A hybrid genetic algorithm for the distributed permutation flowshop scheduling problem
نویسندگان
چکیده
منابع مشابه
Improved genetic algorithm for the permutation flowshop scheduling problem
Genetic algorithms (GAs) are search heuristics used to solve global optimization problems in complex search spaces. We wish to show that the e6ciency of GAs in solving a &owshop problem can be improved signi7cantly by tailoring the various GA operators to suit the structure of the problem. The &owshop problem is one of scheduling jobs in an assembly line with the objective of minimizing the com...
متن کاملThe distributed permutation flowshop scheduling problem
This paper studies a new generalization of the regular permutation flowshop scheduling problem (PFSP) referred to as the distributed permutation flowshop scheduling problem or DPFSP. Under this generalization, we assume that there are a total of identical factories or shops, each one with machines disposed in series. A set of n available jobs have to be distributed among the factories and then ...
متن کاملHybrid Taguchi-based Genetic Algorithm for Flowshop Scheduling Problem
A hybrid Taguchi-based genetic algorithm (HTGA) is developed for solving multi-objective flowshop scheduling problems (FSPs). Search performance is improved by using Taguchi-based crossover to avoid scheduling conflicts, and dynamic weights are randomly selected by a fuzzy inference system. The conventional approach to selecting dynamic weights randomly ignores small value for the objective whe...
متن کاملA Constructive Genetic Algorithm for permutation flowshop scheduling
The general flowshop scheduling problem is a production problem where a set of n jobs have to be processed with identical flow pattern on m machines. In permutation flowshops the sequence of jobs is the same on all machines. A significant research effort has been devoted for sequencing jobs in a flowshop minimizing the makespan. This paper describes the application of a Constructive Genetic Alg...
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ژورنال
عنوان ژورنال: International Journal of Computational Intelligence Systems
سال: 2011
ISSN: 1875-6891,1875-6883
DOI: 10.1080/18756891.2011.9727808