Industrial & Manufacturing Engineering

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Research from the Department of Industrial & Manufacturing Engineering. The department website may be found at https://www.ndsu.edu/ce/https://www.ndsu.edu/ime/

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Intelligent Cold Supply Chain Management System with Radio Frequency Identification, Global Positioning System, and Wireless Sensor Network
(North Dakota State University, 2009) Yang, Xiaomin
This thesis establishes an intelligent cold supply chain management system which consists of two parts: one is the intelligent tracking system integrated with Radio Frequency Identification (RFID), Global Positioning System (GPS), and Wireless Sensor Network (WSN); the other is the cold supply chain model. This tracking system is mainly designed to monitor the food products during the transport, including two parts, a data terminal and a data server. The data terminal is installed inside a container, comprised of GPS, Bluetooth, industrial computer, WSN, RFID reader, RFID antenna, and Code Division Multiple Access (CDMA) modem. The data server is a computer which is able to access internet and has one Structured Query Language (SQL) database. Related application programs are developed with JAVA language. The whole system is successfully tested and meets the expectations we desired at the beginning. In this study, a refrigerator is used to simulate the environment of the container. The data terminal collects all information, including temperature inside the container, GPS location, Product's Identification, and current time in five minute intervals (customers will be asked to set this time interval at the beginning). CDMA cellular network provides the communication between the data server and the data terminal. The data server receives all information and saves the information in the SQL database, which can be used to predict the food safety. Advantages of this tracking system include the ability: 1) to trace and track the products starting from the suppliers to retailers; 2) to monitor and store important parameters during the processing and distribution of food products, such as temperature; 3) to communicate in real time for prompt response; and 4) to quantify food safety prediction. The objective of the model developed in this study is to maximize the profit of the cold supply chain. There are one distribution center, multiple retailers and suppliers involved in the cold supply chain. Since the real-time quality situations of products are available even during the transport, retailers can set prices of products based on the real quality situation. The company is able to dynamically plan the quantity of distribution from the distribution or suppliers' site. In addition, retailers are able to manage the inventory based on the real shelf life of products. This thesis also concludes all different inventory results for retailers under different scenarios which can help retailers to predict and manage the inventory. The optimization software, Lindo, is used to demonstrate that this model is capable to dynamically plan the distribution quantity. The sensitivity analysis for prices, transportation costs, and holding costs is discussed to simulate different situations during the transportation and distribution.
Adaptive Production Planning and Scheduling for the Make-to-order DNA Manufacturing System
(North Dakota State University, 2010) Song, Dan
This thesis develops an adaptive production planning and scheduling system for the make-to-order plasmid (DNA) manufacturing system. The system, which has stochastic nature and random demand, was represented by a mathematical programming model first. Then in order to solve it, discrete-event simulation models were developed to generate a feasible schedule that maximizes the production throughput in the planning horizon in a mix-product type environment. A special heuristic order selecting and splitting procedure was designed to aid the production planning and scheduling process. Experiments were conducted to evaluate the algorithm and results are compared with those obtained by using four classic dispatching rules, such as first come first served (FCFS) and shortest processing time (SPT). To take advantage of simulation results, a rule-based expert system was created with pre-defined scheduling rules. Rules regarding production planning and scheduling can be used by human schedulers easily and the system is very flexible in further extension.
Winter Road Maintenance System Design for Snow Plowing
(North Dakota State University, 2010) Kayabas, Poyraz
Winter road maintenance is critical to ensuring safety and mobility of transportation systems in regions with heavy snowfall. Winter road maintenance system design involves several inter-related decision making problems for different operations that are often performed with expensive and limited resources. This study involves developing an integrated solution methodology for depot location selection, district design, and vehicle routing problems for winter road maintenance system design in the context of snow plowing. The methodology allows decision makers to evaluate and compare different system alternatives based on a number of service level related system design criteria. The solution methodology is illustrated using the example of the road network of the Fargo District of North Dakota's transportation system. Results indicate that the methodology can be used as a decision making support tool for planning winter road maintenance operations.
