Crop Yield Prediction Using Deep Neural Networks

Crop yield is a highly complex trait determined by multiple factors such as genotype, environment, and their interactions. Accurate yield prediction requires fundamental understanding of the functional relationship between yield and these interactive factors, and to reveal such relationship requires both comprehensive datasets and powerful algorithms. In the 2018 Syngenta Crop Challenge, Syngenta released several large datasets that recorded the genotype and yield performances of 2,267 maize hybrids planted in 2,247 locations between 2008 and 2016 and asked participants to predict the yield performance in 2017. As one of the winning teams, we designed a deep neural network (DNN) approach that took advantage of state-of-the-art modeling and solution techniques. Our model was found to have a superior prediction accuracy, with a root-mean-square-error (RMSE) being 12% of the average yield and 50% of the standard deviation for the validation dataset using predicted weather data. With perfect weather data, the RMSE would be reduced to 11% of the average yield and 46% of the standard deviation. We also performed feature selection based on the trained DNN model, which successfully decreased the dimension of the input space without significant drop in the prediction accuracy. Our computational results suggested that this model significantly outperformed other popular methods such as Lasso, shallow neural networks (SNN), and regression tree (RT). The results also revealed that environmental factors had a greater effect on the crop yield than genotype.Comment: 9 pages, Presented at 2018 INFORMS Conference on Business Analytics and Operations Research (Baltimore, MD, USA). One of the winning solutions to the 2018 Syngenta Crop Challeng

The Watermelon Algorithm for The Bilevel Integer Linear Programming Problem

This paper presents an exact algorithm for the bilevel integer linear programming (BILP) problem. The proposed algorithm, which we call the watermelon algorithm, uses a multiway disjunction cut to remove bilevel infeasible solutions from the search space, which was motivated by how watermelon seeds can be carved out by a scoop. Serving as the scoop, a polyhedron is designed to enclose as many bilevel infeasible solutions as possible, and then the complement of this polyhedron is applied to the search space as a multiway disjunction cut in a branch-and-bound framework. We have proved that the watermelon algorithm is able to solve all BILP instances finitely and correctly, providing either a global optimal solution or a certificate of infeasibility or unboundedness. Computational experiment results on two sets of small- to medium-sized instances suggest that the watermelon algorithm could be significantly more efficient than previous branch-and-bound based BILP algorithms

Three Essays on Decision Making under Uncertainty in Electric Power Systems

This thesis consists of three essays, discussing three different but connected problems on decision making under uncertainty in electricpower systems.The first essay uses a system model to examine how various factors affect the market price of electricity, and decomposes the price toquantitatively evaluate the contributions of individual factors as well as their interactions. Sensitivity analysis results from a parametric quadratic program are applied in the computation.The second essay formulates the well studied security constrained economic dispatch (SCED) problem as a Markov decision process model,where the action space is a polyhedron defined by linear generation and transmission constraints. Such a model enables the decision maker to accurately evaluate the impact of a dispatch decision to the entire future operation of the electric power system.The third essay examines the effect of demand and supply side uncertainties on the exercise of market power. Solutions under Bertrand, Cournot, and linear supply function equilibrium (LSFE)models are derived and compared.The three problems studied in the essays are a unique representation of different levels of the decision making process in a sophisticated deregulated electric power system, using techniques from both mathematical programming and probability/statistics

A tri-level optimization model for inventory control with uncertain demand and lead time

We propose an inventory control model for an uncapacitated warehouse in a manufacturing facility under demand and lead time uncertainty. The objective is to make ordering decisions to minimize the total system cost. We introduce a two-stage tri-level optimization model with a rolling horizon to address the uncertain demand and lead time regardless of their underlying distributions. In addition, an exact algorithm is designed to solve the model. We compare this model in a case study with three decision-making strategies: optimistic, moderate, and pessimistic. Our computational results suggest that the performances of these models are either consistently inferior or highly sensitive to cost parameters (such as holding cost and shortage cost), whereas the new tri-level optimization model almost always results in the lowest total cost in all parameter settings

Three new approaches to genomic selection

Conventional genomic selection approaches use breeding values to evaluate individual plants or animals and to make selection decisions. Multiple variants of breeding values and selection approaches have been proposed, but they suffer two major limitations. First, selection decisions are not responsive to changes in time and resource availability. Second, selection decisions are not coordinated with related decisions such as mating and resource allocation. We present three new genomic selection approaches that attempt to address these two limitations, which were designed by engineering students in a class project at Iowa State University. Compared with previous approaches using the same data set from the literature, two of these engineering approaches were found to be comparable to the state of the art, and the third one significantly dominated all the previous approaches

