Operational Research Commons^™

Multistage Random Key Genetic Algortihm Optimization For Scheduling Flexible Flow Lines With Sequence Depenedent Setup Times, Aadithan Anbuvanan

All Theses

This thesis proposes a new variation to the Random Key Genetic Algorithm (RKGA) for scheduling optimization in flexible flow line manufacturing with sequence dependent setup times. The proposed RKGA representation decodes scheduling information independently at each stage, unlike the traditional RKGA, which is only sequenced based on the first stage, limiting flexibility. The proposed method's performance is compared to the traditional method with varying numbers of jobs and stages. It is compared regarding performance ratio and statistical significance of differences through the Wilcoxon Signed Rank Test. Results show that the proposed RKGA outperformed traditional RKGA in high complexity (8 Stage …

A Multi-Objective Approach To Optimal Deployment Policies For Wireless Sensor Networks Using Drop Points, Noah E. Fox

Theses and Dissertations

This research addresses the development of deployment policies for aerially dropped sensors in a wireless sensor network (WSN). Multi-objective genetic algorithm (GA) and simulated annealing meta-heuristic techniques, along with Monte Carlo simulation are used to identify policies with the aim of maximizing coverage and minimizing the number of sensors deployed. The policies developed from these techniques are then compared against uniform sensor distribution, as well as initial deployment policies that focus sensors in the center and edge of the region, as well as evenly deployed over the region. A total of 29 non-dominated policies were identified from the GA and …

Modeling And Solution Methodologies For Mixed-Model Sequencing In Automobile Industry, Ibrahim Ozan Yilmazlar

All Dissertations

The global competitive environment leads companies to consider how to produce high-quality products at a lower cost. Mixed-model assembly lines are often designed such that average station work satisfies the time allocated to each station, but some models with work-intensive options require more than the allocated time. Sequencing varying models in a mixed-model assembly line, mixed-model sequencing (MMS), is a short-term decision problem that has the objective of preventing line stoppage resulting from a station work overload. Accordingly, a good allocation of models is necessary to avoid work overload. The car sequencing problem (CSP) is a specific version of the …

Visibility Based Hospital Inpatient Unit Design., Uttam Karki

Electronic Theses and Dissertations

Patient fall is one of the adverse events in an inpatient unit of a hospital that can lead to disability and/or mortality. Healthcare literature suggests that increased visibility of patients by unit nurses is essential to improve patient monitoring and, in turn, reduce falls. However, such research has been descriptive in nature and does not provide an understanding of the characteristics of an optimal inpatient unit layout from a visibility-standpoint. This dissertation fills significant voids in this domain and adds much-needed realism to develop insights that hospital decision-makers can use to design their inpatient unit layout. Our first contribution (Chapter …

A Study Of Scheduling Problems With Sequence Dependent Restrictions And Preferences, Nitin Srinath

All Dissertations

In some applications like fabric dying, semiconductor wafer processing, and flexible manufacturing, the machines being used to process jobs must be set up and serviced frequently. These setup processes and associated setup times between jobs often depend on the jobs and the sequence in which jobs are placed onto machines. That is, the scheduling of jobs on machines must account for the sequence-dependent setup times as well. These setup times can be a major factor in operational costs. In fabric dyeing processes, the sequence in which jobs are processed is also important for quality, i.e., there is a strong preference …

Simulation-Based Optimization: Implications Of Complex Adaptive Systems And Deep Uncertainty, Andreas Tolk

VMASC Publications

Within the modeling and simulation community, simulation-based optimization has often been successfully used to improve productivity and business processes. However, the increased importance of using simulation to better understand complex adaptive systems and address operations research questions characterized by deep uncertainty, such as the need for policy support within socio-technical systems, leads to the necessity to revisit the way simulation can be applied in this new area. Similar observations can be made for complex adaptive systems that constantly change their behavior, which is reflected in a continually changing solution space. Deep uncertainty describes problems with inadequate or incomplete information about …

Quantitative Methods For Select Problems In Facility Location And Facility Logistics, Bin Li

Graduate Theses and Dissertations

This dissertation presented three logistics problems. The first problem is a parallel machine scheduling problems that considers multiple unique characteristics including release dates, due dates, limited machine availability and job splitting. The objective of is to minimize the total amount of time required to complete work. A mixed integer programming model is presented and a heuristic is developed for solving the problem. The second problem extends the first parallel scheduling problem to include two additional practical considerations. The first is a setup time that occurs when warehouse staff change from one type of task to another. The second is a …

