Journal Article10.1007/S13369-014-0980-3
Knowledge-Based Genetic Algorithm for Dynamic Machine–Tool Selection and Operation Allocation
2
TL;DR: A knowledge-based genetic algorithm for solving a problem of dynamic machine–tool selection and operation allocation in a flexible manufacturing system and shows that the KBGA obtains better performance in objective function values and CPU times.
read more
Abstract: This paper describes a knowledge-based genetic algorithm (KBGA) for solving a problem of dynamic machine–tool selection and operation allocation in a flexible manufacturing system. In this problem, there are a set of tools and machines that can produce different parts. The proposed KBGA combines a simple genetic algorithm with tacit and explicit knowledge that makes knowledge base (KB) to search optimal solution. In the proposed algorithm, the knowledge is used in generating initial population, selecting individuals, and applying mutation and crossover operators. Finally, 21 examples of different sizes are used to verify the performance of the proposed KBGA and to compare it with the five simple procedures algorithm and the branch-and-bound method. The results show that the KBGA obtains better performance in objective function values and CPU times.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Fault Tolerant Task Scheduling on Computational Grid Using Checkpointing Under Transient Faults
Ritu Garg,Awadhesh Kumar Singh +1 more
TL;DR: The simulation results show that the proposed fault tolerant task scheduling algorithm achieves better performance and execution reliability than other previous algorithms in the presence of failures.
21
Generating Methods for Group Affective Preferences with Engineering Applications
TL;DR: This paper explicitly presents a stimulated and transferring affective computing model, along with the definition of group affective preferences and the interactive generating methods, which can gradually grasp group’s experience in group decision making and help reduce group's subjective fatigue and make decisions more quickly.
References
•Book
An introduction to the bootstrap
Bradley Efron,Robert Tibshirani +1 more
- 01 Jan 1993
TL;DR: This article presents bootstrap methods for estimation, using simple arguments, with Minitab macros for implementing these methods, as well as some examples of how these methods could be used for estimation purposes.
•Book
Adaptation in natural and artificial systems
John H. Holland
- 01 Jan 1975
TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.
A Dynamic Theory of Organizational Knowledge Creation
TL;DR: In this paper, the authors propose a paradigm for managing the dynamic aspects of organizational knowledge creating processes, arguing that organizational knowledge is created through a continuous dialogue between tacit and explicit knowledge.
An Introduction to the Bootstrap.
Bradley Efron,Robert Tibshirani +1 more
TL;DR: In this article, the authors present a geometric representation for the Bootstrap and the Jackknife, as well as an overview of nonparametric and Parametric Inference methods for estimating the error in Bootstrap estimates.
15.3K
The Knowledge-Creating Company
Ikujiro Nonaka,Hirotaka Takeuchi +1 more
- 18 May 1995
Abstract: Abstract How has Japan become a major economic power, a world leader in the automotive and electronics industries? What is the secret of their success? The consensus has been that, though the Japanese are not particularly innovative, they are exceptionally skilful at imitation, at improving products that already exist. But now two leading Japanese business experts, Ikujiro Nonaka and Hiro Takeuchi, turn this conventional wisdom on its head: Japanese firms are successful, they contend, precisely because they are innovative, because they create new knowledge and use it to produce successful products and technologies. Examining case studies drawn from such firms as Honda, Canon, Matsushita, NEC, 3M, GE, and the U.S. Marines, this book reveals how Japanese companies translate tacit to explicit knowledge and use it to produce new processes, products, and services.
14K