [R/OL],2012, http://www.afdc.energy.gov/laws/
[191]U.S. Department of Agriculture Statistics [R/OL],2012, http://www.nass.usda.gov/
[192]EISA. Energy Independence and Security Act of 2007 [M]. Public Law 110-140,110th Congress.2007.
[193]赵小强.炼厂生产调度问题研究[D]；浙江大学,2005.
[194]罗春鹏.炼油企业生产调度研究[D]；浙江大学,2008.
[195]顾海杰.不确定性条件下炼油厂供应链优化问题研究[D]；浙江大学,2009.
[196]王继帅.带有不确定性的石化企业供应链优化研究[D]；浙江大学,2011.
[197]Martin M, Grossmann I E. Optimal use of hybrid feedstock, switchgrass and shale gas for the simultaneous production of hydrogen and liquid fuels [J]. Energy, 2013,55(0):378-91.
[198]Cafaro D C, Grossmann I E. Alternate approximation of concave cost functions for process design and supply chain optimization problems [J]. Comput Chem Eng, 2014,60(0):376-80.