Two-stage stochastic programming supply chain model for biodiesel production via wastewater treatment

• Published in: Computers & operations research Vol. 49; pp. 1 - 17
• Main Authors: Marufuzzaman, Mohammad, Eksioglu, Sandra D., (Eric) Huang, Yongxi
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.09.2014; Elsevier; Pergamon Press Inc
• Subjects: Applied sciences; Biodiesel fuels; Biodiesel supply chain; Biomass; Carbon; Carbon regulatory mechanisms; Costs; Decision making models; Decisions; Emissions trading; Energy; Exact sciences and technology; Integer programming; Lagrangian relaxation with multi-cut L-shaped algorithm; Logistics; Mathematical models; Mathematical programming; Natural energy; Operational research and scientific management; Operational research. Management science; Pollution; Programming; Stochastic programming; Stochasticity; Studies; Supply chain management; Supply chains; Transportation problem (Operations research); Uncertainty; Wastewaters; Water treatment; Water treatment and pollution; United States > US; Mississippi; Lagrangian relaxation with multi-cut L-shaped algorithm; Biodiesel supply chain; Carbon regulatory mechanisms; Stochastic programming; Stochastic model; Costs; Biofuel; Tax; Biomass; Sustainable development; Modeling; Location problem; Lagrange multiplier; Uncertain system; Emissions trading; Relaxation method; Regulation policy; Decision making; Linear programming; Mixed integer programming; Biodiesel; Carbon; L-shaped algorithm; Case study; Integer programming; Taxes; Lagrangian relaxation with multi-cut; Objective function; Waste water purification; Logistics
• ISSN: 0305-0548; 1873-765X; 0305-0548
• DOI: 10.1016/j.cor.2014.03.010

This paper presents a two-stage stochastic programming model used to design and manage biodiesel supply chains. This is a mixed-integer linear program and an extension of the classical two-stage stochastic location-transportation model. The proposed model optimizes not only costs but also emissions in the supply chain. The model captures the impact of biomass supply and technology uncertainty on supply chain-related decisions; the tradeoffs that exist between location and transportation decisions; and the tradeoffs between costs and emissions in the supply chain. The objective function and model constraints reflect the impact of different carbon regulatory policies, such as carbon cap, carbon tax, carbon cap-and-trade, and carbon offset mechanisms on supply chain decisions. We solve this problem using algorithms that combine Lagrangian relaxation and L-shaped solution methods, and we develop a case study using data from the state of Mississippi. The results from the computational analysis point to important observations about the impacts of carbon regulatory mechanisms as well as the uncertainties on the performance of biocrude supply chains.