Applying Genetic Algorithms for Production Scheduling and Resource Allocation. Special Case: A Small Size Manufacturing Company

2005 ◽  
pp. 547-550 ◽  
Author(s):  
A. Ricardo Contreras ◽  
C. Virginia Valero ◽  
J. M. Angélica Pinninghoff
Keyword(s):  
Resource Allocation ◽  
Genetic Algorithms ◽  
Production Scheduling ◽  
Manufacturing Company ◽  
Special Case

scholarly journals Applying Quantum Optimization Algorithms for Linear Programming

2017 ◽  
Author(s):  
Mert Side ◽  
Volkan Erol
Keyword(s):  
Resource Allocation ◽  
Linear Programming ◽  
Production Scheduling ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Quantum Algorithms ◽  
Quantum Algorithm ◽  
Quantum Computers ◽  
Quantum Optimization ◽  
Special Case

Quantum computers are machines that are designed to use quantum mechanics in order to improve upon classical computers by running quantum algorithms. One of the main applications of quantum computing is solving optimization problems. For addressing optimization problems we can use linear programming. Linear programming is a method to obtain the best possible outcome in a special case of mathematical programming. Application areas of this problem consist of resource allocation, production scheduling, parameter estimation, etc. In our study, we looked at the duality of resource allocation problems. First, we chose a real world optimization problem and looked at its solution with linear programming. Then, we restudied this problem with a quantum algorithm in order to understand whether if there is a speedup of the solution. The improvement in computation is analysed and some interesting results are reported.

scholarly journals Applying Quantum Optimization Algorithms for Linear Programming

2017 ◽  
Author(s):  
Mert Side ◽  
Volkan Erol
Keyword(s):  
Resource Allocation ◽  
Linear Programming ◽  
Production Scheduling ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Quantum Algorithms ◽  
Quantum Algorithm ◽  
Quantum Computers ◽  
Quantum Optimization ◽  
Special Case

Quantum computers are machines that are designed to use quantum mechanics in order to improve upon classical computers by running quantum algorithms. One of the main applications of quantum computing is solving optimization problems. For addressing optimization problems we can use linear programming. Linear programming is a method to obtain the best possible outcome in a special case of mathematical programming. Application areas of this problem consist of resource allocation, production scheduling, parameter estimation, etc. In our study, we looked at the duality of resource allocation problems. First, we chose a real world optimization problem and looked at its solution with linear programming. Then, we restudied this problem with a quantum algorithm in order to understand whether if there is a speedup of the solution. The improvement in computation is analysed and some interesting results are reported.

scholarly journals Applying Quantum Optimization Algorithms for Linear Programming

2017 ◽  
Author(s):  
Mert Side ◽  
Volkan Erol
Keyword(s):  
Resource Allocation ◽  
Linear Programming ◽  
Production Scheduling ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Quantum Algorithms ◽  
Quantum Algorithm ◽  
Quantum Computers ◽  
Quantum Optimization ◽  
Special Case

Quantum computers are machines that are designed to use quantum mechanics in order to improve upon classical computers by running quantum algorithms. One of the main applications of quantum computing is solving optimization problems. For addressing optimization problems we can use linear programming. Linear programming is a method to obtain the best possible outcome in a special case of mathematical programming. Application areas of this problem consist of resource allocation, production scheduling, parameter estimation, etc. In our study, we looked at the duality of resource allocation problems. First, we chose a real world optimization problem and looked at its solution with linear programming. Then, we restudied this problem with a quantum algorithm in order to understand whether if there is a speedup of the solution. The improvement in computation is analysed and some interesting results are reported.

scholarly journals A Recommender for Resource Allocation in Compute Clouds Using Genetic Algorithms and SVR

2019 ◽  
Author(s):  
Thiago Nelson Faria Dos Reis ◽  
Mário Antonio Meireles Teixeira ◽  
João Dallyson Sousa De Almeida ◽  
Anselmo Cardoso De Paiva
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Genetic Algorithms ◽  
Support Vector Regression ◽  
Support Vector

Alocação de recursos em Cloud Computing tem sido feito de forma reativa, dificultando garantias de serviço e gerando carga desnecessária de recursos ociosos. Para mitigar esses problemas, este trabalho propõe e avalia uma abordagem de alocação de recursos preditiva, implementado como um recomendador de configuração, com base em Support Vector Regression (SVR) e Algoritmos Genéticos (AG). Esta combinação é utilizada para estimar tempo de execução do aplicativo e recomenda uma configuração viável e válida de recursos na nuvem, sobre o tempo de execução e custos monetários. Como estudo de caso, as aplicações de aprendizagem de máquina com base na ferramenta Weka são escolhidos. Os resultados mostram que os tempos previstos foram muito perto dos reais, conseguindo uma estimativa eficiente de tempo e custo e sua consequente redução.

scholarly journals A meta-heuristic algorithm for integrated optimization of dynamic resource allocation planning and production scheduling in parallel machine system

2019 ◽  
Vol 11 (12) ◽  
pp. 168781401989834
Author(s):  
Na Wang ◽  
Yaping Fu ◽  
Hongfeng Wang
Keyword(s):  
Resource Allocation ◽  
Heuristic Algorithm ◽  
Production Scheduling ◽  
Optimization Problem ◽  
Manufacturing System ◽  
Parallel Machine ◽  
Dynamic Resource Allocation ◽  
Integration Problem ◽  
Advanced Information Technology ◽  
Dynamic Resource

With the wide application of advanced information technology and intelligent equipment in the manufacturing system, the decisions of design and operation have become more interdependent and their integration optimization has gained great concerns from the community of operational research recently. This article investigates an optimization problem of integrating dynamic resource allocation and production schedule in a parallel machine environment. A meta-heuristic algorithm, in which heuristic-based partition, genetic-based sampling, promising index calculation, and backtracking strategies are employed, is proposed for solving the investigated integration problem in order to minimize the makespan of the manufacturing system. The experimental results on a set of random-generated test instances indicate that the presented model is effective and the proposed algorithm exhibits the satisfactory performance that outperforms two state-of-the-art algorithms from literature.

Application of Genetic Algorithms for a Tyre Production Scheduling Information System

2009 ◽  
Vol 4 (9) ◽  
Author(s):  
Lin Liu ◽  
Xinbao Liu ◽  
Hao Cheng ◽  
Ying Guo ◽  
Shanlin Yang
Keyword(s):  
Information System ◽  
Genetic Algorithms ◽  
Production Scheduling
Sign in / Sign up

Export Citation Format

Share Document