A broader view of the job-shop scheduling problem
Management Science
Neural network and genetic algorithm-based hybrid approach to expanded job-shop scheduling
Computers and Industrial Engineering
The hybrid heuristic genetic algorithm for job shop scheduling
Computers and Industrial Engineering
Computers and Industrial Engineering
A Pareto archive particle swarm optimization for multi-objective job shop scheduling
Computers and Industrial Engineering
Metaheuristics for minimizing the makespan of the dynamic shop scheduling problem
Advances in Engineering Software
Assignment and Scheduling in Flexible Job-Shops by Hierarchical Optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
An Effective PSO and AIS-Based Hybrid Intelligent Algorithm for Job-Shop Scheduling
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Inapproximability results for no-wait job shop scheduling
Operations Research Letters
Hi-index | 12.05 |
The job-shop scheduling problem (JSSP) is one of the most general and difficult of all traditional scheduling problems. Many different approaches have been applied to JSSP and a rich harvest has been obtained. However, some JSSP, even with moderate size, cannot be solved to guarantee optimality. In this paper, a computationally effective team process algorithm (TPA) for solving the minimum makespan problem of job-shop scheduling is used. In the TPA system, the team numbers are divided into the elite and plain groups. The manipulations of learning and exploring are properly defined, and the member renewal rules are reasonably established. It makes the algorithm possesses the potential of global, local and directional search. Numerical results verify that the modified TPA has the properties of simple implementation, high success rate for global optimization, fast convergence, and better than the modified particle swarm optimization (PSO) with GA and GA alone.