Browsing Industrial engineering by Subject "Operations research"
Now showing items 1-18 of 18
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Approximate dynamic programming for weakly coupled Markov decision processes with perfect and imperfect information
A broad range of optimization problems in applications such as healthcare operations, revenue management, telecommunications, high-performance computing, logistics and transportation, business analytics, and defense, have ... -
Approximating Large-Scale Binary Integer Programs by Discrete Optimal Control
Optimal control theory has been introduced as a powerful tool for approximately solving binary integer programming problems. In previous studies, an approach using continuous optimal control theory was developed, where the ... -
Cost-Effectiveness Analysis of Adaptive Monitoring Strategies for Depression Treatment
Depression is a significant challenge for the American medical care system and affects as high as 10% of the adult population in the U.S. There are many challenges in treating depression. People are reluctant to reveal ... -
Discrete-event Simulation and Optimization to Improve the Performance of a Healthcare System
Healthcare systems have attracted the attention of management and analysis due to their high percentage of the gross domestic product (GDP) and increasing rate of growth of expenditures. Within the various types of healthcare ... -
Exact and Heuristic Approaches to Middle and Last Mile Logistics
Logistics is a well-studied field in operations research. Numerous authors have done extensive work in this area, especially in the domain of routing problems. However there are still aspects of routing to be better explored. ... -
Explicitly Controlling Geometric Characteristics of Corridors in Spatial Optimization
Spatially-explicit mixed-integer programming models (MIPs) allow decision makers to explore a variety of complex scenarios and determine optimal sets of actions across a landscape. In reserve selection problems, the landscape ... -
Improving Efficiency in Allocating Pediatric Ambulatory Care Clinics
Low utilized resources is a common problem in the health care sector. As health care costs and the need for more efficient operations increases, managers are looking for new methods to increase the utilization of their ... -
Information theoretic learning methods for Markov decision processes with parametric uncertainty
Markov decision processes (MDPs) model a class of stochastic sequential decision problems with applications in engineering, medicine, and business analytics. There is considerable interest in the literature in MDPs with ... -
Methodology for the Preliminary Design of a System of Integrated Clinical Laboratories
Clinical laboratory testing is used for deriving patient diagnoses, monitoring treatment and predicting the expected outcome of a disease (prognosis). Thus, the performance of clinical laboratories is critical for our ... -
Modeling Depression Progression Dynamics from Electronic Health Record
To assess and monitor the progression dynamics of patients' depression severity conditions, Markov models are refined from other disease progression modeling methodologies to identify the characteristics and evolvement of ... -
Optimization and Machine Learning Frameworks for Complex Network Analysis
Networks are all around us, and they may be connections of tangible objects in the Euclidean space such as electric power grids, the Internet, highways systems, etc. Among the wide range of areas in the network analysis, ... -
Optimizing Personalized Treatment Selection for Partially Observable Chronic Conditions
For many chronic diseases, an individual patient may experience a wide variety of progres- sion pathways. Personalized medicine needs tools to predict the trajectory of an individual patient’s disease progression, which ... -
Reducing Disruptive Effects of Patient No-shows: A Scheduling Approach
(2013-11-14)Appointment scheduling systems have been studied for nearly 60 years. From a decision making point of view, related problems can be classified into two categories: static and dynamic. In a static scheduling problem, all ... -
Risk-Averse Optimization in Multicriteria and Multistage Decision Making
Risk-averse stochastic programming provides means to incorporate a wide range of risk attitudes into decision making. Pioneered by the advances in financial optimization, several risk measures such as Value-at-Risk (VaR) ... -
Robust, Non-stationary, and Adaptive Fractionation in Radiotherapy
In external beam radiotherapy for cancer, high-energy radiation is passed through the pa- tient’s body from an outside source to kill tumor cells. The challenge is that radiation also damages healthy tissue and organs-at-risk ... -
Scheduling Mass Customized Large Product Assembly Line Considering Learning Effect and Shifting Bottleneck
(2014-04-30)The large product assembly industry is a complex and heavily manually based assembly manufacturing process. Examples are commercial airplanes, ships, and wind turbines. These manufacturing enterprises are increasingly ... -
Simulation and Statistical Methods in Proactive and Strategic Obsolescence Management
In fields with sustainment-dominated systems, where the sustainment costs are larger than the original system costs, managing the life-cycle costs is a non-trivial task. These fields include aviation, power plants, medical, ... -
Stochastic Combinatorial Optimization with Applications in Graph Covering
We study stochastic combinatorial optimization models and propose methods for their solution. First, we consider a risk-neutral two-stage stochastic programming model for which the objective value function of the second-stage ...