Sandra D. Eksioglu

Analyzing the design and management of biomass-to-biorefinery supply chain

Bioenergy has been recognized as an important source of energy that will reduce nations dependency on petroleum, and have a positive impact on the economy, environment, and society. Production of bioenergy is expected to increase. As a result, we foresee an increase in the number of biorefineries in the near future. This paper analyzes logistical challenges with supplying biomass to a biorefinery. We also propose a mathematical model that can be used to design the supply chain and manage the logistics of a biorefinery. Supply chain-design decisions are long-term type of decisions; while logistics management involves medium to short-term decisions. The proposed model coordinates these decisions. The model determines the number, size and location of biorefineries needed to produce biofuel using the available biomass. The model also determines the amount of biomass shipped, processed and inventoried during a time period. Inputs to the model are the availability of biomass feedstock, as well as biomass transportation, inventory and processing costs. We use the State of Mississippi as the testing ground of this model.

Analyzing Impact of Intermodal Facilities on Design and Management of Biofuel Supply Chain

The impact of an intermodal facility on location and transportation decisions for biofuel production plants is analyzed. Location decisions affect the management of the inbound and outbound logistics of a plant. This supply chain design and management problem is modeled as a mixed integer program. Input data for this model are location of intermodal facilities and available transportation modes, cost and cargo capacity for each transportation mode, geographical distribution of biomass feedstock and production yields, and biomass processing and inventory costs. Outputs from this model are the number, location, and capacity of biofuel production plants. For each plant, the transportation mode used, timing of shipments, shipment size, inventory size, and production schedule that minimize the delivery cost of biofuel are determined. The model proposed in this research can be used as a decision-making tool for investors in the biofuels industry since it estimates the real cost of the business. The state of Mississippi is considered as the testing grounds for the model.

Optimizing the use of public transit system during no-notice evacuation of urban areas

This paper proposes a methodology that can be used to design plans for evacuating transit-dependent citizens during no-notice disasters. A mixed-integer linear program is proposed to model the problem of finding optimal evacuation routes. The objective of the problem is to minimize the total evacuation time and the number of casualties, simultaneously. A traffic simulation package is used to explicitly incorporate the traffic flow dynamics into our model in order to generate solutions which are consistent with the dynamics of traffic network. Due to the long running time of CPLEX, a Tabu search algorithm is designed that finds evacuation routes for transit vehicles. Computational experiments demonstrate that the solutions found are of high-quality. Numerical experiments are conducted using the transportation network of the city of Forth Worth, TX to illustrate the modeling procedure and solution approach.

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

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.

A tabu search algorithm for the flowshop scheduling problem with changing neighborhoods

Flowshop scheduling deals with the sequencing of a set of jobs that visit a set of machines in the same order. A tabu search procedure is proposed for the flowshop scheduling problem with the makespan minimization criterion. Different from other tabu search procedures, the neighborhood of a solution is generated using a combination of three different exchange mechanisms. This has resulted in a well-diversified search procedure. The performance of the algorithm is tested using Taillards benchmark problems. The results are compared to recently developed neuro-tabu search and ant colony heuristics. The computational results indicate the effectiveness of the proposed approach.

Integrating multimodal transport into cellulosic biofuel supply chain design under feedstock seasonality with a case study based on California

A multistage, mixed integer programing model was developed that fully integrates multimodal transport into the cellulosic biofuel supply chain design under feedstock seasonality. Three transport modes are considered: truck, single railcar, and unit train. The goal is to minimize the total cost for infrastructure, feedstock harvesting, biofuel production, and transportation. Strategic decisions including the locations and capacities of transshipment hubs, biorefineries, and terminals and tactical decisions on system operations are optimized in an integrated manner. When the model was implemented to a case study of cellulosic ethanol production in California, it was found that trucks are convenient for short-haul deliveries while rails are more effective for long-haul transportation. Taking the advantage of these benefits, the multimodal transport provides more cost effective solutions than the single-mode transport (truck).

Cross-Facility production and transportation planning problem with perishable inventory

This study addresses a production and distribution planning problem in a dynamic, two-stage supply chain. This supply chain consists of a number of facilities and retailers. The model considers that the final product is perishable and therefore has a limited shelf life. We formulate this problem as a network flow problem with a fixed charge cost function which is NP-hard. A primal-dual heuristic is developed that provides lower and upper bounds. The models proposed can be used for operational decisions.

Environmentally Friendly Supply Chain Planning and Design for Biodiesel Production via Wastewater Sludge

This study presents mathematical models that capture the impact of different carbon-emission-related policies on the design of the biodiesel supply chain. These mathematical models identify locations and production capacities for biocrude production plants by exploring the trade-offs that exist between transportation costs, facility investments costs, and emissions. The mathematical models capture the dynamics of biomass supply and transportation costs during a predefined time horizon. We analyze the behavior of the chain under different regulatory policies such as carbon cap, carbon tax, carbon cap and trade, and carbon offset mechanisms. A number of observations are made about the impact of each policy on the supply chain performance. The models we developed are solved by using a commercial software GAMS/CPLEX. We use the state of Mississippi as the testing grounds for these models, and employ ArcGIS to visualize and validate the results from the optimization models.

A multi-objective, hub-and-spoke model to design and manage biofuel supply chains

In this paper we propose a multi-objective, mixed integer linear programming model to design and manage the supply chain for biofuels. This model captures the trade-offs that exist between costs, environmental and social impacts of delivering biofuels. The in-bound supply chain for biofuel plants relies on a hub-and-spoke structure which optimizes transportation costs of biomass. The model proposed optimizes the