Bio-energy Logistics Network Design Under Price-based Supply and Yield Uncertainty

In this dissertation, we study the design and planning of bio-energy supply chain networks. This dissertation consists of 3 studies that focus on different aspects of bio-energy supply chain systems. In the first study, we consider planning and design of an extended supply chain for bio-energy networks in an integrated fashion while simultaneously addressing strategic and tactical decisions pertaining to location, production, inventory, and distribution in a multi-period planning horizon setting. For an efficient solution of our model, we suggest a Benders Decomposition based algorithm that can handle realistic size problems for design and analysis purposes. We provide computational results that demonstrate the efficiency of the solution approach on a wide ranging set of problem instances. Furthermore, we develop a realistic case by utilizing data pertaining to the state of Texas and conduct an extensive analysis on the effects of varying input parameters on the design outcomes for a bio-energy supply chain network. In the second study, we consider a two-stage stochastic problem to model farm-to-biorefinery biomass logistics while designing a policy that encourages farmers to plant biomass energy crops by offering them a unit wholesale price. In the first-stage, the model determines the supply chain network structure as well as the policy parameter, which is the biomass wholesale price offered to farmers. Second-stage problem is to determine the logistical decisions such as transportation, salvaging and out-sourcing. To solve this problem, we propose a solution framework that uses an algorithm based on the L-shaped method along with a Sample Average Approximation (SAA) approach. An extensive case study by varying some of the problem input parameters is conducted in Texas and the effects on the policy parameter (wholesale price), supply chain network design and expected total system cost are observed. In the last study, we propose a two-stage stochastic program to model a multi-period biomass-biofuel supply chain system to maximize the expected total system profit. We utilize a similar policy used in the second study to stimulate biomass energy crop production. Our model determines the policy parameter and the supply chain network structure in the first-stage and the tactical decisions for every time period in the second-stage. To solve this problem efficiently, we propose a solution algorithm based on the L-shaped method. Moreover, we also employ SAA approach in our solution methodology to statistically justify our solution quality. A case study is conducted in Texas for different biofuel prices and we analyze changes in the expected system profit the policy parameter and the supply chain network structure. Our case study results indicate that biofuel price needs to be at least $2.62/gal for the system to have a profit

Models and solution approaches for intermodal and less-than-truckload network design with load consolidations

Logistics and supply chain problems arising in the context of intermodal transportation and less-than-truckload (LTL) network design typically require commodities to be consolidated and shipped via the most economical route to their destinations. Traditionally, these problems have been modelled using network design or hub-and- spoke approaches. In a network design problem, one is given the network and flow requirements between the origin and destination pairs (commodities), and the objective is to route the flows over the network so as to minimize the sum of the fixed charge incurred in using arcs and routing costs. However, there are possible benefits, due to economies-of-scale in transportation, that are not addressed in standard network design models. On the other hand, hub location problems are motivated by potential economies-of-scale in transportation costs when loads are consolidated and shipped together over a completely connected hub network. However, in a hub location problem, the assignment of a node to a hub is independent of the commodities originating at, or destined to, this node. Such an indiscriminate assignment may not be suitable for all commodities originating at a particular node because of their different destinations. Problems arising in the area of LTL transportation, intermodal transportation and package routing generally have characteristics such as economies- of-scale in transportation costs in addition to the requirement of commodity-based routing. Obviously, the existing network design and hub location-based models are not directly suitable for these applications. In this dissertation, we investigate the development of models and solution algorithms for problems in the areas of LTL and intermodal transportation as well as in the freight forwarders industry. We develop models and solution methods to address strategic, tactical and operational level decision issues and show computational results. This research provides new insights into these application areas and new solution methods therein. The solution algorithms developed here also contribute to the general area of discrete optimization, particularly for problems with similar characteristics