A Market Analysis on Green Production Lines Penetrating into Original Equipment Manufacturers (OEMs)

Fierce market competition has been big obstacle in the growth process of sustainable/green production industry. Sustainable production lines often come into play by penetrating into traditional Original Equipment Manufacturers (OEMs). This is due to the high setup cost and opportunity risks. In this study, we look into the scenario when traditional Original Equipment Manufacturers (OEMs) introduce green production lines into their production systems. We want to address the questions whether and how the green production lines can survive in the market competitions. Analyses are conducted between ordinary and green production sectors which produce a similar type of product with different materials and techniques. A game theoretic model is formulated to analyze the competition in the market. Sensitivity analyses and numerical examples can provide suggestions to assist policy makers, government, company executives and consumers to make better and rational decisions

A Lot Aggregation Optimization Model for Minimizing Food Traceability Effort

This paper proposes a lot aggregation optimization model for minimizing the traceability effort at a grain elevator. The problem involves blending of bulk grain to meet customer specifications. A mathematical multi-objective mixed integer programming (MIP) model is proposed with two objective functions. The objective functions allow in calculating the minimum levels of lot aggregation and minimum discounts that need to be applied to a shipment when the customer contract specifications are not met. Constraints on the system include customer contract specifications, availability of grain at the elevator and the blending requirements. The solutions include the quantities of grain lots from different bins to be used for blending for a shipment while using the minimum number of storage bins and the total discounts to be applied. The numerical results are presented for two shipment scenarios to demonstrate the application of this model to bulk grain blending. The Pareto optimal solutions were calculated that represent the different optimal solutions for the blending problem. This provides the elevator management with a set of blending options. This model provides an effective method for minimizing the traceability effort by minimizing the food safety risk. Besides minimizing the lot aggregation, this model also allows in using the maximum volume of grain present in a given bin which leads to emptying of the storage bins and the extent of aggregation of old grain lots with the new incoming lots can decrease considerably. Use of fewer bins for blending shipments is also easier logistically and can lead to additional savings in terms of grain handling cost and time

Potential competition for biomass between biopower and biofuel under RPS and RFS2

Driven by Renewable Portfolio Standards and Renewable Fuel Standard, biopower generation and biofuel production will increasingly compete for the same biomass resource over the next two decades. We use a linear programming model to study this competition as well as other interactions between the two policies. Our model describes the U.S. renewable energy portfolio by explicitly accounting for all major renewable energy resources, unique resource availability and policy requirements in all 50 states and Washington, DC, and policy deadlines set by all RPS and RFS2 policies within a 2013-2035 modeling horizon. Our modeling results were used to address five important questions regarding interactions between RPS and RFS2 and the impact on U.S. renewable energy portfolio

BSG alignment of SDSS galaxy groups

We study the alignment signal between the distribution of brightest satellite galaxies (BSGs) and the major axis of their host groups using SDSS group catalog constructed by Yang et al. (2007). After correcting for the effect of group ellipticity, a statistically significant (~ 5\sigma) major-axis alignment is detected and the alignment angle is found to be 43.0 \pm 0.4 degrees. More massive and richer groups show stronger BSG alignment. The BSG alignment around blue BCGs is slightly stronger than that around red BCGs. And red BSGs have much stronger major-axis alignment than blue BSGs. Unlike BSGs, other satellites do not show very significant alignment with group major axis. We further explore the BSG alignment in semi-analytic model (SAM) constructed by Guo et al. (2011). We found general good agreement with observations: BSGs in SAM show strong major-axis alignment which depends on group mass and richness in the same way as observations; and none of other satellites exhibit prominent alignment. However, discrepancy also exists in that the SAM shows opposite BSG color dependence, which is most probably induced by the missing of large scale environment ingredient in SAM. The combination of two popular scenarios can explain the detected BSG alignment. The first one: satellites merged into the group preferentially along the surrounding filaments, which is strongly aligned with the major axis of the group. The second one: BSGs enter their host group more recently than other satellites, then will preserve more information about the assembling history and so the major-axis alignment. In SAM, we found positive evidence for the second scenario by the fact that BSGs merged into groups statistically more recently than other satellites. On the other hand, although is opposite in SAM, the BSG color dependence in observation might indicate the first scenario as well.Comment: 8 pages, 11 figures, ApJ accepte