Optimisation of a Robotic Workcell Layout Using Genetic Algorithms

2005 ◽  
Author(s):  
Siang-Kok Sim ◽  
Meng-Leong Tay ◽  
Ahmad Khairyanto
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Cycle Time ◽  
Production Cycle ◽  
Robot Arm ◽  
Layout Problem ◽  
Common Features ◽  
Placement Method ◽  
The Common

With the advent of robots in modern-day manufacturing workcells, optimization of robotic workcell layout (RWL) is crucial in ensuring the minimization of the production cycle time. Although RWL share many aspects with the well-known facility layout problem (FLP), there are features which set the RWL apart. However, the common features which they share enable approaches in FLP to be ported over to RWL. One heuristic gaining popularity is genetic algorithm (GA). In this paper, we present a GA approach to optimizing RWL by using the distance covered by the robot arm as a means of gauging the degree of optimization. The approach is constructive: the different stations within the workcell are placed one by one in the development of the layout. The placement method adopted is based on the spiral placement method first broached by Islier (1998). The algorithm was implemented in Visual C++ and a case study assessed its performance.

Using Genetic Algorithms to Set Target Values for Engineering Characteristics in the House of Quality

1999 ◽  
Author(s):  
Patricia Brackin ◽  
Jonathan Colton
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Penalty Functions ◽  
Preliminary Design ◽  
Test Results ◽  
House Of Quality ◽  
Design Specifications ◽  
Engineering Characteristics ◽  
Target Values

Abstract As part of a strategy for obtaining preliminary design specifications from the House of Quality, genetic algorithms were used to generate and optimize preliminary design specifications for an automotive case study. This paper describes the House of Quality for the automotive case study. In addition, the genetic algorithm chosen, the genetic coding, the methods used for mutation and reproduction, and the fitness and penalty functions are descrobed. Methods for determining convergence are examined. Finally, test results show that the genetic algorithm produces reasonable preliminary design specifications.

scholarly journals Tsunami Evacuation Routes Optimization using Genetic Algorithms: A Case Study in Palu

2020 ◽  
Vol 331 ◽  
pp. 01008
Author(s):  
Yusuf Anshori ◽  
Dwi Shinta Angreni ◽  
Suci Ramadhani Arifin
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Tsunami Disaster ◽  
Evacuation Routes ◽  
Evacuation Route

Palu area and its surroundings, besides being very prone to earthquakes, are also prone to tsunamis. A devastating earthquake occurred On September 28, 2018, followed by a destructive and deadly tsunami that struck Palu Bay. This makes the need for proper planning in overcoming the tsunami disaster. One of them is by showing the evacuation route for people in tsunami-prone areas. This study aims to show the best route to the safe point of the tsunami using Genetic Algorithm. The results of the studies show that the best route for tsunami evacuations can be provided best depend on the available of the safe points. Some clusters, namely 9, 10, and 12 have few safe points, limiting people to access a safe location from the tsunami.

scholarly journals Optimal sampling in a noisy genetic algorithm for risk-based remediation design

2003 ◽  
Vol 5 (1) ◽  
pp. 11-25 ◽  
Author(s):  
Gayathri Gopalakrishnan ◽  
Barbara S. Minsker ◽  
David E. Goldberg
Keyword(s):  
Genetic Algorithm ◽  
Monte Carlo Simulation ◽  
Genetic Algorithms ◽  
Monte Carlo ◽  
Fitness Function ◽  
Sampling Strategies ◽  
Robust Designs ◽  
Highly Efficient ◽  
Efficient Sampling

A groundwater management model has been developed that predicts human health risks and uses a noisy genetic algorithm to identify promising risk-based corrective action (RBCA) designs. Noisy genetic algorithms are simple genetic algorithms that operate in noisy environments. The noisy genetic algorithm uses a type of noisy fitness function (objective function) called the sampling fitness function, which utilises Monte-Carlo-type sampling to find robust designs. Unlike Monte Carlo simulation modelling, however, the noisy genetic algorithm is highly efficient and can identify robust designs with only a few samples per design. For hydroinformatic problems with complex fitness functions, however, it is important that the sampling be as efficient as possible. In this paper, methods for identifying efficient sampling strategies are investigated and their performance evaluated using a case study of a RBCA design problem. Guidelines for setting the parameter values used in these methods are also developed. Applying these guidelines to the case study resulted in highly efficient sampling strategies that found RBCA designs with 98% reliability using as few as 4 samples per design. Moreover, these designs were identified with fewer simulation runs than would likely be required to identify designs using trial-and-error Monte Carlo simulation. These findings show considerable promise for applying these methods to complex hydroinformatic problems where substantial uncertainty exists but extensive sampling cannot feasibly be done.