Multiresponse Optimization Methodology Considering Related Quality Characteristics
(North Dakota State University, 2011) Thambidorai, Ganesh
Engineering problems often involve many conflicting quality characteristics that must be optimized simultaneously. Engineers are required to select suitable design parameter values which provide better trade-off among all quality characteristics. Multiresponse optimization is one of the most essential tools for solving engineering problems involving multiple quality characteristics. Optimizing several quality characteristics when the quality characteristics are correlated makes the optimization process more complex. The aim of this research is to evaluate the performance of several existing multiresponse optimization methods and investigate their capabilities in dealing with correlated quality characteristics. This study also investigates the impact of uncertainty in terms of input parameter selection. A new multi-response optimization approach has been proposed for solving correlated quality characteristics. The proposed approach is compared with the existing methods and found more robust in terms dealing with uncertainty in target selection. The comparative study and application of the proposed approach is demonstrated by considering two examples from the literature having correlated quality characteristics.
CFD Heat Transfer Simulation of the Human Upper Respiratory Tract for Oronasal Breathing Condition
(North Dakota State University, 2011) Srinivasan, Raghavan
In this thesis. a three dimensional heat transfer model of heated airflow through the upper human respiratory tract consisting of nasal, oral, trachea, and the first two generations of bronchi is developed using computational fluid dynamics simulation software. Various studies have been carried out in the literature investigating the heat and mass transfer characteristics in the upper human respiratory tract, and the study focuses on assessing the injury taking place in the upper human respiratory tract and identifying acute tissue damage based on level of exposure. The model considered is for the simultaneous oronasal breathing during the inspiration phase with high volumetric flow rate of 90/liters minute and a surrounding air temperature of 100 degrees centigrade. The study of the heat and mass transfer, aerosol deposition and flow characteristics in the upper human respiratory tract using computational fluid mechanics simulation requires access to a two dimensional or three dimensional model for the human respiratory tract. Depicting an exact model is a complex task since it involves the prolonged use of imaging devices on the human body. Hence a three dimensional geometric representation of the human upper respiratory tract is developed consisting of nasal cavity, oral cavity, nasopharynx, pharynx, oropharynx, trachea and first two generations of the bronchi. The respiratory tract is modeled circular in cross-section and varying diameter for various portions as identified in this study. The dimensions are referenced from the literature herein. Based on the dimensions, a simplified model representing the human upper respiratory tract is generated.This model will be useful in studying the flow characteristics and could assist in treatment of injuries to the human respiratory tract as well as help optimize drug delivery mechanism and dosages. Also a methodology is proposed to measure the characteristic dimension of the human nasal and oral cavity at the inlet/outlet points which are classified as internal measurements.
Integration of Simulation and DEA to Determine the Most Efficient Patient Appointment Scheduling Model for a Specific Clinic Setting
(North Dakota State University, 2011) Aslani, Nazanin
This study develops a method to determine the most efficient scheduling model for a specific clinic setting. The appointment scheduling system assigns clinics' timeslots to incoming requests. There are three major scheduling models: centralized scheduling model (CSM), decentralized scheduling model (DSM) and hybrid scheduling model (HSM). In order to schedule multiple appointments, CSM involves one scheduler, DSM involves all the schedulers of individual clinics and HSM combines CSM and DSM. Clinic settings are different in terms of important factors such as randomness of appointment arrival and proportion of multiple appointments. Scheduling systems operate inefficiently if there is not an appropriate match between scheduling models and clinic settings to provide balance between indicators of efficiency. A procedure is developed to determine the most efficient scheduling model by the integrated contribution of simulation and Data Envelopment Analysis (DEA). A case study serves as a guide to use and as proof for the validity of the developed procedure.
