Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/20041
標題: 解決多目標流線型工廠排程問題之多軌跡搜尋演算法
A Multiple Trajectory Search for Multi-objective Flowshop Schedule Problem
作者: 陳伯鈞
Chen, Bo-Chun
關鍵字: 基因演算法
Genetic Algorithm
區域搜尋演算法
基因區域搜尋演算法
流線型工廠排程問題
Local Search Algorithm
Flowshop Schedule Problem
makespan
total flow time
出版社: 資訊科學與工程學系所
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Leisten, ” An efficient constructive heuristic for flowtime minimisation in permutation flow shops,” OMEGA, vol. 31, pp. 311-317, 2003. M. Garey, D. S. Johnson and R. Sethi, “The complexity of flowshop and jobshop scheduling,” Mathematical Methods of Operations Research, vol. 1, pp. 117–129, 1976. J. C. Ho, “Flowshop sequencing with mean flowtime objective,”European Journal of Operational Research, vol. 81(3), pp. 571-578, 1995. S.Y. Ho and J.H. Chen, “A GA-basedsystematic reasoning approach for solving traveling salesman problems using an orthogonal array crossover,” in Proceeding ofThe Fourth International IEEE Conference/Exhibition on High Performance Computing in Asia-Pacific Region, Beijing,China, pp. 659-663, 2000. H. Ishibuchi and T. Murata,“A multi-objective genetic local search algorithm and its application to flowshop scheduling,” IEEE Transactions on Systems, Man, and Cybernetics PartC, vol. 28, no. 3, pp. 392–403, 1998. E. Ignall and L.E. Schrage, “Application of branch and bound technique to some flow-shop problem,” Operations Research, vol. 13, pp. 400-412, 1964. S. M. Johnson, “Optimal two-and three-stage schedules with setup times included,” Naval Research Logistics Quarterly,vol.1 no. 1, pp.61-68, 1954. J. Knowles and D. Corne, “On metrics for comparing nondominated sets,” in Congress on Evolutionary Computation (CEC02), pp. 711–716, 2002. B. B. Li and L. Wang, “A hybrid quantum-inspired genetic algorithm for multi-objective flow shop scheduling,” IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, vol. 37, no. 3, pp. 576–591, 2007. G. Minella, R. Ruiz and M. Ciavotta, “A review and evaluation of multi-objective algorithms for the flowshop scheduling problem,”INFORMS Journal of Computing, vol. 20, pp. 451−471, 2008. M. Nawaz, E. Enscore and I. Ham, “A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem,” Omega, vol. 11, no. 1, pp. 91–95, 1983. A. C. Nearchou, “The effect of various operators on the genetic search for large scheduling problems,” International Journal of Production Economics, vol. 88, no. 2, pp. 191–203, 2004. F. A. Ogbu and D. K. Smith, “Simulated annealing for the permutation flowshop problem,” Omega, vol. 19, no. 1, pp. 64–67, 1990. D. S. Palmer, “Sequencing jobs through a multistage process in the minimum total time: A quick method of obtaining a near-optimum,”Operational Research Quarterly, vol. 16, pp. 101–107, 1965. S. Parthasarathy and C. Rajendran , “Scheduling to minimize mean tardiness and weighted mean tardiness in flowshop and flowline-based manufacturing cell,” Computers and Industrial Engineering, vol. 34(2), PP. 531–546, 1998 . C. R. Reeves, “A genetic algorithm for flowshop scheduling,” Computers & Operations Research, vol. 22, no. 1, pp. 5–13, 1995. C. Rajendran, “Heuristics for scheduling in flowshop with multiple objectives,” European Journal of Operational Research, vol. 83, pp. 540−555, 1995. 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摘要: 流線型工廠排程問題是NP-Hard裡的核心問題之一,在製造業生產線、軟體開發等領域有諸多應用。 本研究中使用混合式基因演算法來解決流線型工廠排程問題,針對目前許多演算法的缺點,我們提出了三點改善:1.加入了直交表(orthogonal array)交配(crossover)提高強化性(intensification);2.提出兩層架構,在各個區域有種子(seed)產生,比較不會錯過全域最佳解; 3.維持多樣性(diversification)。 我們應用本論文所提出的演算法於E.Taillard於1993所彙整的Taillard’s 標準測試題組(benchmark),並和其他演算法做一比較,並獲得不錯的成果。
The permutation flowshop scheduling problem is one of the core NP-hard problems with many applications filed such as, the industry production line and the software development. In this thesis, we propose a hybrid genetic local search algorithm to solve the multi-objective flowshop scheduling problem. The objectives are considered, namely, the makespan and the total flow time. Observing the weak points with some algorithms published in the literature, we developed our algorithm with two characteristics:1.Adding the orthogonal-array crossover operator to enhance the intensification. 2.We use two-layer genetic algorithm to search the solution space more thoroughly. 3.Maintaining diversification. We compare the proposed algorithm with other algorithms in the literature published on the benchmarks offered by Taillard’s 1993. The quality of solutions obtained by the proposed algorithm is competitive with other algorithms, whereas the computation time needed is more than that of other algorithms.
URI: http://hdl.handle.net/11455/20041
其他識別: U0005-0908201214030100
文章連結: http://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-0908201214030100
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