California Methanol Assessment; Volume II, Technical Report

A joint effort by the Jet Propulsion Laboratory and the California Institute of Technology Division of Chemistry and Chemical Engineering has brought together sponsors from both the public and private sectors for an analysis of the prospects for methanol use as a fuel in California, primarily for the transportation and stationary application sectors. Increasing optimism in 1982 for a slower rise in oil prices and a more realistic understanding of the costs of methanol production have had a negative effect on methanol viability in the near term (before the year 2000). Methanol was determined to have some promise in the transportation sector, but is not forecasted for large-scale use until beyond the year 2000. Similarly, while alternative use of methanol can have a positive effect on air quality (reducing NOx, SOx, and other emissions), a best case estimate is for less than 4% reduction in peak ozone by 2000 at realistic neat methanol vehicle adoption rates. Methanol is not likely to be a viable fuel in the stationary application sector because it cannot compete economically with conventional fuels except in very limited cases. On the production end, it was determined that methanol produced from natural gas will continue to dominate supply options through the year 2000, and the present and planned industry capacity is somewhat in excess of all projected needs. Nonsubsidized coal-based methanol cannot compete with conventional feedstocks using current technology, but coal-based methanol has promise in the long term (after the year 2000), providing that industry is willing to take the technical and market risks and that government agencies will help facilitate the environment for methanol. Given that the prospects for viable major markets (stationary applications and neat fuel in passenger cars) are unlikely in the 1980s and early 1990s, the next steps for methanol are in further experimentation and research of production and utilization technologies, expanded use as an octane enhancer, and selected fleet implementation. In the view of the study, it is not advantageous at this time to establish policies within California that attempt to expand methanol use rapidly as a neat fuel for passenger cars or to induce electric utility use of methanol on a widespread basis

Optimization of Large-Scale Sustainable Renewable Energy Supply Chains in a Stochastic Environment

Due to the increasing demand of energy and environmental concern of fossil fuels, it is becoming increasingly important to find alternative renewable energy sources. Biofuels produced from lignocellulosic biomass feedstock's show enormous potential as a renewable resource. Electricity generated from the combustion of biomass is also one important type of bioenergy. Renewable resources like wind also show great potential as a resource for electricity generation. In order to deliver competitive renewable energy products to the end-market, robust renewable energy supply chains (RESCs) are essential. Research is needed in two distinct types of RESCs, namely: 1) lignocellulosic biomass-to-biofuel (LBSC); and 2) wind energy/biomass-to-electricity (WBBRESSC). LBSC is a complex system which consists of multiple uncertainties which include: 1) purchase price and availability of biomass feedstock; 2) sale price and demand of biofuels. To ensure LBSC sustainability, the following decisions need to be optimized: a) allocation of land for biomass cultivation; b) biorefinery sites selection; c) choice of biomass-to-biofuel conversion technology; and d) production capacity of biorefineries. The major uncertainty in a WBBRESC concerns wind speeds which impact the power output of wind farms. To ensure WBBRESC sustainability, the following decisions need to be optimized: a) site selection for installation of wind farms, biomass power plants (BMPPs), and grid stations; b) generation capacity of wind farms and BMPPs; and c) transmission capacity of power lines. The multiple uncertainties in RESCs if not jointly considered in the decision making process result in non-optimal (or even infeasible) solutions which generate lower profits, increased environmental pollution, and reduced social benefits. This research proposes a number of comprehensive mathematical models for the stochastic optimization of RESCs. The proposed large-scale stochastic mixed integer linear programming (SMILP) models are solved to optimality by using suitable decomposition methods (e.g. Bender's) and appropriate metaheuristic algorithms (e.g. Sample Average Approximation). Overall, the research outcomes will help to design robust RESCs focused towards sustainability in order to optimally utilize the renewable resources in the near future. The findings can be used by renewable energy producers to sustainably operate in an efficient (and cost effective) manner, boost the regional economy, and protect the environment

Optimal Capacity Utilization and Reallocation in a German Bank Branch Network: Exploring Some Strategic Scenarios

