Computers and Industrial Engineering
A design knowledge management system to support collaborative information product evolution
Decision Support Systems - Special issue on decision support in the new millennium
The hybrid heuristic genetic algorithm for job shop scheduling
Computers and Industrial Engineering
Working Knowledge: How Organizations Manage What They Know
Working Knowledge: How Organizations Manage What They Know
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
A very fast Tabu search algorithm for the permutation flow shop problem with makespan criterion
Computers and Operations Research
Proportionate flexible flow shop scheduling via a hybrid constructive genetic algorithm
Expert Systems with Applications: An International Journal
A genetic algorithm for the Flexible Job-shop Scheduling Problem
Computers and Operations Research
Applications of particle swarm optimisation in integrated process planning and scheduling
Robotics and Computer-Integrated Manufacturing
Computers and Industrial Engineering
GA-based learning bias selection mechanism for real-time scheduling systems
Expert Systems with Applications: An International Journal
Two-phase sub population genetic algorithm for parallel machine-scheduling problem
Expert Systems with Applications: An International Journal
Genetic algorithms for match-up rescheduling of the flexible manufacturing systems
Computers and Industrial Engineering
Computers and Industrial Engineering
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
In this paper a complex scheduling problem in flexible manufacturing system (FMS) has been addressed with a novel approach called knowledge based genetic algorithm (KBGA). The literature review indicates that meta-heuristics may be used for combinatorial decision-making problem in FMS and simple genetic algorithm (SGA) is one of the meta-heuristics that has attracted many researchers. This novel approach combines KB (which uses the power of tacit and implicit expert knowledge) and inherent quality of SGA for searching the optima simultaneously. In this novel approach, the knowledge has been used on four different stages of SGA: initialization, selection, crossover, and mutation. Two objective functions known as throughput and mean flow time, have been taken to measure the performance of the FMS. The usefulness of the algorithm has been measured on the basis of number of generations used for achieving better results than SGA. To show the efficacy of the proposed algorithm, a numerical example of scheduling data set has been tested. The KBGA was also tested on 10 different moderate size of data set to show its robustness for large sized problems involving flexibility (that offers multiple options) in FMS.