Hybrid robust and stochastic optimization for closed-loop supply chain network design using accelerated Benders decomposition

Environmental, social and economic concerns motivate the operation of closed- loop supply chain networks (CLSCN) in many industries. We propose a novel profit maximization model for CLSCN design as a mixed-integer linear program in which there is flexibility in covering the proportions of demand satisfied and returns collected based on the firm\u27s policies. Our major contribution is to develop a novel hybrid robust-stochastic programming (HRSP) approach to simultaneously model two different types of uncertainties by including stochastic scenarios for transportation costs and polyhedral uncertainty sets for demands and returns. Transportation cost scenarios are generated using a Latin Hypercube Sampling method and scenario reduction is applied to consolidate them. An accelerated stochastic Benders decomposition algorithm is proposed for solving this model. To speed up the convergence of this algorithm, valid inequalities are introduced to improve the quality of lower bound, and also a Pareto-optimal cut generation scheme is used to strengthen the Benders optimality cuts. Numerical studies are performed to verify our mathematical formulation and also demonstrate the benefits of the HRSP approach. The performance improvements achieved by the valid inequalities and Pareto-optimal cuts are demonstrated in randomly generated instances

Personalized Data-Driven Learning and Optimization: Theory and Applications to Healthcare

This dissertation is broadly about developing new personalized data-driven learning and optimization methods with theoretical performance guarantees for three important applications in healthcare operations management and medical decision-making. In these research problems, we are dealing with longitudinal settings, where the decision-maker needs to make multi-stage personalized decisions while collecting data in-between stages. In each stage, the decision-maker incorporates the newly observed data in order to update his current system's model or belief, thereby making better decisions next. This new class of data-driven learning and optimization methods indeed learns from data over time so as to make efficient and effective decisions for each individual in real-time under dynamic, uncertain environments. The theoretical contributions lie in the design and analysis of these new predictive and prescriptive learning and optimization methods and proving theoretical performance guarantees for them. The practical contributions are to apply these methods to resolve unmet real-world needs in healthcare operations management and medical decision-making so as to yield managerial and practical insights and new functionality.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/167949/1/keyvan_1.pd

Contextual Bandits with Budgeted Information Reveal

Contextual bandit algorithms are commonly used in digital health to recommend personalized treatments. However, to ensure the effectiveness of the treatments, patients are often requested to take actions that have no immediate benefit to them, which we refer to as pro-treatment actions. In practice, clinicians have a limited budget to encourage patients to take these actions and collect additional information. We introduce a novel optimization and learning algorithm to address this problem. This algorithm effectively combines the strengths of two algorithmic approaches in a seamless manner, including 1) an online primal-dual algorithm for deciding the optimal timing to reach out to patients, and 2) a contextual bandit learning algorithm to deliver personalized treatment to the patient. We prove that this algorithm admits a sub-linear regret bound. We illustrate the usefulness of this algorithm on both synthetic and real-world data

Searching the genome of beluga (Huso huso) for sex markers based on targeted bulked segregant analysis (BSA)

In sturgeon aquaculture, where the main purpose is caviar production, a reliable method is needed to separate fish according to gender. Currently, due to the lack of external sexual dimorphism, the fish are sexed by an invasive surgical examination of the gonads. Development of a non-invasive procedure for sexing fish based on genetic markers is of special interest. In the present study we employed Bulked Segregant Analysis (BSA) methodology to search for DNA markers associated with the sex of the beluga sturgeon (Huso huso). DNA bulks (male and female) were created by combining equal amounts of genomic DNA from 10 fish of both sexes. A total of 101 decamer primers associated with the sex-specific sequences in non-sturgeon species was used for targeted screening of the bulks, resulting in 2846 bands that all of them were present in both sexes. Our results showed that sex chromosomes are weakly differentiated in the sturgeon genome and comprised sequences not complementary to the sex-specific primers in non-sturgeon species

Hybrid robust and stochastic optimization for closed-loop supply chain network design using accelerated Benders decomposition

