Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
The knowledge management toolkit: practical techniques for building a knowledge management system
The knowledge management toolkit: practical techniques for building a knowledge management system
A design knowledge management system to support collaborative information product evolution
Decision Support Systems - Special issue on decision support in the new millennium
Genetic Algorithms
Working Knowledge: How Organizations Manage What They Know
Working Knowledge: How Organizations Manage What They Know
A model and methodologies for the location problem with logistical components
Computers and Operations Research
Study on multi-stage logistic chain network: a spanning tree-based genetic algorithm approach
Computers and Industrial Engineering - Supply chain management
Emergent Supply Networks: System Dynamics Simulation of Adaptive Supply Agents
HICSS '01 Proceedings of the 34th Annual Hawaii International Conference on System Sciences ( HICSS-34)-Volume 3 - Volume 3
Network Models and Optimization: Multiobjective Genetic Algorithm Approach
Network Models and Optimization: Multiobjective Genetic Algorithm Approach
A steady-state genetic algorithm for multi-product supply chain network design
Computers and Industrial Engineering
Network model and optimization of reverse logistics by hybrid genetic algorithm
Computers and Industrial Engineering
A genetic algorithm approach for multi-objective optimization of supply chain networks
Computers and Industrial Engineering
Computers and Industrial Engineering
Expert Systems with Applications: An International Journal
Adaptive knowledge-based system for health care applications with RFID-generated information
Decision Support Systems
Advances in Engineering Software
IEEE Transactions on Evolutionary Computation
Hi-index | 0.00 |
In this paper, we present a Knowledge Based Genetic Algorithm (KBGA) for the network optimization of Supply Chain (SC). The proposed algorithm integrates the knowledge base for generating the initial population, selecting the individuals for reproduction and reproducing new individuals. From the literature, it has been seen that simple genetic-algorithm-based heuristics for this problem lead to and large number of generations. This paper extends the simple genetic algorithm (SGA) and proposes a new methodology to handle a complex variety of variables in a typical SC problem. To achieve this aim, three new genetic operators-knowledge based: initialization, selection, crossover, and mutation are introduced. The methodology developed here helps to improve the performance of classical GA by obtaining the results in fewer generations. To show the efficacy of the algorithm, KBGA also tested on the numerical example which is taken from the literature. It has also been tested on more complex problems.