Routing and scheduling in a flexible job shop by tabu search
Annals of Operations Research - Special issue on Tabu search
LEARNABLE EVOLUTION MODEL: Evolutionary Processes Guided by Machine Learning
Machine Learning - Special issue on multistrategy learning
Variable neighborhood search for the degree-constrained minimum spanning tree problem
Discrete Applied Mathematics - Special issue: Third ALIO-EURO meeting on applied combinatorial optimization
Design and Analysis of Experiments
Design and Analysis of Experiments
A genetic algorithm for the Flexible Job-shop Scheduling Problem
Computers and Operations Research
Variable Neighborhood Genetic Algorithm for the Flexible Job Shop Scheduling Problems
ICIRA '08 Proceedings of the First International Conference on Intelligent Robotics and Applications: Part II
An effective hybrid optimization approach for multi-objective flexible job-shop scheduling problems
Computers and Industrial Engineering
A Knowledge-Based Ant Colony Optimization for Flexible Job Shop Scheduling Problems
Applied Soft Computing
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Learning with case-injected genetic algorithms
IEEE Transactions on Evolutionary Computation
Hi-index | 0.00 |
Flexible job shop scheduling problem (FJSP) is quite a difficult combinatorial model. Various metaheuristic algorithms are used to find a local or global optimum solution for this problem. Among these algorithms, variable neighborhood search (VNS) is a capable one and makes use of a systematic change of neighborhood structure for evading local optimum. The search process for finding a local or global optimum solution by VNS is totally random. This is one of the weaknesses of this algorithm. To remedy this weakness of VNS, this paper combines VNS algorithm with a knowledge module and proposes knowledge-based VNS (KBVNS). In KBVNS, the VNS part searches the solution space to find good solutions and knowledge module extracts the knowledge of good solution and feed it back to the algorithm. This would make the search process more efficient. Computational results of the paper on different size test problems prove the efficiency of our algorithm for FJS problem.