Economic Analysis of Packaging Systems
(North Dakota State University, 2011) Biradar, Vaibhav Mahadev
Packaging has a significant impact on the efficiency and effectiveness of the supply chain, where improvement can be achieved through the development and selection of an appropriate packaging system. One way to explore this is through the development and use of mathematical models that facilitate economic analysis of packaging systems. Recently, one of the most remarkable trends in logistics is the extensive use of returnable or reusable containers. Returnable container systems have increasingly been introduced in various industries to take advantages of cost savings, but it is very crucial to ensure that a reusable packaging system is an economical packaging choice. In this thesis, an extensive study of an economic analysis of disposable, recyclable, and reusable packaging systems is conducted. This includes identification of significant cost factors and variables involved in the management of disposable, recyclable and reusable packaging systems, and formulation of a mathematical model to compare total cost of packaging systems. The developed mathematical model can be used to choose the most economical packaging system for industries. The linear programming (LP) method is used to develop the mathematical model. The various new factors such as the collapsible ratio of recyclable, disposable and reusable packages have been introduced for the first time in the economic analysis of the packaging systems. The developed mathematical model can be used for a range of industries and for different industry scenarios. The packaging system information of Toyota assembly plant is used for the validation of a mathematical model. The obtained results are compared with previous research based on the same data set and results found in concert with the finding of previous research which validate the model.
Modeling and Solving Multi-Product Multi-Layer Location-Routing Problems
(North Dakota State University, 2011) Hamidi, Mohsen
Distribution is a very important component of logistics and supply chain management. Location-Routing Problem (LRP) simultaneously takes into consideration location, allocation, and vehicle routing decisions to design an optimal distribution network. Multi-layer and multi-product LRP is even more complex as it deals with the decisions at multiple layers of a distribution network where multiple products are transported within and between layers of the network. This dissertation focuses on modeling and solving complicated four-layer and multi-product LRPs which have not been tackled yet. The four-layer LRP represents a multi-product distribution network consisting of plants, central depots, regional depots, and customers. The LRP integrates location, allocation, vehicle routing, and transshipment problems. Through the modeling phase, the structure, assumptions, and limitations of the distribution network are defined and the mathematical optimization programming model that can be used to obtain optimal solutions is developed. Since the mathematical model can obtain the optimal solution only for small-size problems, through the solving phase metaheuristic algorithms are developed to solve large-size problems. GRASP (Greedy Randomized Adaptive Search Procedure), probabilistic tabu search, local search techniques, the Clarke-Wright Savings algorithm, and a node ejection chains algorithm are combined to solve two versions of the four-layer LRP. Results show that the metaheuristic can solve the problem effectively in terms of computational time and solution quality. The presented four-layer LRP, which considers realistic assumptions and limitations such as producing multiple products, limited plant production capacity, limited depot and vehicle capacity, and limited traveling distances, enables companies to mimic the real world limitations and obtain realistic results. The main objective of this research is to develop solution algorithms that can solve large-size multi-product multi-layer LRPs and produce high-quality solutions in a reasonable amount of time.
Transparent and Crack-Free Silica Aerogels
(North Dakota State University, 2012) Athmuri, Kalyan Ram
The process of making silica aerogels has been studied in detail over the past two decades due to its usage in a wide range of low end applications such as thermal insulators, supercapacitors etc., as well as high end applications like particle physics, space explorations. These applications call for control over the properties of aerogels, such as their transparency, density, porosity, pore size, and integrity. However, despite all the past research, controlling properties of aerogels is still not a fully developed science, a lot more research needs to be done. The literature on silica aerogels does not cover the study of the relation between transparency and cracks in aerogels – which can be a key factor in making aerogels for many applications. Hence, optimization of the transparency and integrity of the aerogels in order to obtain high transparency and low cracks was attempted in this thesis.
