A collaborative process planning and scheduling system
Advances in Engineering Software - Special issue: computer-aided process planning
Computers and Industrial Engineering
A symbiotic evolutionary algorithm for the integration of process planning and job shop scheduling
Computers and Operations Research
Evaluating the impact of alternative plans on manufacturing performance
Computers and Industrial Engineering
Representations for Genetic and Evolutionary Algorithms
Representations for Genetic and Evolutionary Algorithms
An overview of distributed process planning and its integration with scheduling
International Journal of Computer Applications in Technology
A simulated annealing-based optimization approach for integrated process planning and scheduling
International Journal of Computer Integrated Manufacturing
Applications of particle swarm optimisation in integrated process planning and scheduling
Robotics and Computer-Integrated Manufacturing
Integration of process planning and scheduling-A modified genetic algorithm-based approach
Computers and Operations Research
Computers and Operations Research
An agent-based approach for integrated process planning and scheduling
Expert Systems with Applications: An International Journal
Integrated process planning and scheduling by an agent-based ant colony optimization
Computers and Industrial Engineering
Hi-index | 12.05 |
In traditional approaches, process planning and scheduling are carried out sequentially, where scheduling is done separately after the process plan has been generated. However, the functions of these two systems are usually complementary. The traditional approach has become an obstacle to improve the productivity and responsiveness of the manufacturing system. If the two systems can be integrated more tightly, greater performance and higher productivity of a manufacturing system can be achieved. Therefore, the research on the integrated process planning and scheduling (IPPS) problem is necessary. In this paper, a new active learning genetic algorithm based method has been developed to facilitate the integration and optimization of these two systems. Experimental studies have been used to test the approach, and the comparisons have been made between this approach and some previous approaches to indicate the adaptability and superiority of the proposed approach. The experimental results show that the proposed approach is a promising and very effective method on the research of the IPPS problem.