Benders' decomposition algorithm to solve bi-level bi-objective scheduling of aircrafts and gate assignment under uncertainty

Abstract Management and scheduling of flights and assignment of gates to aircraft play a significant role in improving the procedure of the airport, due to the growing number of flights, decreasing the flight times. This research addresses assigning and scheduling of runways and gates in the main airport simultaneously. Moreover, this research considers the unavailability of runway's constraint and the uncertain parameters relating to both areas of runway and gate assignment. The proposed model is formulated as a comprehensive bi-level bi-objective problem.The leader's objective function minimizes the total waiting time for runways and gates for all aircrafts based on their importance coefficient. Meanwhile, the total distance traveled by all passengers in the airport terminal is minimized by a follower's objective function. To solve the proposed model, the decomposition approach based on Benders' decomposition method is applied. Empirical data are used to show the validation and application of our model. A comparison shows the effectiveness of the proposed model and its significant impact on cost decreasing

Integration of machine learning and optimization for decision making under uncertainties with applications in agriculture and power system

This dissertation focuses on formulating and solving decision-making problems in uncertain environments using the integration of machine learning and optimization. Decision making under uncertainty is a challenging and complex problem because of logistical limitations. First, there exist enormous amount of uncertainty. Considering too many scenarios causes the model to be intractable, while too few scenarios cannot represent uncertainty enough. Second, due to time and resource constraints, it is unrealistic to design and validate algorithms in the real world. Hence, we need computer simulation. Third, considering multiple decision-making stages, many scenarios, complex interactions between scenarios, and decisions needs advanced algorithms and models. In an uncertain environment, the decision-making model has several real-world applications such as energy policies, agriculture, marketing, supply chain design, transportation, etc. The dissertation is devoted to both theoretical research on decision-making under uncertainty and its applications, which consists of three parts, each in a paper format. The first paper develops a new approach to select a small number of high-quality scenarios from many scenarios in the application of transmission expansion planning. Because of correlations between generation capacity, demand, and fuel cost, we develop a heuristic algorithm to capture the generation capacity and construct realistic scenarios. In this study, high-quality scenarios are chosen to minimize the Kantorovich distance of social welfare distributions between the selected and the whole set of scenarios. We explore the performance of the proposed framework on U.S. Eastern and Western Interconnections as a case study. In the second paper, we focus on parameter calibration of computer simulation to have a realistic model for designing and validating the proposed decision-making framework. Because calibration of simulation software is required to reflect nature more realistically, we present a new automated framework and a parallel Bayesian optimization algorithm to estimate time-dependent parameters. In this paper, we focus on a crop model as the simulator of nature. A new automated framework by integrating the power of optimization and machine learning with agronomic insight is proposed to tune time-dependent parameters for crop models and have a realistic simulator. The third paper develops risk-averse stochastic optimization frameworks to optimize management practices and select the best cultivar at different levels of risk aversion. We integrate the crop model and an optimization algorithm to develop multiple stages of the decision-making process. The optimization framework at different levels of risk aversion contains an optimizer and a simulator. The optimizer uses parallel Bayesian algorithms as the core search engine to effectively search the best combinations of management decisions over unknown objective functions. The crop model as a simulator is used to capture complex interactions between scenarios and decisions and evaluate optimizers’ decisions appropriately. The objective function in this study is maximizing yield by optimizing planting date, N fertilizer amount, fertilizing date, and plant density in the farm, and selecting the best cultivar with different maturity days. As a case study, we use a crop model of 25 locations with different environments across the US Corn Belt and optimize for three test years (2016-2018) at three time-wise strategies during the growing season

Performance prediction of crosses in plant breeding through genotype by environment interactions

