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Articles 1 - 15 of 15
Full-Text Articles in Engineering
Essays On Perioperative Services Problems In Healthcare, Amogh S. Bhosekar
Essays On Perioperative Services Problems In Healthcare, Amogh S. Bhosekar
All Dissertations
One of the critical challenges in healthcare operations management is to efficiently utilize the expensive resources needed while maintaining the quality of care provided. Simulation and optimization methods can be effectively used to provide better healthcare services. This can be achieved by developing models to minimize patient waiting times, minimize healthcare supply chain and logistics costs, and maximize access. In this proposal, we study some of the important problems in healthcare operations management. More specifically, we focus on perioperative services and study scheduling of operating rooms (ORs) and management of necessary resources such as staff, equipment, and surgical instruments. We …
A Study Of Scheduling Problems With Sequence Dependent Restrictions And Preferences, Nitin Srinath
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 …
The Traveling Salesman Problem: An Analysis And Comparison Of Metaheuristics And Algorithms, Mason Helmick
The Traveling Salesman Problem: An Analysis And Comparison Of Metaheuristics And Algorithms, Mason Helmick
Senior Honors Theses
One of the most investigated topics in operations research is the Traveling Salesman Problem (TSP) and the algorithms that can be used to solve it. Despite its relatively simple formulation, its computational difficulty keeps it and potential solution methods at the forefront of current research. This paper defines and analyzes numerous proposed solutions to the TSP in order to facilitate understanding of the problem. Additionally, the efficiencies of different heuristics are studied and compared to the aforementioned algorithms’ accuracy, as a quick algorithm is often formulated at the expense of an exact solution.
A Literature Review On Combining Heuristics And Exact Algorithms In Combinatorial Optimization, Hesamoddin Tahami, Hengameh Fakhravar
A Literature Review On Combining Heuristics And Exact Algorithms In Combinatorial Optimization, Hesamoddin Tahami, Hengameh Fakhravar
Engineering Management & Systems Engineering Faculty Publications
There are several approaches for solving hard optimization problems. Mathematical programming techniques such as (integer) linear programming-based methods and metaheuristic approaches are two extremely effective streams for combinatorial problems. Different research streams, more or less in isolation from one another, created these two. Only several years ago, many scholars noticed the advantages and enormous potential of building hybrids of combining mathematical programming methodologies and metaheuristics. In reality, many problems can be solved much better by exploiting synergies between these approaches than by “pure” classical algorithms. The key question is how to integrate mathematical programming methods and metaheuristics to achieve such …
Energy Consumption And Tardiness Improvement For A Flexible Job Shop And A Warehouse, Ahmad Ebrahimi
Energy Consumption And Tardiness Improvement For A Flexible Job Shop And A Warehouse, Ahmad Ebrahimi
LSU Doctoral Dissertations
In recent years, energy consumption (EC) is studied to see its effects on monetary and non-monetary costs in manufacturing and warehousing. EC in manufacturing and warehousing, however, needs to be studied with conventional performance measures such as production/operation tardiness since there is a trade-off between EC and tardiness costs. Therefore, this research is conducted with four objectives to study EC and tardiness for a flexible job shop and a warehouse as follows. The first objective of this research is to integrate job scheduling and layout, which are interrelated in improving EC and tardiness for a flexible job shop. Thus, we …
Metaheuristics For The Generalized Quadratic Assignment Problem, Roseline Mostafa
Metaheuristics For The Generalized Quadratic Assignment Problem, Roseline Mostafa
Graduate Theses, Dissertations, and Problem Reports
The generalized quadratic assignment problem (GQAP) is the task of assigning a set of facilities to a set of locations such that the sum of the assignment and transportation costs is minimized. The facilities may have different space requirements, and the locations may have varying space capacities. Also, multiple facilities may be assigned to each location such that space capacity is not exceeded. In this research, an application of the GQAP is presented for assigning a set of machines to a set of locations on the plant floor. Two meta-heuristics are proposed for solving the GQAP: tabu search (TS) and …
Simulation And Optimization Of Ant Colony Optimization Algorithm For The Stochiastic Uncapacitated Location-Allocation Problem, Jean-Paul Arnaout, Georges Arnaout, John El Khoury
Simulation And Optimization Of Ant Colony Optimization Algorithm For The Stochiastic Uncapacitated Location-Allocation Problem, Jean-Paul Arnaout, Georges Arnaout, John El Khoury
Engineering Management & Systems Engineering Faculty Publications
This study proposes a novel methodology towards using ant colony optimization (ACO) with stochastic demand. In particular, an optimizationsimulation-optimization approach is used to solve the Stochastic uncapacitated location-allocation problem with an unknown number of facilities, and an objective of minimizing the fixed and transportation costs. ACO is modeled using discrete event simulation to capture the randomness of customers’ demand, and its objective is to optimize the costs. On the other hand, the simulated ACO’s parameters are also optimized to guarantee superior solutions. This approach’s performance is evaluated by comparing its solutions to the ones obtained using deterministic data. The results …
Meta-Raps Hybridization With Machine Learning Algorithms, Fatemah Al-Duoli
Meta-Raps Hybridization With Machine Learning Algorithms, Fatemah Al-Duoli
Engineering Management & Systems Engineering Theses & Dissertations
This dissertation focuses on advancing the Metaheuristic for Randomized Priority Search algorithm, known as Meta-RaPS, by integrating it with machine learning algorithms. Introducing a new metaheuristic algorithm starts with demonstrating its performance. This is accomplished by using the new algorithm to solve various combinatorial optimization problems in their basic form. The next stage focuses on advancing the new algorithm by strengthening its relatively weaker characteristics. In the third traditional stage, the algorithms are exercised in solving more complex optimization problems. In the case of effective algorithms, the second and third stages can occur in parallel as researchers are eager to …
