Financial Risk Assessment and Optimal Planning of Biofuels Supply Chains under Uncertainty (original) (raw)
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Stochastic design of biorefinery supply chains considering economic and environmental objectives
Journal of Cleaner Production, 2016
Biomass is a renewable resource that has attractive characteristics for energy production, but the corresponding supply chain could be subject of several uncertain factors that can affect drastically the optimal configuration, and those have not been properly accounted in previous publications. Therefore, this work presents a new approach for the optimal planning under uncertainty for a biomass conversion system involving simultaneously economic and environmental issues. The environmental impact was measured via the Eco-indicator99 method and the economic aspect was determined through the net annual profit. The proposed method considered the uncertainty involved in the raw material price by the stochastic generation of scenarios using the Latin Hypercube method followed by the implementation of the Monte-Carlo method, where a deterministic optimization problem was solved for each single scenario to select the structure of the more robust supply chain relying on statistical data. The proposed approach was applied to a case study for a distributed biorefinery system in Mexico. The results showed that the behavior of the profit values for the stochastic case is not associated to the behavior of the raw material price; also, it is possible to observe that that the supply chain topology could be affected for the uncertainty in the raw material price; however, the environmental and economic objectives did not present significant changes.
Multistage optimization of the supply chains of biofuels
Transportation Research Part E: Logistics and Transportation Review, 2010
In this study, a mathematical model that integrates spatial and temporal dimensions is developed for strategic planning of future bioethanol supply chain systems. The planning objective is to minimize the cost of the entire supply chain of biofuel from biowaste feedstock fields to end users over the entire planning horizon, simultaneously satisfying demand, resource, and technology constraints. This model is used to evaluate the economic potential and infrastructure requirements for bioethanol production from eight waste biomass resources in California as a case study. It is found that, through careful supply chain design, biowaste-based ethanol production can be sustained at a compatible cost around $1.1 per gallon.
2020
Biomass to Biofuel Supply Chain Design and Planning under Uncertainty: Concepts and Quantitative Methods explores the design and optimization of biomass-to-biofuel supply chains for commercial-scale implementation of biofuel projects by considering the problems and challenges encountered in real supply chains. By offering a fresh approach and discussing a wide range of quantitative methods, the book enables researchers and practitioners to develop hybrid methods that integrate the advantages and features of two or more methods in one decision-making framework for the efficient optimization of biofuel supply chains, especially for complex supply chain models. Combining supply chain management and modeling techniques in a single volume, the book is beneficial for graduate students who no longer need to consult subject-specific books alongside mathematical modeling textbooks. The book consists of two main parts. The first part describes the key components of biofuel supply chains, including biomass production, harvesting, collection, storage, preprocessing, conversion, transportation, and distribution. It also provides a comprehensive review of the concepts, problems, and opportunities associated with biofuel supply chains, such as types and properties of the feedstocks and fuel products, decision-making levels, sustainability concepts, uncertainty analysis and risk management, as well as integration of biomass supply chain with other supply chains. The second part focuses on modeling and optimization of biomass-to-biofuel supply chains under uncertainty, using different quantitative methods to determine optimal design.
Design and planning of infrastructures for bioethanol and sugar production under demand uncertainty
Chemical Engineering Research and Design, 2012
In this paper, we address the strategic planning of integrated bioethanol-sugar supply chains (SC) under uncertainty in the demand. The design task is formulated as a multi-scenario mixed-integer linear programming (MILP) problem that decides on the capacity expansions of the production and storage facilities of the network over time along with the associated planning decisions (i.e., production rates, sales, etc.). The MILP model seeks to optimize the expected performance of the SC under several financial risk mitigation options. This consideration gives a rise to a multi-objective formulation, whose solution is given by a set of network designs that respond in different ways to the actual realization of the demand (the uncertain parameter). The capabilities of our approach are demonstrated through a case study based on the Argentinean sugarcane industry. Results include the investment strategy for the optimal SC configuration along with an analysis of the effect of demand uncertainty on the economic performance of several biofuels SC structures.
Development and implementation of an optimisation model for biofuels supply chain
Biofuels supply chain comprises a wide set of activities involving a rather complex set of parameters. Cultivation of the raw materials is closely related to the agricultural sector whereas the production of the final product presumes the operation of a conversion plant. The distribution network aims at delivering the final product close to the consumption. The extent of the involvement of each one of the previously mentioned sectors is the result of strategic and operational planning of the whole supply chain and, in the general case, determines the efficiency of the biofuels sector. Taking also into account the very rapidly changing opinions related to the environmental behaviour of the whole biofuels supply chain, it becomes very clear that the parameters in the sector are continuously changing. Therefore, the consideration of an integrated supply chain appropriately modelled is believed to be very critical and could result in the optimal solution per case, economically and/or environmentally speaking. In this paper the development of a mathematical model for the optimal design and operation of Biofuels Supply Chain is proposed as an integrated approach that can take into account both technical and economic parameters affecting the performance of the whole value chain. Model implementation would facilitate and support the decision taking in various planning and operational issues such as infrastructure investments, the quantities of raw materials to be cultivated, the quantities of biofuels to be produced in the domestic market or imported, identifying the best available solution for the optimal design and operation of the biofuels supply chain.
Acs Sustainable Chemistry & Engineering, 2014
This paper addresses the optimal design and strategic planning of the integrated biofuel and petroleum supply chain system in the presence of pricing and quantity uncertainties. The drop-in properties of advanced hydrocarbon biofuels pose considerable potential for biofuel supply chains to leverage the existing production and distribution infrastructures of petroleum supply chains, which may lead to significant capital savings. 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 a stochastic programming approach to optimize the expectation under a number of scenarios associated with biomass availability, fuel demand, crude oil prices, and technology evolution. The integrated model is formulated as a stochastic mixed-integer linear program, which is illustrated by a case study involving 21 harvesting sites, 7 potential preconversion facilities, 6 potential integrated biorefineries, 2 petroleum refineries, and 39 demand zones. Results show the market share of biofuels increases gradually due to the increasing crude oil price and biomass availability.
Bioresource technology, 2015
The aim of this work was to develop a model for optimizing the life cycle cost of biofuel supply chain under uncertainties. Multiple agriculture zones, multiple transportation modes for the transport of grain and biofuel, multiple biofuel plants, and multiple market centers were considered in this model, and the price of the resources, the yield of grain and the market demands were regarded as interval numbers instead of constants. An interval linear programming was developed, and a method for solving interval linear programming was presented. An illustrative case was studied by the proposed model, and the results showed that the proposed model is feasible for designing biofuel supply chain under uncertainties.
Journal of management and sustainability, 2015
Incorporating financial hedging and sustainability in a supply chain is crucial for profit maximization or cost minimization. Uncertainties in supply chain develop into risks that affect the profit maximization or cost minimization expectations. In order to deliver end-products to destination markets in an efficient and effective manner, a supply chain management model that incorporates risk management measures is crucial. This paper develops a mathematical model that integrates hedging strategies in a biofuel supply chain with a corn and cellulosic raw material production setting. The paper is structured by first developing an optimization model considering maximization of the supply chain profit with risk without hedging for both corn and cellulosic biorefinery plants. Secondly, we incorporate sustainability concepts including environmental and social aspects. Finally, a heuristic method is developed for the hedging and a two-stage stochastic linear programming with Multi-cut Benders Decomposition Algorithm (MBD) is used to solve the problem. A case study in North Dakota is adopted for this study. The results for hedging and non-hedging are compared and sensitivity analyses conducted.