A Study on the Impact of Patient Centered Medical Home (PCMH) Implementation on Nursing Work Practice
(North Dakota State University, 2012) Shinde, Nikhil Vijay
Primary care and nursing are important components of the U.S. healthcare system and are facing challenges of quality, access, cost, time spent and inefficiencies of clinical activities. Patient Centered Medical Home (PCMH) is a newly developed care model which has the potential to overcome these challenges. The present study uses a questionnaire approach to find the impact PCMH implementation may have on nursing practice and subsequently primary care. Analysis of the data collected from the questionnaire revealed some surprising results about the nursing practice. For example nurses spend less time on direct care and more time on indirect care and documentation. The nursing demand in terms of Full Time Equivalent (FTE) for nurses decreases. The future demand for nurses (not in FTE) shows an increase after PCMH implementation. The satisfaction level and overall health of patients, patient readmission, job satisfaction of nurses and department productivity shows improvement after PCMH implementation.
The Effect of Stress on Task Capacity and Situational Awareness
(North Dakota State University, 2012) Karim, Reza Ul
In today’s industry, many occupations require manpower resources to include both labor and cognitive resources. As the technology is rapidly changing and businesses are becoming more dependent on cognitive performance, it is essential to find any effect physical stress might have on task performance. Situational awareness is also becoming an integral part of human task performance. It is critical for many operations to design systems such that the effects of physical stress, however minute, on task performance and situational awareness are considered. The test methodology developed here measures the effect of stress on cognitive task performance as a result of situational awareness related to the task. The test measured and compared task capacity among different age groups and different working groups. A comparison was made on task performance based on the effects of low level physical stress and lack of it. Response time and accuracy were measured for statistical analysis. The subject’s stress levels were measured before starting the test to create a baseline for the candidates stress level. The developed tool was able to detect the effect of stress on task performance successfully and efficiently. Subjects with previous work experience performed better both in Phase I and Phase II of the experiment as compared to subjects with no previous work experience. The analysis indicates low level stress does have significant effects on task performance. In reality, stress is an unavoidable factor in daily activities. When designing any system that requires cognitive tasks, stress needs to be considered as a contributing factor to the variability of operation.
Improvement of Wind Forecasting Accuracy and its Impacts on Bidding Strategy Optimization for Wind Generation Companies
(North Dakota State University, 2012) Li, Gong
One major issue of wind generation is its intermittence and uncertainty due to the highly volatile nature of wind resource, and it affects both the economy and the operation of the wind farms and the distribution networks. It is thus urgently needed to develop modeling methods for accurate and reliable forecasts on wind power generation. Meanwhile, along with the ongoing electricity market deregulation and liberalization, wind energy is expected to be directly auctioned in the wholesale market. This brings the wind generation companies another issue of particular importance, i.e., how to maximize the profits by optimizing the bids in the gradually deregulated electricity market based on the improved wind forecasts. As such, the main objective of this dissertation research is to investigate and develop reliable modeling methods for tackling the two issues. To reach the objective, three main research tasks are identified and accomplished. Task 1 is about testing forecasting models for wind speed and power. After a thorough investigation into currently available forecasting methods, several representative models including autoregressive integrated moving average (ARIMA) and artificial neural networks (ANN) are developed for short-term wind forecasting. The forecasting performances are evaluated and compared in terms of mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE). The results reveal that no single model can outperform others universally. This indicates the need of generating a single robust and reliable forecast by applying a post-processing method. As such, a reliable and adaptive model for short-term forecasting the wind power is developed via adaptive Bayesian model averaging algorithms in Task 2. Experiments are performed for both long-term wind assessment and short-term wind forecasting. The results show that the proposed BMA-based model can always provide adaptive, reliable, and iv comparatively accurate forecast results in terms of MAE, RMSE, and MAPE. It also provides a unified approach to tackle the challenging model selection issue in wind forecasting applications. Task 3 is about developing a modeling method for optimizing the wind power bidding process in the deregulated electricity wholesale market. The optimal bids on wind power must take into account the uncertainty in wind forecasts and wind power generation. This research investigates the application of combining improved wind forecasts with agent-based models to optimize the bid and maximize the net earnings. The WSCC 9-bus 3-machine power system network and the IEEE 30-bus 9-GenCo power system network are adopted. Both single-sided and double-sided auctions are considered. The results demonstrate that improving wind forecasting accuracy helps increase the net earnings of wind generation companies, and that the implementation of agent learning algorithms further improves the earnings. The results also verify that agent-based simulation is a viable modeling tool for providing realistic insights about the complex interactions among different market participants and various market factors.
