Research On Supply Chain Optimization Under Uncertainty In Petrochemical Industry

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.