Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing
Intelligent Optimisation Techniques: Genetic Algorithms, Tabu Search, Simulated Annealing and Neural Networks
CADCAM: Principles, Practice and Manufacturing Management
CADCAM: Principles, Practice and Manufacturing Management
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
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
A Genetic Algorithm for Integration of Process Planning and Scheduling Problem
ICIRA '08 Proceedings of the First International Conference on Intelligent Robotics and Applications: Part II
International Journal of Computer Integrated Manufacturing
Computers and Operations Research
An agent-based approach for integrated process planning and scheduling
Expert Systems with Applications: An International Journal
Computers and Operations Research
Expert Systems with Applications: An International Journal
An active learning genetic algorithm for integrated process planning and scheduling
Expert Systems with Applications: An International Journal
A new approach for integrating process planning with scheduling
International Journal of Computer Applications in Technology
A multi-agent system for dynamic integrated process planning and scheduling using heuristics
KES-AMSTA'12 Proceedings of the 6th KES international conference on Agent and Multi-Agent Systems: technologies and applications
Application of ant colony optimization algorithm in process planning optimization
Journal of Intelligent Manufacturing
Robotics and Computer-Integrated Manufacturing
SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
Computers and Industrial Engineering
A multi-agent system to support heuristic-based dynamic manufacturing rescheduling
Intelligent Decision Technologies
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A job shop needs to deal with a lot of make-to-order business, in which the orders are usually diverse in types but each one is small in volume. To increase the flexibility and responsiveness of the job shop in the more competitive market, process planning and scheduling modules have been actively developed and deployed. The functions of the two modules are usually complementary. It is ideal to integrate them more tightly to achieve the global optimization of product development and manufacturing. In this paper, a unified representation model and a simulated annealing-based approach have been developed to facilitate the integration and optimization process. In the approach, three strategies, including processing flexibility, operation sequencing flexibility and scheduling flexibility, have been used for exploring the search space to support the optimization process effectively. Performance criteria, such as makespan, the balanced level of machine utilization, job tardiness and manufacturing cost, have been systematically defined to make the algorithm adaptive to meet various practical requirements. Case studies under various working conditions and the comparisons of this approach with two modern evolutionary approaches are given. The merits and characteristics of the approach are thereby highlighted.