Quite a few studies have considered efficiency at the bank branch level by comparing mostly a single branch network, while an abundance of studies have focused on comparing banking institutions. However, to the best of our knowledge no study has ever assessed performance at the level of the branch bank network by looking for ways to reallocate resources such that overall performance improves. Here, we introduce the Johansen-Färe measure of plant capacity of the firm into a multi-output, frontier-based version of the short-run Johansen industry model. The first stage capacity model carefully checks for the impact of the convexity assumption on the estimated capacity utilization results. Policy scenarios considered for the short-run Johansen industry model vary in terms of their tolerance with respect to existing bank branch inefficiencies, the formulation of closure policies, the reallocation of labor in terms of integer units, etc. The application to a network of 142 bank branches of a German savings bank in the year 1998 measures their efficiency and capacity utilization and demonstrate that by this industry model approach one can improve the performance of the whole branch network.Bank Branch Network, Efficiency, Capacity, Reallocation

Modular Supply Network Optimization of Renewable Ammonia and Methanol Co-production

To reduce the use of fossil fuels and other carbonaceous fuels, renewable energy sources such as solar, wind, geothermal energy have been suggested to be promising alternative energy that guarantee sustainable and clean environment. However, the availability of renewable energy has been limited due to its dependence on weather and geographical location. This challenge is intended to be solved by the utilization of the renewable energy in the production of chemical energy carriers. Hydrogen has been proposed as a potential renewable energy carrier, however, its chemical instability and high liquefaction energy makes researchers seek for other alternative energy carriers. Ammonia and methanol can serve as promising alternative energy carriers due to their chemical stability at room temperature, low liquefaction energy, high energy value. The co-production of these high energy dense energy carriers offers economic and environmental advantages since their synthesis involve the direct utilization of CO2 and common unit operations. This problem report aims to review the optimization of the co-production of methanol and ammonia from renewable energy. Form this review, research challenges and opportunities are identified in the following areas: (i) optimization of methanol and ammonia co-production under renewable and demand uncertainty, (ii) impacts of the modular exponent on the feasibility of co-production of ammonia and methanol, and (iii) development of modern computational tools for systems-based analysis

Development of sustainable groundwater management methodologies to control saltwater intrusion into coastal aquifers with application to a tropical Pacific island country

