Name
Abstract | ||
|---|---|---|
In this paper, crucial processes in a computer-aided process planning system, such as selecting machining resources, determining set-up plans and sequencing operations of a part, have been considered simultaneously and modelled as a constraint-based optimization problem, and a Tabu search-based approach has been proposed to solve it effectively. In the optimization model, costs of the utilized machines and cutting tools, machine changes, tool changes, set-ups and departure of good manufacturing practices (penalty function) are integrated as an optimization evaluation criterion. A case study, which is used to compare this approach with the genetic algorithm and simulated annealing approaches, is discussed to highlight the advantages of this approach in terms of solution quality, computation efficiency and the robustness of the algorithm. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1007/978-3-540-45226-3_137 | LECTURE NOTES IN ARTIFICIAL INTELLIGENCE |
Keywords | Field | DocType |
penalty function,simulated annealing,optimization problem,tabu search,genetic algorithm | Simulated annealing,Hill climbing,Mathematical optimization,Computer science,Meta-optimization,Robustness (computer science),Tabu search,Genetic algorithm,Metaheuristic,Penalty method | Conference |
Volume | ISSN | Citations |
2774 | 0302-9743 | 1 |
PageRank | References | Authors |
0.35 | 2 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| W. D. Li | 1 | 374 | 34.17 |
| Soh-khim Ong | 2 | 82 | 8.63 |
| Y. Q. Lu | 3 | 5 | 0.77 |
| Andrew Y. C. Nee | 4 | 151 | 14.43 |