Job shop scheduling by simulated annealing
Operations Research
Routing and scheduling in a flexible job shop by tabu search
Annals of Operations Research - Special issue on Tabu search
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Combining simplex with niche-based evolutionary computation for job-shop scheduling
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Journal of Systems and Software
Computers and Industrial Engineering
Using evolutionary computation and local search to solve multi-objective flexible job shop problems
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A hybrid watermarking technique applied to digital images
Applied Soft Computing
PSO-based algorithm for home care worker scheduling in the UK
Computers and Industrial Engineering
Choquet integral for criteria aggregation in the flexible job-shop scheduling problems
Mathematics and Computers in Simulation
Artificial Intelligence in Medicine
Computers and Operations Research
A new particle swarm optimization for the open shop scheduling problem
Computers and Operations Research
A particle swarm optimization algorithm for the multiple-level warehouse layout design problem
Computers and Industrial Engineering
A Pareto archive particle swarm optimization for multi-objective job shop scheduling
Computers and Industrial Engineering
Multi-objective flexible job shop schedule: Design and evaluation by simulation modeling
Applied Soft Computing
Computers and Industrial Engineering
Research on quality performance conceptual design based on SPEA2+
Computers & Mathematics with Applications
Computers and Industrial Engineering
Adaptive representation for flexible job-shop scheduling and rescheduling
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Flexible job-shop scheduling with parallel variable neighborhood search algorithm
Expert Systems with Applications: An International Journal
A particle swarm optimization algorithm for flexible jobshop scheduling problem
CASE'09 Proceedings of the fifth annual IEEE international conference on Automation science and engineering
A Knowledge-Based Ant Colony Optimization for Flexible Job Shop Scheduling Problems
Applied Soft Computing
Solving multiobjective flexible job-shop scheduling using an adaptive representation
Proceedings of the 12th annual conference on Genetic and evolutionary computation
An effective heuristic for flexible job-shop scheduling problem with maintenance activities
Computers and Industrial Engineering
Modular design of a hybrid genetic algorithm for a flexible job-shop scheduling problem
Knowledge-Based Systems
Scheduling data-intensive work-flow applications using particle swarm approaches
ICCOM'06 Proceedings of the 10th WSEAS international conference on Communications
Hi-index | 0.01 |
Scheduling for the flexible job-shop is very important in both fields of production management and combinatorial optimization. However, it is quite difficult to achieve an optimal solution to this problem with traditional optimization approaches owing to the high computational complexity. The combining of several optimization criteria induces additional complexity and new problems. Particle swarm optimization is an evolutionary computation technique mimicking the behavior of flying birds and their means of information exchange. It combines local search (by self experience) and global search (by neighboring experience), possessing high search efficiency. Simulated annealing (SA) as a local search algorithm employs certain probability to avoid becoming trapped in a local optimum and has been proved to be effective for a variety of situations, including scheduling and sequencing. By reasonably hybridizing these two methodologies, we develop an easily implemented hybrid approach for the multi-objective flexible job-shop scheduling problem (FJSP). The results obtained from the computational study have shown that the proposed algorithm is a viable and effective approach for the multi-objective FJSP, especially for problems on a large scale.