带有不确定性的石化企业供应链优化研究
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摘要
由于实际工况中不确定性广泛存在于石化企业供应链的各个层次,因此研究不确定性条件下的供应链优化问题对供应链管理理论探讨和实际应用都有重大的意义。本文在综述了石化企业供应链各个层次管理研究现状的基础上,提出了一种通用的集成鲁棒优化方法,对其在供应链设计、计划优化、调度优化等多方面的应用做了深入研究,同时,以智能工厂集成优化平台为统一案例,对各个层次的优化效果做了测试。在这一主线上,本文的主要研究内容和创新点如下：
(1)提出了一种处理不确定性的集成鲁棒优化方法,为两阶段随机规划形式,第一阶段能够将不确定问题转行为确定形式,第二阶段基于场景。整个模型在有限的场景内既能够处理具有离散分布的不确定性参数,也能够处理具有连续分布的不确定性参数,同时具有鲁棒特性,通过调节目标函数的权重参数可以达到一定的折中鲁棒效果。
(2)提出了一种带有风险约束的石化企业供应链设计方法,能够应对需求不确定性带来的影响。首先建立了不确定条件下的供应链设计模型,并在此基础上加入了风险约束,最后将多目标优化问题转化为单目标优化问题,形成了确定性的两阶段随机规划模型。
(3)针对石化企业计划的不确定性,提出了一种多周期、多产品的中等周期计划优化模型。基于离散时间建模方法,利用模糊可能性方法将不确定性问题转化为确定性问题,建立了混合整数线性规划模型。同时采用模型预测控制思想,利用模型的动态属性为决策者提供了不同满意度下的决策序列。
(4)利用集成鲁棒方法,对原油调度问题进行了建模和分析。不确定性出现在原油到港时间和常减压装置加工量不确定。针对这两种不确定性,分别采取了集成鲁棒方法的相应部分。同时,对目标函数中的参数对于模型鲁棒性和解鲁棒性的影响做了分析和折中。
(5)提出了一种基于离散时间的混合整数线性规划模型,能够解决不确定条件下石化产品多管道系统的优化问题。利用条件风险价值方法(conditional value-at-risk, CVAR)对带有不确定性的多管道优化问题进行了分析。首先在现存模型的基础上做了改进,改善了计算效率。然后采用了CVAR框架扩充模型为基于场景的两阶段随机规划。通过案例,证明了此方法的可行性和有效性。
最后在总结全文的基础上,提出了不确定条件下石化企业供应链优化有待深入研究的几个问题。
Decision making for supply chain optimization under uncertainties in petrochemical industrial is significant to supply chain management both in terms of theory and practice, since uncertainties are in multi-stages within a supply chain. This dissertation mainly focuses on supply chain design, planning and scheduling based on the review of supply chain management:first, we introduced an integrated robust optimization method to cope with uncertainties; second, the effectiveness of the proposed method is tested in the aboved mentioned aspects through an Inplant. The details are listed as follows:
(1) A two-stage robust model is proposed to solve supply chain optimization problem under uncertain conditions. The first-stage of the model is developed using chance constrained programming and fuzzy programming which can be transformed into deterministic counterpart problem, while the second-stage is scenario-based. Through the combination of the approaches, the two-stage model can deal with uncertain parameters with both continuous and discrete probability distributions within a finite number of scenarios. The robustness could be changed through parameters in the objective function.
(2) The financial risk management in the design of multiproduct, multi-echelon supply chain networks in petrochemical industry is discussed. A model dealing with uncertainty and financial risk management constraints are first introduced. The model is established in the framework of deterministic two-stage stochastic programming. Based on a case from a petrochemical corporation, the financial risk is analyzed, and the ability to manage it is also discussed.
(3) A multi-period, multi-product planning optimization under uncertainty is analysed. Based on the discrete-time modeling method, we propose a mixed integer linear programming (MILP) model, in which the nonlinear part is converted to linear problem using fuzzy possibility method. Meanwhile, the usefulness of MPC as a tactical decision policy is integrated to the model.
(4) The integrated robust optimization model is applied to solve the crude oil scheduling optimization problem under uncertain conditions. Uncertainties are introduced in ship arrival time and fluctuating product demand. Computational results demonstrate the effectiveness and robustness of the proposed approach. The trade-off between solution robustness and model robustness is also analyzed.
(5) A discrete mathematical approach to solve scheduling of multi-pipeline systems for refined products under demand uncertainty is presented. To deal with the uncertainty, the conditional value-at-risk (CVAR) analysis is adopted as a risk measure. Firstly an improved scheduling approach is proposed to model the multi-pipeline system with the consideration of different pipeline segment sizes. The improved formulation tends to generate problems that are computationally intractable when pipeline segments have distinct capacities. Then the model took the robust model to cope with uncertainty in a CVAR framework. Computational results are presented to demonstrate the effectiveness of the proposed approach through several case studies.
