The single machine early/tardy problem
Management Science
The ant colony optimization meta-heuristic
New ideas in optimization
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
ACO Applied to Group Shop Scheduling: A Case Study on Intensification and Diversification
ANTS '02 Proceedings of the Third International Workshop on Ant Algorithms
Ant Colony Optimization
Ant colony optimization for resource-constrained project scheduling
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Environmentally constrained economic dispatch using Pareto archive particle swarm optimisation
International Journal of Systems Science
A meta-heuristic approach to solve a JIT scheduling problem in hybrid flow shop
Engineering Applications of Artificial Intelligence
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A new approach for solving permutation scheduling problems with ant colony optimization (ACO) is proposed in this paper. The approach assumes that no precedence constraints between the jobs have to be fulfilled. It is tested with an ACO algorithm for the single-machine total weighted deviation problem. In the new approach the ants allocate the places in the schedule not sequentially, as in the standard approach, but in random order. This leads to a better utilization of the pheromone information. It is shown by experiments that adequate combinations between the standard approach which can profit from list scheduling heuristics and the new approach perform particularly well.