A symbiotic evolutionary algorithm for the integration of process planning and job shop scheduling
Computers and Operations Research
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
Agent-based distributed manufacturing process planning and scheduling: a state-of-the-art survey
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Hi-index | 12.05 |
Process planning and scheduling are two key sub-functions in the manufacturing system. Traditionally, process planning and scheduling were regarded as the separate tasks to perform sequentially. Recently, a significant trend is to integrate process planning and scheduling more tightly to achieve greater performance and higher productivity of the manufacturing system. Because of the complementarity of process planning and scheduling, and the multiple objectives requirement from the real-world production, this research focuses on the multi-objective integrated process planning and scheduling (IPPS) problem. In this research, the Nash equilibrium in game theory based approach has been used to deal with the multiple objectives. And a hybrid algorithm has been developed to optimize the IPPS problem. Experimental studies have been used to test the performance of the proposed approach. The results show that the developed approach is a promising and very effective method on the research of the multi-objective IPPS problem.