Electrical Performance Analysis of a Novel Embedded Chip Technology
(North Dakota State University, 2012) Sarwar, Ferdous
Recently ultra-thin embedded die technology gained much attention for their reduced footprint, light weight, conformality and three-dimensional assembly capabilities. The traditional flexible circuit fabrication process showed its limitations to meet the demand for increasing packaging density. The embedded die technology can be successfully utilized to develop flexible printed circuits that will satisfy the demand for reliable and high density packaging. With a tremendous application potential in wearable and disposable electronics, the reliability of the flexible embedded die package is of paramount importance. Presented is the author's contribution to the novel fabrication process for flexible packages with ultrathin (below 50 µm) dice embedded into organic polymer substrate and the results from the investigation of the electrical performance of embedded bare dice bumped using three different techniques. In this research, embedded flexible microelectronic packaging technology was developed and reliability of different packages was evaluated through JEDEC test standards based on their electrical performance. The reliability test of the developed packages suggested the better and stable performance of stud bump bonded packages. This research also covered the thinning and handling ultra-thin chips, die metallization, stud bump formation, laser ablation of polymers, and assembly of ultra-thin die. The stud bumped flexible packages that were designed and developed in this research have promising application potential in wearable RFID tags, smart textile and three dimensional-stacked packaging, among the many other application areas.
Impact of Integrating Zone Bypass Conveyor on the Performance of a Pick-To-Light Order Picking System
(North Dakota State University, 2012) Xu, Xia
This thesis investigates the impact of integrating Zone Bypass (ZBP) conveyor to a Pick-To-Light (PTL) order picking system. This integration results in a new system (PTL+Z), which could be helpful to achieve higher levels of productivity in warehousing operations. Two options have been proposed to improve the current PTL system productivity. One is to adapt the ZBP conveyor, which will help each order to bypass unnecessary zones with nothing to pick. Another one is to better plan stock keeping units (SKU) assignment by applying level loading assignment. Mathematical models are developed to evaluate system throughput of PTL system with random assignment (PTL/R), PTL system with level loading assignment (PTL/L), PTL+Z system with random assignment (PTL+Z/R), and PTL+Z system with level loading assignment (PTL+Z/L). Simulation models are validated to test the reliability of mathematical models. Also, economic analysis is developed in term of payback period for decision purpose.
Study and Analysis of Automated Order Picking Systems
(North Dakota State University, 2012) Ambati, Akhilesh Chandra
Order picking is an essential part of order processing in warehousing and distribution operations and can be performed using manual, automated, or semi-automated systems. This thesis analyzes two automated systems, which include carousel and AS/RS (automated storage and retrieval system). The main goal of this research is to develop mathematical models to compare the performance of both systems under random and class-based storage assignments. Simulation models are used to validate the reliability of mathematical models. The outputs of mathematical and simulation models are consistent indicating carousel system with class-based assignment has the highest throughput. Economic analysis is used to estimate the payback periods required to convert from manual to AS/RS and carousel systems. The economic analysis shows that converting from manual to AS/RS with class-based assignment has the shortest payback period.
