Integrating process planning and scheduling by an agent-based ant colony system

This paper presents the application of an ant colony optimization (ACO) algorithm in an agent-based system to integrate process planning and shopfloor scheduling in manufacturing. A graph structure is devised to represent sets of alternative processes and machines. Artificial ants have to construct schedules by node selections in the graph. Inspiring from the foraging behaviour of real ants which are able to find shorter paths for food, the process plan and the schedule are determined dynamically with an objective of minimizing makespan. The proposed ACO approach takes advantage of distributed computation in the multi-agent platform, each artificial ant is implemented as a software agent which runs separately and simultaneously. Simulation studies have been established to evaluate the performance of the ant approach. The experimental results show that the proposed ACO algorithm can effectively generate the optimum process routing and schedule.