Ant colony optimization system for a multi-quantitative and qualitative objective job-shop parallel-machine-scheduling problem

Abstract

This paper addresses a multi-stage job-shop parallel-machine-scheduling problem with an ant colony optimization system developed. The problem is practically important and yet more complex, especially when customer order splitting in multiple lots for the reduction of operation times in each workstation is allowed. It also includes the decisions of the numbers of parallel machines in workstations dynamically scheduled. In addition, this paper also addresses the multiple-objectives scheduling. For the practical concern, in addition to the production (or quantitative) objectives, the marketing (strategic or qualitative) criteria are also considered. A soft constraint thus may be realized from a thus-called qualitatively evaluated order sequence. The soft constraint with the ant colony optimization solution constructs a penalty function for the multiple qualitative objectives and the results of scheduling obtained by ant colony optimization. For this problem, the ant colony optimization components (including the network representation, tabu lists, transition probabilities, and pheromone trail updating) are also developed and adapted for the multiple objectives. The experiment results of parameter design and different problem sizes are provided. The results of a genetic algorithm also developed for the present problem under the developed system concept are also provided, since in the literature the genetic algorithm has also not been explored for the present problem with multiple objectives and order splitting. The results of both solution techniques show the potential usefulness of the system and are comparable, but the ant colony optimization provides a more computationally efficient better result.

Keywords:

• Parallel machines
• Ant colony optimization
• Multiple objectives
• Job-shop
• Order splitting
• Genetic algorithms

Acknowledgements

This research was partially supported by the National Science Council of Taiwan, ROC, under Grant No. NSC 92-2213-E-029-007.