Saltwater intrusion due to the over-exploitation of groundwater in coastal aquifers is a critical challenge facing groundwater-dependent coastal communities throughout the world. Sustainable management of coastal aquifers for maintaining abstracted groundwater quality within permissible salinity limits is regarded as an important groundwater management problem necessitating urgent reliable and optimal management methodologies. This study focuses on the development and evaluation of groundwater salinity prediction tools, coastal aquifer multi-objective management strategies, and adaptive management strategies using new prediction models, coupled simulation-optimization (S/O) models, and monitoring network design, respectively. Predicting the extent of saltwater intrusion into coastal aquifers in response to existing and changing pumping patterns is a prerequisite of any groundwater management framework. This study investigates the feasibility of using support vector machine regression (SVMR), an innovative artificial intelligence-based machine learning algorithm, to predict salinity at monitoring wells in an illustrative aquifer under variable groundwater pumping conditions. For evaluation purposes, the prediction results of SVMR are compared with well-established genetic programming (GP) based surrogate models. The prediction capabilities of the two learning machines are evaluated using several measures to ensure their practicality and generalisation ability. Also, a sensitivity analysis methodology is proposed for assessing the impact of pumping rates on salt concentrations at monitoring locations. The performance evaluations suggest that the predictive capability of SVMR is superior to that of GP models. The sensitivity analysis identifies a subset of the most influential pumping rates, which is used to construct new SVMR surrogate models with improved predictive capabilities. The improved predictive capability and generalisation ability of SVMR models, together with the ability to improve the accuracy of prediction by refining the dataset used for training, make the use of SVMR models more attractive. Coupled S/O models are efficient tools that are used for designing multi-objective coastal aquifer management strategies. This study applies a regional-scale coupled S/O methodology with a Pareto front clustering technique to prescribe optimal groundwater withdrawal patterns from the Bonriki aquifer in the Pacific Island of Kiribati. A numerical simulation model is developed, calibrated and validated using field data from the Bonriki aquifer. For computational feasibility, SVMR surrogate models are trained and tested utilizing input-output datasets generated using the flow and transport numerical simulation model. The developed surrogate models were externally coupled with a multi-objective genetic algorithm optimization (MOGA) model, as a substitute for the numerical model. The study area consisted of freshwater pumping wells for extracting groundwater. Pumping from barrier wells installed along the coastlines is also considered as a management option to hydraulically control saltwater intrusion. The objective of the multi-objective management model was to maximise pumping from production wells and minimize pumping from barrier wells (which provide a hydraulic barrier) to ensure that the water quality at different monitoring locations remains within pre-specified limits. The executed multi-objective coupled S/O model generated 700 Pareto-optimal solutions. Analysing a large set of Pareto-optimal solution is a challenging task for the decision-makers. Hence, the k-means clustering technique was utilized to reduce the large Pareto-optimal solution set and help solve the large-scale saltwater intrusion problem in the Bonriki aquifer. The S/O-based management models have delivered optimal saltwater intrusion management strategies. However, at times, uncertainties in the numerical simulation model due to uncertain aquifer parameters are not incorporated into the management models. The present study explicitly incorporates aquifer parameter uncertainty into a multi-objective management model for the optimal design of groundwater pumping strategies from the unconfined Bonriki aquifer. To achieve computational efficiency and feasibility of the management model, the calibrated numerical simulation model in the S/O model was is replaced with ensembles of SVMR surrogate models. Each SVMR standalone surrogate model in the ensemble is constructed using datasets from different numerical simulation models with different hydraulic conductivity and porosity values. These ensemble SVMR models were coupled to the MOGA model to solve the Bonriki aquifer management problem for ensuring sustainable withdrawal rates that maintain specified salinity limits. The executed optimization model presented a Pareto-front with 600 non-dominated optimal trade-off pumping solutions. The reliability of the management model, established after validation of the optimal solution results, suggests that the implemented constraints of the optimization problem were satisfied; i.e., the salinities at monitoring locations remained within the pre-specified limits. The correct implementation of a prescribed optimal management strategy based on the coupled S/O model is always a concern for decision-makers. The management strategy actually implemented in the field sometimes deviates from the recommended optimal strategy, resulting in field-level deviations. Monitoring such field-level deviations during actual implementation of the recommended optimal management strategy and sequentially updating the strategy using feedback information is an important step towards adaptive management of coastal groundwater resources. In this study, a three-phase adaptive management framework for a coastal aquifer subjected to saltwater intrusion is applied and evaluated for a regional-scale coastal aquifer study area. The methodology adopted includes three sequential components. First, an optimal management strategy (consisting of groundwater extraction from production and barrier wells) is derived and implemented for the optimal management of the aquifer. The implemented management strategy is obtained by solving a homogeneous ensemble-based coupled S/O model. Second, a regional-scale optimal monitoring network is designed for the aquifer system, which considers possible user noncompliance of a recommended management strategy and uncertainty in aquifer parameter estimates. A new monitoring network design is