At the end of this dissertation, promising future researches on supply chain optimization under uncertainties in petrochemical industry are introduced based on the conclusion of this dissertation.
(1)提出了一种处理不确定性的集成鲁棒优化方法,为两阶段随机规划形式,第一阶段能够将不确定问题转行为确定形式,第二阶段基于场景。整个模型在有限的场景内既能够处理具有离散分布的不确定性参数,也能够处理具有连续分布的不确定性参数,同时具有鲁棒特性,通过调节目标函数的权重参数可以达到一定的折中鲁棒效果。
(2)提出了一种带有风险约束的石化企业供应链设计方法,能够应对需求不确定性带来的影响。首先建立了不确定条件下的供应链设计模型,并在此基础上加入了风险约束,最后将多目标优化问题转化为单目标优化问题,形成了确定性的两阶段随机规划模型。
(3)针对石化企业计划的不确定性,提出了一种多周期、多产品的中等周期计划优化模型。基于离散时间建模方法,利用模糊可能性方法将不确定性问题转化为确定性问题,建立了混合整数线性规划模型。同时采用模型预测控制思想,利用模型的动态属性为决策者提供了不同满意度下的决策序列。
(4)利用集成鲁棒方法,对原油调度问题进行了建模和分析。不确定性出现在原油到港时间和常减压装置加工量不确定。针对这两种不确定性,分别采取了集成鲁棒方法的相应部分。同时,对目标函数中的参数对于模型鲁棒性和解鲁棒性的影响做了分析和折中。
(5)提出了一种基于离散时间的混合整数线性规划模型,能够解决不确定条件下石化产品多管道系统的优化问题。利用条件风险价值方法(conditional value-at-risk, CVAR)对带有不确定性的多管道优化问题进行了分析。首先在现存模型的基础上做了改进,改善了计算效率。然后采用了CVAR框架扩充模型为基于场景的两阶段随机规划。通过案例,证明了此方法的可行性和有效性。
最后在总结全文的基础上,提出了不确定条件下石化企业供应链优化有待深入研究的几个问题。
Decision making for supply chain optimization under uncertainties in petrochemical industrial is significant to supply chain management both in terms of theory and practice, since uncertainties are in multi-stages within a supply chain. This dissertation mainly focuses on supply chain design, planning and scheduling based on the review of supply chain management:first, we introduced an integrated robust optimization method to cope with uncertainties; second, the effectiveness of the proposed method is tested in the aboved mentioned aspects through an Inplant. The details are listed as follows:
(1) A two-stage robust model is proposed to solve supply chain optimization problem under uncertain conditions. The first-stage of the model is developed using chance constrained programming and fuzzy programming which can be transformed into deterministic counterpart problem, while the second-stage is scenario-based. Through the combination of the approaches, the two-stage model can deal with uncertain parameters with both continuous and discrete probability distributions within a finite number of scenarios. The robustness could be changed through parameters in the objective function.
(2) The financial risk management in the design of multiproduct, multi-echelon supply chain networks in petrochemical industry is discussed. A model dealing with uncertainty and financial risk management constraints are first introduced. The model is established in the framework of deterministic two-stage stochastic programming. Based on a case from a petrochemical corporation, the financial risk is analyzed, and the ability to manage it is also discussed.
(3) A multi-period, multi-product planning optimization under uncertainty is analysed. Based on the discrete-time modeling method, we propose a mixed integer linear programming (MILP) model, in which the nonlinear part is converted to linear problem using fuzzy possibility method. Meanwhile, the usefulness of MPC as a tactical decision policy is integrated to the model.
(4) The integrated robust optimization model is applied to solve the crude oil scheduling optimization problem under uncertain conditions. Uncertainties are introduced in ship arrival time and fluctuating product demand. Computational results demonstrate the effectiveness and robustness of the proposed approach. The trade-off between solution robustness and model robustness is also analyzed.
(5) A discrete mathematical approach to solve scheduling of multi-pipeline systems for refined products under demand uncertainty is presented. To deal with the uncertainty, the conditional value-at-risk (CVAR) analysis is adopted as a risk measure. Firstly an improved scheduling approach is proposed to model the multi-pipeline system with the consideration of different pipeline segment sizes. The improved formulation tends to generate problems that are computationally intractable when pipeline segments have distinct capacities. Then the model took the robust model to cope with uncertainty in a CVAR framework. Computational results are presented to demonstrate the effectiveness of the proposed approach through several case studies.
At the end of this dissertation, promising future researches on supply chain optimization under uncertainties in petrochemical industry are introduced based on the conclusion of this dissertation.
引文
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