An Ant Algorithm with a New Pheromone Evaluation Rule for Total Tardiness Problems
Real-World Applications of Evolutionary Computing, EvoWorkshops 2000: EvoIASP, EvoSCONDI, EvoTel, EvoSTIM, EvoROB, and EvoFlight
Design of Iterated Local Search Algorithms
Proceedings of the EvoWorkshops on Applications of Evolutionary Computing
Ant Colony Optimization for the Total Weighted Tardiness Problem
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Bi-Criterion Optimization with Multi Colony Ant Algorithms
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
A hybrid heuristic for the maximum clique problem
Journal of Heuristics
The probabilistic heuristic in local (PHIL) search meta-strategy
IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
MA|PM: memetic algorithms with population management
Computers and Operations Research
Block approach: tabu search algorithm for single machine total weighted tardiness problem
Computers and Industrial Engineering
Hybrid heuristic algorithms for single machine total weighted tardiness scheduling problems
International Journal of Intelligent Systems Technologies and Applications
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
Expert Systems with Applications: An International Journal
Computers and Operations Research
Interval-indexed formulation based heuristics for single machine total weighted tardiness problem
Computers and Operations Research
An exact algorithm for single-machine scheduling without machine idle time
Journal of Scheduling
Block approach-tabu search algorithm for single machine total weighted tardiness problem
Computers and Industrial Engineering
MA|PM: memetic algorithms with population management
Computers and Operations Research
Solving multi-criteria optimization problems with population-based ACO
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Threshold accepting framework for discrete and continuous search spaces
International Journal of Innovative Computing and Applications
Iterated local search and very large neighborhoods for the parallel-machines total tardiness problem
Computers and Operations Research
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Multi-objective optimization with fuzzy measures and its application to flow-shop scheduling
Engineering Applications of Artificial Intelligence
Hi-index | 0.00 |
This paper presents several local search heuristics for the problem of scheduling a single machine to minimize total weighted tardiness. We introduce a new binary encoding scheme to represent solutions, together with a heuristic to decode the binary representations into actual sequences. This binary encoding scheme is compared to the usual "natural" permutation representation for descent, simulated annealing, threshold accepting, tabu search and genetic algorithms on a large set of test problems. Computational results indicate that all of the heuristics which employ our binary encoding are very robust in that they consistently produce good quality solutions, especially when multistart implementations are used instead of a single long run. The binary encoding is also used in a new genetic algorithm which performs very well and requires comparatively little computation time. A comparison of neighborhood search methods which use the permutation and binary representations shows that the permutation-based methods have a higher likelihood of generating an optimal solution, but are less robust in that some poor solutions are obtained. Of the neighborhood search methods, tabu search clearly dominates the others. Multistart descent performs remarkably well relative to simulated annealing and threshold accepting.