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Articles 1 - 8 of 8
Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering
Influence Of The Inherent Safety Principles On Quantitative Risk In Process Industry: Application Of Genetic Algorithm Process Optimization (Gapo), Mehdi Jahangiri, Abolfazl Moghadasi, Mojtaba Kamalinia, Farid Sadeghianjahromi, Sean Banaee
Influence Of The Inherent Safety Principles On Quantitative Risk In Process Industry: Application Of Genetic Algorithm Process Optimization (Gapo), Mehdi Jahangiri, Abolfazl Moghadasi, Mojtaba Kamalinia, Farid Sadeghianjahromi, Sean Banaee
Community & Environmental Health Faculty Publications
Inherent safety (IS) refers to a set of measures that enhance the safety level of processes and equipment, rendering additional equipment and/or add-ons. The early design phase of processes is suited best for implementation of IS strategies as some of such strategies either are impossible to be implemented at the operation phase or substantially increase costs. The purpose of this study is to present a new approach called genetic algorithm process optimization (GAPO), by which processes can be made inherently safer even at the operation phase. This study simulates the IS principle, assessing its impact on quantitative risk and the …
Instance Selection Using Genetic Algorithms For An Intelligent Ensemble Trading System, Youngmin Kim, David Lee Enke
Instance Selection Using Genetic Algorithms For An Intelligent Ensemble Trading System, Youngmin Kim, David Lee Enke
Engineering Management and Systems Engineering Faculty Research & Creative Works
Instance selection is a way to remove unnecessary data that can adversely affect the prediction model, thereby selecting representative and relevant data from the original data set that is expected to improve predictive performance. Instance selection plays an important role in improving the scalability of data mining algorithms and has also proven to be successful over a wide range of classification problems. However, instance selection using an evolutionary approach, as proposed in this study, is different from previous methods that have focused on improving accuracy performance in the stock market (i.e., Up or Down forecast). In fact, we propose a …
Genetic Algorithm Optimization Of Sos Meta-Architecture Attributes For Fuzzy Rule Based Assessments, Andrew Renault, Cihan H. Dagli
Genetic Algorithm Optimization Of Sos Meta-Architecture Attributes For Fuzzy Rule Based Assessments, Andrew Renault, Cihan H. Dagli
Engineering Management and Systems Engineering Faculty Research & Creative Works
The analysis of an acknowledged systems of systems (SoS) meta-architecture requires a preliminary method for potential trade space exploration to ensure compliance to evolving capability requirements. It is important to assess the SoS meta-architecture concept to ensure that it satisfies all stakeholder needs and requirements in the early stages of development. There are numerous linguistic terms called key performance attributes (KPAs) that could be used to assess the different aspects of the architectures capabilities, however, too many KPAs could complicate the assessment. The initial population of suitable KPAs is reduced through non-derivative based optimization employed by a genetic algorithm (GA) …
Group Scheduling In A Cellular Manufacturing Shop To Minimise Total Tardiness And Nt: A Comparative Genetic Algorithm And Mathematical Modelling Approach, Gokhan Egilmez, Emre M. Mese, Bulent Erenay, Gürsel A. Süer
Group Scheduling In A Cellular Manufacturing Shop To Minimise Total Tardiness And Nt: A Comparative Genetic Algorithm And Mathematical Modelling Approach, Gokhan Egilmez, Emre M. Mese, Bulent Erenay, Gürsel A. Süer
Mechanical and Industrial Engineering Faculty Publications
In this paper, family and job scheduling in a cellular manufacturing shop is addressed where jobs have individual due dates. The objectives are to minimise total tardiness and the number of tardy jobs. Family splitting among cells is allowed but job splitting is not. Two optimisation methods are employed in order to solve this problem, namely mathematical modelling (MM) and genetic algorithm (GA). The results showed that GA found the optimal solution for most of the problems with high frequency. Furthermore, the proposed GA is efficient compared to the MM especially for larger problems in terms of execution times. Other …
Optimizing Macd Parameters Via Genetic Algorithms For Soybean Futures, Phoebe S. Wiles, David Lee Enke
Optimizing Macd Parameters Via Genetic Algorithms For Soybean Futures, Phoebe S. Wiles, David Lee Enke
Engineering Management and Systems Engineering Faculty Research & Creative Works
To create profits, traders must time the market correctly and enter and exit positions at ideal times. Finding the optimal time to enter the market can be quite daunting. The soybean market can be volatile and complex. Weather, sentiment, supply, and demand can all affect the price of soybeans. Traders typically use either fundamental analysis or technical analysis to predict the market for soybean futures' contracts. Every agricultural future's contract or security contract is different in its nature, volatility, and structure. Therefore, the purpose of this research is to optimize the moving average convergence divergence parameter values from traditionally used …
A Computational Intelligence Approach To System-Of-Systems Architecting Incorporating Multi-Objective Optimization, David M. Curry, Cihan H. Dagli
A Computational Intelligence Approach To System-Of-Systems Architecting Incorporating Multi-Objective Optimization, David M. Curry, Cihan H. Dagli
Engineering Management and Systems Engineering Faculty Research & Creative Works
A computational intelligence approach to system-of-systems architecting is developed using multi-objective optimization. Such an approach yields a set of optimal solutions (the Pareto set) which has both advantages and disadvantages. The primary benefit is that a set of solutions provides a picture of the optimal solution space that a single solution cannot. The primary difficulty is making use of a potentially infinite set of solutions. Therefore, a significant part of this approach is the development of a method to model the solution set with a finite number of points allowing the architect to intelligently choose a subset of optimal solutions …
Reducing Uncertainty In Technology Selection For Long Life Cycle Engineering Designs, Halil I. Ozdemir, C. Ariel Pinto, Resit Unal, Charles B. Keating, Colin Britcher, Sila Çetinkaya (Ed.), J. K. Ryan (Ed.)
Reducing Uncertainty In Technology Selection For Long Life Cycle Engineering Designs, Halil I. Ozdemir, C. Ariel Pinto, Resit Unal, Charles B. Keating, Colin Britcher, Sila Çetinkaya (Ed.), J. K. Ryan (Ed.)
Engineering Management & Systems Engineering Faculty Publications
The best capabilities are usually achieved by having the latest technologies in defense systems. However, including the new, usually immature, technologies in a system design does not always easily result in achieving the capabilities at the right level, at an affordable cost, and in a timely manner. Many programs have suffered from immature technologies as cost overruns, late or no deliveries, and poor performance levels. Another impact of technology selection appears as obsolescence after the deployment of systems, or even before the deployment of the system. As the technologies of a system become obsolete, the cost of maintaining the system …
A Parallel Genetic-Neuro Scheduler For Job-Shop Scheduling Problems, H. C. Lee, Cihan H. Dagli
A Parallel Genetic-Neuro Scheduler For Job-Shop Scheduling Problems, H. C. Lee, Cihan H. Dagli
Engineering Management and Systems Engineering Faculty Research & Creative Works
Despite relentless efforts on developing new approaches, there are still large gaps between schedules generated through various planning systems, and schedules actually used in the shop floor environment. An effective schedule generation is a knowledge intensive activity requiring a comprehensive model of a factory and its environment at all times. There are four main difficulties that need to be addressed. First, job shop scheduling belongs to a class of NP-hard problems. Second, it is a highly constrained problem that changes from shop to shop. Third, scheduling decisions depend upon other decisions which are not isolated from other functions. Thus, it …