Eating Disorders

2019 ◽  
pp. 3-10
Author(s):  
Carolyn Black Becker ◽  
Nicholas R. Farrell ◽  
Glenn Waller
Keyword(s):  
Mental Health ◽  
Eating Disorders ◽  
Binge Eating ◽  
Mental Health Disorders ◽  
Body Image Disturbance ◽  
Significant Morbidity ◽  
Common Features ◽  
The Common ◽  
Health Disorders

Eating disorders are serious mental health disorders that are associated with significant morbidity and mortality. This chapter provides eating disorders clinicians with the necessary understanding of both the differences associated with specific ED diagnoses and the transdiagnostic features that commonly present across diagnoses. Many, if not all, of the common features can be targeted using exposure therapy, which is discussed in subsequent chapters. The authors specifically address the most common features of anorexia nervosa, bulimia nervosa, and binge-eating disorder, as well as avoidant/restrictive food intake disorder and other specified/unspecified eating disorders. Key transdiagnostic features of eating disorders, including eating-related fear and avoidance, body image disturbance, and binge eating, are addressed through a brief case study.

Using Genetic Algorithms to Set Target Values for Engineering Characteristics in the House of Quality

2002 ◽  
Vol 2 (2) ◽  
pp. 106-114 ◽  
Author(s):  
Patricia Brackin ◽  
Jonathan S. Colton
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Penalty Functions ◽  
Preliminary Design ◽  
Test Results ◽  
House Of Quality ◽  
Design Specifications ◽  
Engineering Characteristics ◽  
Target Values

As part of a strategy for obtaining preliminary design specifications from the House of Quality, genetic algorithms are used to generate and optimize preliminary design specifications for an automotive case study. This paper describes the House of Quality for an automotive case study. In addition, the genetic algorithm chosen, the genetic coding, the methods used for mutation and reproduction, and the fitness and penalty functions are described. Methods for determining convergence are examined. Finally, test results show that the genetic algorithm produces reasonable preliminary design specifications.

scholarly journals Literatura De Controversia Religiosa Judío-Cristiana Medieval En Portugal (Siglos Xiii-Xv): Estado De La Cuestión. Discursos Y Motivaciones

Perichoresis ◽  
2020 ◽  
Vol 18 (4) ◽  
pp. 63-79
Author(s):  
Alice Tavares
Keyword(s):  
Middle Ages ◽  
Literary Genre ◽  
Common Features ◽  
The Common ◽  
Definition Of ◽  
The Middle Ages

AbstractThe case study presented in this article is an analysis of Portuguese literary manuscripts which deal with religious Jewish controversies during the Middle Ages (13th to the 15th centuries). These documents came down to us through the subsequent centuries and are available in the Libraries of Portugal. The article is intended to make known variegated documents of religious controversies over three centuries, while, at the same time, we shall draft a brief presentation of their authors, except for anonymous works. In the second part, we shall proceed with a definition of this particlar literary genre as well as analyze the characteristics and also the common features of the different works. In the end, we shall investigaate the discourses, motivations, and the different authors who have influenced the writing of these medieval controversies.

scholarly journals OPTIMIZATION OF ARCHITECTURAL LAYOUT BY THE IMPROVED GENETIC ALGORITHM

2005 ◽  
Vol 11 (1) ◽  
pp. 13-21 ◽  
Author(s):  
Romualdas Baušys ◽  
Ina Pankrašovaite
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Rate Of Convergence ◽  
Conceptual Design ◽  
Search Method ◽  
Evolutionary Search ◽  
Improved Genetic Algorithm ◽  
Layout Problem ◽  
Current Stage

In this paper we consider architectural layout problem that seeks to determine the layout of Units based on lighting, heating, available sizes and other objectives and constraints. For a conceptual design of architectural layout we present an approach based on evolutionary search method known as the genetic algorithms (GAs). However, the rate of convergence of GAs is often not good enough at their current stage. For this reason, the improved genetic algorithm is proposed. We have analysed and compared the performance of standard and improved genetic algorithm for architectural layout problem solutions and presented the results of performance.

scholarly journals A Hybrid Approach Combining Fuzzy c-Means-Based Genetic Algorithm and Machine Learning for Predicting Job Cycle Times for Semiconductor Manufacturing

2021 ◽  
Vol 11 (16) ◽  
pp. 7428
Author(s):  
Gyu M. Lee ◽  
Xuehong Gao
Keyword(s):  
Genetic Algorithm ◽  
Cycle Time ◽  
Hybrid Approach ◽  
Cycle Times ◽  
Fuzzy C Means ◽  
Backpropagation Network ◽  
Semiconductor Fabrication ◽  
Time Required ◽  
And Training

Job cycle time is the cycle time of a job or the time required to complete a job. Prediction of job cycle time is a critical task for a semiconductor fabrication factory. A predictive model must forecast job cycle time to pursue sustainable development, meet customer requirements, and promote downstream operations. To effectively predict job cycle time in semiconductor fabrication factories, we propose an effective hybrid approach combining the fuzzy c-means (FCM)-based genetic algorithm (GA) and a backpropagation network (BPN) to predict job cycle time. All job records are divided into two datasets: the first dataset is for clustering and training, and the other is for testing. An FCM-based GA classification method is developed to pre-classify the first dataset of job records into several clusters. The classification results are then fed into a BPN predictor. The BPN predictor can predict the cycle time and compare it with the second dataset. Finally, we present a case study using the actual dataset obtained from a semiconductor fabrication factory to demonstrate the effectiveness and efficiency of the proposed approach.

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