Environmental, social and economic concerns motivate the operation of closed-loop supply chain networks (CLSCN) in many industries. We propose a novel profit maximization model for CLSCN design as a mixed-integer linear program in which there is flexibility in covering the proportions of demand satisfied and returns collected based on the firm\u27s policies. Our major contribution is to develop a novel hybrid robust-stochastic programming (HRSP) approach to simultaneously model two different types of uncertainties by including stochastic scenarios for transportation costs and polyhedral uncertainty sets for demands and returns. Transportation cost scenarios are generated using a Latin Hypercube Sampling method and scenario reduction is applied to consolidate them. An accelerated stochastic Benders decomposition algorithm is proposed for solving this model. To speed up the convergence of this algorithm, valid inequalities are introduced to improve the lower bound quality, and also a Pareto-optimal cut generation scheme is used to strengthen the Benders optimality cuts. Numerical studies are performed to verify our mathematical formulation and also demonstrate the benefits of the HRSP approach. The performance improvements achieved by the valid inequalities and Pareto-optimal cuts are demonstrated in randomly generated instances

Resource planning strategies for healthcare systems during a pandemic

We study resource planning strategies, including the integrated healthcare resources’ allocation and sharing as well as patients’ transfer, to improve the response of health systems to massive increases in demand during epidemics and pandemics. Our study considers various types of patients and resources to provide access to patient care with minimum capacity extension. Adding new resources takes time that most patients don't have during pandemics. The number of patients requiring scarce healthcare resources is uncertain and dependent on the speed of the pandemic's transmission through a region. We develop a multi-stage stochastic program to optimize various strategies for planning limited and necessary healthcare resources. We simulate uncertain parameters by deploying an agent-based continuous-time stochastic model, and then capture the uncertainty by a forward scenario tree construction approach. Finally, we propose a data-driven rolling horizon procedure to facilitate decision-making in real-time, which mitigates some critical limitations of stochastic programming approaches and makes the resulting strategies implementable in practice. We use two different case studies related to COVID-19 to examine our optimization and simulation tools by extensive computational results. The results highlight these strategies can significantly improve patient access to care during pandemics; their significance will vary under different situations. Our methodology is not limited to the presented setting and can be employed in other service industries where urgent access matters

Proteomics and the search for welfare and stress biomarkers in animal production in the one-health context

Stress and welfare are important factors in animal production in the context of growing production optimization and scrutiny by the general public. In a context in which animal and human health are intertwined aspects of the one-health concept it is of utmost importance to define the markers of stress and welfare. These are important tools for producers, retailers, regulatory agents and ultimately consumers to effectively monitor and assess the welfare state of production animals. Proteomics is the science that studies the proteins existing in a given tissue or fluid. In this review we address this topic by showing clear examples where proteomics has been used to study stress-induced changes at various levels. We adopt a multi-species (cattle, swine, small ruminants, poultry, fish and shellfish) approach under the effect of various stress inducers (handling, transport, management, nutritional, thermal and exposure to pollutants) clearly demonstrating how proteomics and systems biology are key elements to the study of stress and welfare in farm animals and powerful tools for animal welfare, health and productivity

A review of the quantitative real-time PCR and Omics approaches applied to study the effects of dietary selenium nanoparticles (nano-Se) on fish

Selenium (Se) is an essential trace microelement required for the overall health of humans and animals. The importance of Se is mainly related to its participation in the structure of selenoproteins with diverse biological functions, including antioxidant defense, immunity, and thyroid hormone metabolism. The functionality of Se depends on its chemical form (inorganic and organic Se). Due to low toxicity and higher efficacy, Se nanoparticles (nano-Se) have been recently applied in aquafeeds to enhance fish performance. New technological advances have offered different Omics approaches, such as transcriptomics, proteomics, and metabolomics, to realize molecular mechanisms underlying biological processes. In recent years, Omics approaches have been employed to study nano-Se effects on fish. The present article summarizes the impacts of nano-Se supplementation on fish performance, then reviews the qRT-PCR assay and Omics-based approaches used to study the dietary nano-Se supplementation effects in fish