Stochastic Optimization of Sustainable Industrial Symbiosis Based Hybrid Generation Bioethanol Supply Chains
(North Dakota State University, 2013) Gonela, Vinay
Bioethanol is becoming increasingly attractive for the reasons of energy security, diversity, and sustainability. As a result, the use of bioethanol for transportation purposes has been encouraged extensively. However, designing an effective bioethanol supply chain that is both sustainable and robust is still questionable. Therefore, this research focuses on designing a bioethanol supply chain that is: 1) sustainable in improving economic, environmental, social, and energy efficiency aspects; and 2) robust to uncertainties such as bioethanol price, bioethanol demand and biomass yield. In this research, we first propose a decision framework to design an optimal bioenergy-based industrial symbiosis (BBIS) under certain constraints. In BBIS, traditionally separate plants collocate in order to efficiently utilize resources, reduce wastes and increase profits for the entire BBIS and each player in the BBIS. The decision framework combines linear programming models and large scale mixed integer linear programming model to determine: 1) best possible combination of plants to form the BBIS, and 2) the optimal multi-product network of various materials in the BBIS, such that the bioethanol production cost is reduced. Secondly, a sustainable hybrid generation bioethanol supply chain (HGBSC), which consists of 1st generation and 2nd generation bioethanol production, is designed to improve economic benefits under environmental and social restrictions. In this study, an optimal HGBSC is designed where the new 2nd generation bioethanol supply chain is integrated with the existing 1st generation bioethanol supply chain under uncertainties such as bioethanol price, bioethanol demand and biomass yield. A stochastic mixed integer linear programming (SMILP) model is developed to design the optimal configuration of HGBSC under different sustainability standards. Finally, a sustainable industrial symbiosis based hybrid generation bioethanol supply chain (ISHGBSC) is designed that incorporates various industrial symbiosis (IS) configurations into HGBSC to improve economic, environmental, social, and energy efficiency aspects of sustainability under bioethanol price, bioethanol demand and biomass yield uncertainties. A SMILP model is proposed to design the optimal ISHGBSC and Sampling Average Approximation algorithm is used as the solution technology. Case studies of North Dakota are used as an application. The results provide managerial insights about the benefits of BBIS configurations within HGBSC.
Applying Simulation and Genetic Algorithm for Patient Appointment Scheduling Optimization
(North Dakota State University, 2013) Peng, Yidong
In this study, we discuss the implementation of integrated simulation and genetic algorithm for patient scheduling optimization under two different settings, namely the "traditional" scheduling system and the "open access" scheduling system. Under the "traditional" setting, we propose a two-phase approach for designing a weekly scheduling template for outpatient clinics providing multiple types of services. Our results demonstrate that the two-phase approach can efficiently find the promising weekly appointment scheduling templates for outpatient clinics. Under the "open access" setting, we propose a discrete event simulation and genetic algorithm (DES-GA) approach to find the heuristic optimal scheduling template for the clinic allowing both open access and walk-in patients. The solution provides scheduling templates consisting of not only the optimal number of reservations for open access appointments and walk-ins, but also the optimized allocation of these reserved slots, by minimizing the average cost per admission of open access or walk-in patient.
