Research On Supply Chain Optimization Under Uncertainty In Petroleum And Biofuel Supply Chain

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.