Adaptive Scheduling For Operating Room Management, Honghan Ye

Theses and Dissertations--Mechanical and Aerospace Engineering

The perioperative process in hospitals can be modelled as a 3-stage no-wait flow shop. The utilization of OR units and the average waiting time of patients are related to makespan and total completion time, respectively. However, minimizations of makespan and total completion time are NP-hard and NP-complete. Consequently, achieving good effectiveness and efficiency is a challenge in no-wait flow shop scheduling. The average idle time (AIT) and current and future idle time (CFI) heuristics are proposed to minimize makespan and total completion time, respectively. To improve effectiveness, current idle times and future idle times are taken into consideration …

Current Barriers To Successful Implementation Of Fist Principles, Brandon Keller, Joseph R. Wirthlin

Faculty Publications

The Fast, Inexpensive, Simple, and Tiny (FIST) framework proposes a broad set of organizational values, but provides limited guidance on practical implementation. Implementing FIST principles requires clarifying the definitions of “fast,” “inexpensive,” and “simple,” recognizing where FIST does and does not apply. Additionally, a subset of the FIST heuristics was expanded upon to increase their usefulness for practitioners. The primary research findings are that FIST principles are less conducive for highly complex or novel systems, immature technologies, future needs, acquisitions in early development phases, or when performance is the foremost value. FIST principles were also found to be constrained by …

A Hybrid Lehmer Code Genetic Algorithm And Its Application On Traveling Salesman Problems, Jun Zhang

Engineering Management & Systems Engineering Theses & Dissertations

Traveling Salesman Problems (TSP) is a widely studied combinatorial optimization problem. The goal of the TSP is to find a tour which begins in a specific city, visits each of the remaining cities once and returns to the initial cities such that the objective functions are optimized, typically involving minimizing functions like total distance traveled, total time used or total cost.

Genetic algorithms were first proposed by John Holland (1975). It uses an iterative procedure to find the optimal solutions to optimization problems.

This research proposed a hybrid Lehmer code Genetic Algorithm. To compensate for the weaknesses of traditional genetic …

Decision Support Models For Design Of Fortified Distribution Networks, Qingwei Li

USF Tampa Graduate Theses and Dissertations

Lean distribution networks have been facing an increased exposure to the risk of unpredicted disruptions causing significant economic forfeitures. At the same time, the existing literature contains very few studies that examine the impact of fortification of facilities for improving network reliability. This dissertation presents three related classes of models that support the design of reliable distribution networks. The models extend the uncapacitated P-median and fixed-charge location models by considering heterogeneous facility failure probabilities, supplier backups, and facility fortification within a finite budget. The first class of models considers binary fortification via linear fortification functions. The second class of models …

Developing New Multidimensional Knapsack Heuristics Based On Empirical Analysis Of Legacy Heuristics, Yong Kun Cho

Theses and Dissertations

The multidimensional knapsack problem (MKP) has been used to model a variety of practical optimization and decision-making applications. Due to its combinatorial nature, heuristics are often employed to quickly find good solutions to MKPs. While there have been a variety of heuristics proposed for the MKP, and a plethora of empirical studies comparing the performance of these heuristics, little has been done to garner a deeper understanding of heuristic performance as a function of problem structure. This dissertation presents a research methodology, empirical and theoretical results explicitly aimed at gaining a deeper understanding of heuristic procedural performance as a function …

Computer-Based Methods For Constructing Two-Level Fractional-Factorial Experimental Designs With A Requirement Set, Steven L. Forsythe

Theses and Dissertations

This dissertation developed four methodologies for computer-aided experimental design of two-level fractional factorial designs with requirement sets (DOE/RS). The requirement sets identify all the experimental factors and the appropriate interaction terms to be evaluated in the experiment. Taguchi graphs and similar manual methods provide techniques for solving the DOE/RS problem. Unfortunately, these methods are limited because they become difficult to use as the number of factors or interaction terms exceeds ten. This research showed that the DOE/RS problem belongs to a class of difficult-to-solve problems known as NP-Complete. It is the combinatorial nature of NP-Complete problems that causes them to …