A Mathematical Model And Metaheuristics For Time Dependent Orienteering Problem, Aldy Gunawan, Zhi Yuan, Hoong Chuin Lau
A Mathematical Model And Metaheuristics For Time Dependent Orienteering Problem, Aldy Gunawan, Zhi Yuan, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
This paper presents a generalization of the Orienteering Problem, the Time-Dependent Orienteering Problem (TDOP) which is based on the real-life application of providing automatic tour guidance to a large leisure facility such as a theme park. In this problem, the travel time between two nodes depends on the time when the trip starts. We formulate the problem as an integer linear programming (ILP) model. We then develop various heuristics in a step by step fashion: greedy construction, local search and variable neighborhood descent, and two versions of iterated local search. The proposed metaheuristics were tested on modified benchmark instances, randomly …
Data Mining Based Hybridization Of Meta-Raps, Fatemah Al-Duoli, Ghaith Rabadi
Data Mining Based Hybridization Of Meta-Raps, Fatemah Al-Duoli, Ghaith Rabadi
Engineering Management & Systems Engineering Faculty Publications
Though metaheuristics have been frequently employed to improve the performance of data mining algorithms, the opposite is not true. This paper discusses the process of employing a data mining algorithm to improve the performance of a metaheuristic algorithm. The targeted algorithms to be hybridized are the Meta-heuristic for Randomized Priority Search (Meta-RaPS) and an algorithm used to create an Inductive Decision Tree. This hybridization focuses on using a decision tree to perform on-line tuning of the parameters in Meta-RaPS. The process makes use of the information collected during the iterative construction and improvement phases Meta-RaPS performs. The data mining algorithm …
Solving Combinatorial Optimization Problems Using Genetic Algorithms And Ant Colony Optimization, Gautham Puttur Rajappa
Solving Combinatorial Optimization Problems Using Genetic Algorithms And Ant Colony Optimization, Gautham Puttur Rajappa
Doctoral Dissertations
This dissertation presents metaheuristic approaches in the areas of genetic algorithms and ant colony optimization to combinatorial optimization problems.
Ant colony optimization for the split delivery vehicle routing problem
An Ant Colony Optimization (ACO) based approach is presented to solve the Split Delivery Vehicle Routing Problem (SDVRP). SDVRP is a relaxation of the Capacitated Vehicle Routing Problem (CVRP) wherein a customer can be visited by more than one vehicle. The proposed ACO based algorithm is tested on benchmark problems previously published in the literature. The results indicate that the ACO based approach is competitive in both solution quality and solution …
Incorporating Memory And Learning Mechanisms Into Meta-Raps, Arif Arin
Incorporating Memory And Learning Mechanisms Into Meta-Raps, Arif Arin
Engineering Management & Systems Engineering Theses & Dissertations
Due to the rapid increase of dimensions and complexity of real life problems, it has become more difficult to find optimal solutions using only exact mathematical methods. The need to find near-optimal solutions in an acceptable amount of time is a challenge when developing more sophisticated approaches. A proper answer to this challenge can be through the implementation of metaheuristic approaches. However, a more powerful answer might be reached by incorporating intelligence into metaheuristics.
Meta-RaPS (Metaheuristic for Randomized Priority Search) is a metaheuristic that creates high quality solutions for discrete optimization problems. It is proposed that incorporating memory and learning …
Metaheuristics For Hub Location Models, Ornurai Sangsawang
Metaheuristics For Hub Location Models, Ornurai Sangsawang
All Dissertations
In this research, we propose metaheuristics for solving two p-hub median problems.. The first p-hub median problem, which is NP-hard, is the uncapacitated single p-hub median problem (USApHMP). In this problem, metaheuristics such as genetic algorithms, simulated annealing and tabu search, are applied in different types of representations. Caching is also
applied to speed up computational time of the algorithms. The results clearly demonstrate that tabu search with a permutation solution representation, augmented with caching is the highest performing method, both in terms of solution quality and computational time among these algorithms for the USApHMP. We also investigate the performance …
A Particle Swarm Optimization Using Random Keys For Flexible Flow Shop Scheduling Problem With Sequence Dependent Setup Times, Vinodh Sankaran
A Particle Swarm Optimization Using Random Keys For Flexible Flow Shop Scheduling Problem With Sequence Dependent Setup Times, Vinodh Sankaran
All Theses
In this research, a particle swarm optimization algorithm (PSO) using random keys is developed to schedule flexible flow lines with sequence dependent setup times to minimize makespan. The flexible flow line scheduling problem is a branch of production scheduling and is found in industries such as printed circuit board and automobile manufacturing. It is well known that this problem is NP-hard. For this reason, we approach the problem by implementing a particle swarm optimization (PSO), a metaheuristic which is inspired by the motion of a flock of birds or a school of fish searching for food. The proposed PSO has …
Tuning Tabu Search Strategies Via Visual Diagnosis, Steven Halim, Hoong Chuin Lau
Tuning Tabu Search Strategies Via Visual Diagnosis, Steven Halim, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
While designing working metaheuristics can be straightforward, tuning them to solve the underlying combinatorial optimization problem well can be tricky. Several tuning methods have been proposed but they do not address the new aspect of our proposed classification of the metaheuristic tuning problem: tuning search strategies. We propose a tuning methodology based on Visual Diagnosis and a generic tool called Visualizer for Metaheuristics Development Framework(V-MDF) to address specifically the problem of tuning search (particularly Tabu Search) strategies. Under V-MDF, we propose the use of a Distance Radar visualizer where the human and computer can collaborate to diagnose the occurrence of …