Modeling and Optimization of Biofuel Supply Chain Considering Uncertainties, Hedging Strategies, and Sustainability Concepts
(North Dakota State University, 2013) Awudu, Iddrisu Kaasowa
Due to energy crisis and environmental concerns, alternative energy has attracted a lot of attention in both industry and academia. Biofuel is one type of renewable energy that can reduce the reliance on fossil fuel, and also help reduce environmental effect and provide social benefits. However, to deliver a competitive biofuel product requires a robust supply chain. The biofuel supply chain (BSC) consists of raw material sourcing, transporting of raw materials to pre-treatment and biorefinery sites, pre-treating the raw material, biofuel production, and transporting of the produced biofuel to the final demand zones. As uncertainties are involved throughout the supply chain, risks are introduced. We first propose a stochastic production planning model for a biofuel supply chain under demand and price uncertainties. A stochastic linear programming model is proposed and Benders decomposition (BD) with Monte Carlo simulation technique is applied to solve the proposed model. A case study compares the performance of a deterministic model and the proposed stochastic model. The results indicate that the proposed model obtain higher expected profit than the deterministic model under different uncertainty settings. Sensitivity analyses are performed to gain management insights. Secondly, a hedging strategy is proposed in a hybrid generation biofuel supply chain (HGBSC). A hedging strategy can purchase corn either through futures or spot, while the ethanol end-product sale is hedged using futures. A two-stage stochastic linear programming method with hedging strategy is proposed, and a Multi-cut Benders Decomposition Algorithm is used to solve the proposed model. Prices of feedstock and ethanol end-products are modeled as a mean reversion (MR). The results for both hedging and non-hedging are compared for profit realizations, and the hedging is better as compared to non-hedging for smaller profits. Further sensitivity analyses are conducted to provide managerial insights. Finally, sustainability concepts, which include economic, environmental, and social sustainability, are incorporated in the HGBSC. A two-stage stochastic mixed integer linear programming approach is used, and the proposed HGBSC model is solved using the Lagrangean Relaxation (LR) and Sample Average Approximation (SAA). A representative case study in North Dakota is used for this study.
Challenges and Barriers Affecting the Success of Total Quality Management
(North Dakota State University, 2013) Rokke, Connie
This thesis is a compilation of two papers. The first provides an overview of TQM from its beginnings through today's business climate. The fundamental principles of TQM are explored and the benefits identified. A review of the challenges and barriers that prohibit most companies from achieving these successes is conducted to understand why well intentioned companies are not always able to sustain this management technique. The second paper analyzes these challenges and barriers of TQM attempting to quantify their impact on the success of a TQM program. This study analyzes survey data using Structural Equation Modeling. The findings indicate the challenges associated with some of the TQM Principles are correlated and a few of them have an impact on the success of a TQM program. This research is unique in its attempt to apply quantifiable measures to the challenges faced by organizations that endeavor to implement TQM programs.
Optimization Models for Scheduling and Rescheduling Elective Surgery Patients Under the Constraint of Downstream Units
(North Dakota State University, 2013) Erdem, Ergin
Healthcare is a unique industry in terms of the associated requirements and services provided to patients. Currently, healthcare industry is facing challenges of reducing the cost and improving the quality and accessibility of service. Operating room is one of the biggest major cost and revenue centers in any healthcare facility. In this study, we develop optimization models and the corresponding solution strategies for addressing the problem of scheduling and rescheduling of the elective patients for surgical operations in the operating room. In the first stage, scheduling of the elective patients based on the availability of the resources is optimized. The resources considered in the study are the availability of the operating rooms, surgical teams, and the beds/equipment in the downstream post anesthesia care units (PACUs). Discrete distributions governing surgical durations for selected surgical specialties are developed for representing variability for duration of surgery. Based on the distributions, a stochastic mathematical programming model is developed. It is indicated that with the increase of problem sizes, the model may not be solved by using a leading commercial solver for optimization problems. As a result, a heuristic solution approach based on genetic algorithm is also developed. It is found out that the genetic algorithm provides close results as compared to the commercial solver in terms of solution quality. For large problem sizes, where the commercial solver is unable to solve the problem due to the memory restrictions, the genetic algorithm based approach is able to find a solution within a reasonable amount of computation time. In the second stage, the rescheduling of the elective patients due to the sudden arrival of the emergency patients is considered. A mathematical programming model for minimizing the costs related with expanding the current capacity and disruption caused by the inclusion of the emergency patient is developed. Also, two different solution approaches are brought forward, one with using the commercial solver, and the other based on genetic algorithm. Genetic algorithm based approach can always make efficient decision regarding whether to accept the emergency patients and how to minimize the reshuffling effort of the original elective surgery schedule.

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