Performance prediction of potential crosses plays a significant role in plant breeding, which aims to produce new crop varieties that have higher yields, require fewer resources, and are more adaptable to the changing environments. In the 2020 Syngenta crop challenge, Syngenta challenged participants to predict the yield performance of a list of potential breeding crosses of inbreds and testers based on their historical yield data in different environments. They released a dataset that contained the observed yields for 294,128 corn hybrids through the crossing of 593 unique inbreds and 496 unique testers across multiple environments between 2016 and 2018. To address this challenge, we designed a new predictive approach that integrates random forest and an optimization model for G × E interaction detection. Our computational experiment found that our approach achieved a relative root-mean-square-error (RMSE) of 0.0869 for the validation data, outperforming other state-of-the-art models such as factorization machine and extreme gradient boosting tree. Our model was also able to detect genotype by environment interactions that are potentially biologically insightful. This model won the first place in the 2020 Syngenta crop challenge in analytics.This article is published as Ansarifar, Javad, Faezeh Akhavizadegan, and Lizhi Wang. "Performance prediction of crosses in plant breeding through genotype by environment interactions." Scientific Reports 10, no. 1 (2020): 11533. DOI: 10.1038/s41598-020-68343-1. Copyright 2020 The Author(s). Attribution 4.0 International (CC BY 4.0). Posted with permission

Scenario selection for iterative stochastic transmission expansion planning

Reliable transmission expansion planning is critical to power systems’ development. To make reliable and sustainable transmission expansion plans, numerous sources of uncertainty including demand, generation capacity, and fuel cost must be taken into consideration in both spatial and temporal dimensions. This paper presents a new approach to selecting a small number of high-quality scenarios for transmission expansion. The Kantorovich distance of social welfare distributions was used to assess the quality of the selected scenarios. A case study was conducted on a power system model that represents the U.S. Eastern and Western Interconnections, and ten high-quality scenarios out of a total of one million were selected for two transmission plans. Results suggested that scenarios selected using the proposed algorithm were able to provide a much more accurate estimation of the value of transmission plans than other scenario selection algorithms in the literature.This article is published as Akhavizadegan, Faezeh, Lizhi Wang, and James McCalley. "Scenario selection for iterative stochastic transmission expansion planning." Energies 13, no. 5 (2020): 1203. DOI: 10.3390/en13051203. Copyright 2020 by the authors. Attribution 4.0 International (CC BY 4.0). Posted with permission

Scheduling Planting Time Through Developing an Optimization Model and Analysis of Time Series Growing Degree Units

Producing higher-quality crops within shortened breeding cycles ensures global food availability and security, but this improvement intensifies logistical and productivity challenges for seed industries in the year-round breeding process due to the storage limitations. In the 2021 Syngenta crop challenge in analytics, Syngenta raised the problem to design an optimization model for the planting time scheduling in the 2020 year-round breeding process so that there is a consistent harvest quantity each week. They released a dataset that contained 2569 seed populations with their planting windows, required growing degree units for harvesting, and their harvest quantities at two sites. To address this challenge, we developed a new framework that consists of a weather time series model and an optimization model to schedule the planting time. A deep recurrent neural network was designed to predict the weather into the future, and a Gaussian process model on top of the time-series model was developed to model the uncertainty of forecasted weather. The proposed optimization models also scheduled the seed population's planting time at the fewest number of weeks with a more consistent weekly harvest quantity. Using the proposed optimization models can decrease the required capacity by 69% at site 0 and up to 51% at site 1 compared to the original planting time.This is a pre-print of the article Ansarifar, Javad, Faezeh Akhavizadegan, and Lizhi Wang. "Scheduling Planting Time Through Developing an Optimization Model and Analysis of Time Series Growing Degree Units." arXiv preprint arXiv:2207.00745 (2022). DOI: 10.48550/arXiv.2207.00745. Attribution 4.0 International (CC BY 4.0). Copyright 2022 The Authors. Posted with permission

Risk-averse Stochastic Optimization for Farm Management Practices and Cultivar Selection Under Uncertainty

Optimizing management practices and selecting the best cultivar for planting play a significant role in increasing agricultural food production and decreasing environmental footprint. In this study, we develop optimization frameworks under uncertainty using conditional value-at-risk in the stochastic programming objective function. We integrate the crop model, APSIM, and a parallel Bayesian optimization algorithm to optimize the management practices and select the best cultivar at different levels of risk aversion. This approach integrates the power of optimization in determining the best decisions and crop model in simulating nature's output corresponding to various decisions. As a case study, we set up the crop model for 25 locations across the US Corn Belt. We optimized the management options (planting date, N fertilizer amount, fertilizing date, and plant density in the farm) and cultivar options (cultivars with different maturity days) three times: a) before, b) at planting and c) after a growing season with known weather. Results indicated that the proposed model produced meaningful connections between weather and optima decisions. Also, we found risk-tolerance farmers get more expected yield than risk-averse ones in wet and non-wet weathers.This is a pre-print of the article Akhavizadegan, Faezeh, Javad Ansarifar, Lizhi Wang, and Sotirios V. Archontoulis. "Risk-averse stochastic optimization for farm management practices and cultivar selection under uncertainty." arXiv preprint arXiv:2208.04840 (2022). DOI: 10.48550/arXiv.2208.04840. Attribution 4.0 International (CC BY 4.0). Copyright 2022 The Authors. Posted with permission