formulated to ensure that candidate monitoring wells are placed at high risk (highly contaminated) locations. In addition, a k-means clustering methodology is utilized to select candidate monitoring wells in areas representative of the entire model domain. Finally, feedback information in the form of salinity measurements at monitoring wells is used to sequentially modify pumping strategies for future time periods in the management horizon. The developed adaptive management framework is evaluated by applying it to the Bonriki aquifer system. Overall, the results of this study suggest that the implemented adaptive management strategy has the potential to address practical implementation issues arising due to user noncompliance, as well as deviations between predicted and actual consequences of implementing a management strategy, and uncertainty in aquifer parameters. The use of ensemble prediction models is known to be more accurate standalone prediction models. The present study develops and utilises homogeneous and heterogeneous ensemble models based on several standalone evolutionary algorithms, including artificial neural networks (ANN), GP, SVMR and Gaussian process regression (GPR). These models are used to predict groundwater salinity in the Bonriki aquifer. Standalone and ensemble prediction models are trained and validated using identical pumping and salinity concentration datasets generated by solving numerical 3D transient density-dependent coastal aquifer flow and transport numerical simulation models. After validation, the ensemble models are used to predict salinity concentration at selected monitoring wells in the modelled aquifer under variable groundwater pumping conditions. The predictive capabilities of the developed ensemble models are quantified using standard statistical procedures. The performance evaluation results suggest that the predictive capabilities of the standalone prediction models (ANN, GP, SVMR and GPR) are comparable to those of the groundwater variable-density flow and salt transport numerical simulation model. However, GPR standalone models had better predictive capabilities than the other standalone models. Also, SVMR and GPR standalone models were more efficient (in terms of computational training time) than other standalone models. In terms of ensemble models, the performance of the homogeneous GPR ensemble model was found to be superior to that of the other homogeneous and heterogeneous ensemble models. Employing data-driven predictive models as replacements for complex groundwater flow and transport models enables the prediction of future scenarios and also helps save computational time, effort and requirements when developing optimal coastal aquifer management strategies based on coupled S/O models. In this study, a new data-driven model, namely Group method for data handling (GMDH) approach is developed and utilized to predict salinity concentration in a coastal aquifer and, simultaneously, determine the most influential input predictor variables (pumping rates) that had the most impact onto the outcomes (salinity at monitoring locations). To confirm the importance of variables, three tests are conducted, in which new GMDH models are constructed using subsets of the original datasets. In TEST 1, new GMDH models are constructed using a set of most influential variables only. In TEST 2, a subset of 20 variables (10 most and 10 least influential variables) are used to develop new GMDH models. In TEST 3, a subset of the least influential variables is used to develop GMDH models. A performance evaluation demonstrates that the GMDH models developed using the entire dataset have reasonable predictive accuracy and efficiency. A comparison of the performance evaluations of the three tests highlights the importance of appropriately selecting input pumping rates when developing predictive models. These results suggest that incorporating the least influential variables decreases model accuracy; thus, only considering the most influential variables in salinity prediction models is beneficial and appropriate. This study also investigated the efficiency and viability of using artificial freshwater recharge (AFR) to increase fresh groundwater pumping rates from production wells. First, the effect of AFR on the inland encroachment of saline water is quantified for existing scenarios. Specifically, groundwater head and salinity differences at monitoring locations before and after artificial recharge are presented. Second, a multi-objective management model incorporating groundwater pumping and AFR is implemented to control groundwater salinization in an illustrative coastal aquifer system. A coupled SVMR-MOGA model is developed for prescribing optimal management strategies that incorporate AFR and groundwater pumping wells. The Pareto-optimal front obtained from the SVMR-MOGA optimization model presents a set of optimal solutions for the sustainable management of the coastal aquifer. The pumping strategies obtained as Pareto-optimal solutions with and without freshwater recharge shows that saltwater intrusion is sensitive to AFR. Also, the hydraulic head lenses created by AFR can be used as one practical option to control saltwater intrusion. The developed 3D saltwater intrusion model, the predictive capabilities of the developed SVMR models, and the feasibility of using the proposed coupled multi-objective SVMR-MOGA optimization model make the proposed methodology potentially suitable for solving large-scale regional saltwater intrusion management problems. Overall, the development and evaluation of various groundwater numerical simulation models, predictive models, multi-objective management strategies and adaptive methodologies will provide decision-makers with tools for the sustainable management of coastal aquifers. It is envisioned that the outcomes of this research will provide useful information to groundwater managers and stakeholders, and offer potential resolutions to policy-makers regarding the sustainable management of groundwater resources. The real-life case study of the Bonriki aquifer presented in this study provides the scientific community with a broader understanding of groundwater resource issues in coastal aquifers and establishes the practical utility of the developed management strategies

Combinatorial optimization and vehicle fleet planning : perspectives and prospects

Bibliography : p.52-60.Research supported in part by the National Science Foundation under grant 79-26225-ECS.by Thomas L. Magnanti