A time-dependent parameter estimation framework for crop modeling

The performance of crop models in simulating various aspects of the cropping system is sensitive to parameter calibration. Parameter estimation is challenging, especially for time-dependent parameters such as cultivar parameters with 2–3 years of lifespan. Manual calibration of the parameters is time-consuming, requires expertise, and is prone to error. This research develops a new automated framework to estimate time-dependent parameters for crop models using a parallel Bayesian optimization algorithm. This approach integrates the power of optimization and machine learning with prior agronomic knowledge. To test the proposed time-dependent parameter estimation method, we simulated historical yield increase (from 1985 to 2018) in 25 environments in the US Corn Belt with APSIM. Then we compared yield simulation results and nine parameter estimates from our proposed parallel Bayesian framework, with Bayesian optimization and manual calibration. Results indicated that parameters calibrated using the proposed framework achieved an 11.6% reduction in the prediction error over Bayesian optimization and a 52.1% reduction over manual calibration. We also trained nine machine learning models for yield prediction and found that none of them was able to outperform the proposed method in terms of root mean square error and R2. The most significant contribution of the new automated framework for time-dependent parameter estimation is its capability to find close-to-optimal parameters for the crop model. The proposed approach also produced explainable insight into cultivar traits’ trends over 34 years (1985–2018).This article is published as Akhavizadegan, Faezeh, Javad Ansarifar, Lizhi Wang, Isaiah Huber, and Sotirios V. Archontoulis. "A time-dependent parameter estimation framework for crop modeling." Scientific Reports 11, no. 1 (2021): 11437. DOI: 10.1038/s41598-021-90835-x Copyright 2021 The Author(s). Attribution 4.0 International (CC BY 4.0). Posted with permission

The burden of prostate cancer in North Africa and Middle East, 1990–2019:Findings from the global burden of disease study

Background: Prostate cancer (PCa) is the second most prevalent cancer among men worldwide. This study presents estimates of PCa prevalence, incidence, death, years-of-life-lost (YLLs), years-lived-with-disability (YLDs), disability-adjusted-life-years (DALYs), and the burden attributable to smoking during 1990-2019 in North Africa and Middle East using data of Global Burden of Diseases (GBD) Study 2019. Methods: This study is a part of GBD 2019. Using vital registration and cancer registry data, the estimates on PCa burden were modeled. Risk factor analysis was performed through the six-step conceptual framework of Comparative Risk Assessment. Results: The age-standardized rates (95% UI) of PCa incidence, prevalence, and death in 2019 were 23.7 (18.5-27.9), 161.1 (126.6-187.6), and 11.7 (9.4-13.9) per 100,000 population. While PCa incidence and prevalence increased by 77% and 144% during 1990-2019, respectively, the death rate stagnated. Of the 397% increase in PCa new cases, 234% was due to a rise in the age-specific incidence rate, 79% due to population growth, and 84% due to population aging. The YLLs, YLDs, and DALYs of PCa increased by 2% (-11.8-23.1), 108% (75.5-155.1), and 6% (-8.9-28.1). The death rate and DALYs rate attributable to smoking have decreased 12% and 10%, respectively. The DALYs rate attributable to smoking was 37.4 (15.9-67.8) in Lebanon and 5.9 (2.5-10.6) in Saudi Arabia, which were the highest and lowest in the region, respectively. Conclusions: The PCa incidence and prevalence rates increased during 1990-2019; however, the death rate stagnated. The increase in the incidence was mostly due to the rise in the age-specific incidence rate, rather than population growth or aging. The burden of PCa attributable to smoking has decreased in the past 30 years