Learning single-machine scheduling heuristics subject to machine breakdowns with genetic programming Export

@inproceedings{citeulike:1877894, abstract = {Genetic programming (GP) has been rarely applied to scheduling problems. In this paper the use of GP to learn single-machine predictive scheduling (PS) heuristics with stochastic breakdowns is investigated, where both tardiness and stability objectives in face of machine failures are considered. The proposed bi-tree structured representation scheme makes it possible to search sequencing and idle time inserting programs integratedly. Empirical results in different uncertain environments show that GP can evolve high quality PS heuristics effectively. The roles of inserted idle time are then analysed with respect to various weighting objectives. Finally some guides are supplied for PS design based on GP-evolved heuristics.}, author = {Yin, Wen-Jun and Liu, Min and Wu, Cheng}, booktitle = {Evolutionary Computation, 2003. CEC '03. The 2003 Congress on}, citeulike-article-id = {1877894}, citeulike-linkout-0 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1299784}, journal = {Evolutionary Computation, 2003. CEC '03. The 2003 Congress on}, keywords = {breakdown, genetic-programming, jsp, robustness, single-machine}, pages = {1050--1055 Vol.2}, posted-at = {2007-11-07 15:48:50}, priority = {0}, title = {Learning single-machine scheduling heuristics subject to machine breakdowns with genetic programming}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1299784}, volume = {2}, year = {2003} }

TY - CONF ID - citeulike:1877894 L3 - citeulike-article-id:1877894 N2 - Genetic programming (GP) has been rarely applied to scheduling problems. In this paper the use of GP to learn single-machine predictive scheduling (PS) heuristics with stochastic breakdowns is investigated, where both tardiness and stability objectives in face of machine failures are considered. The proposed bi-tree structured representation scheme makes it possible to search sequencing and idle time inserting programs integratedly. Empirical results in different uncertain environments show that GP can evolve high quality PS heuristics effectively. The roles of inserted idle time are then analysed with respect to various weighting objectives. Finally some guides are supplied for PS design based on GP-evolved heuristics. JF - Evolutionary Computation, 2003. CEC '03. The 2003 Congress on EP - 1055 Vol.2 TI - Learning single-machine scheduling heuristics subject to machine breakdowns with genetic programming VL - 2 T2 - Evolutionary Computation, 2003. CEC '03. The 2003 Congress on SP - 1050 KW - breakdown KW - genetic-programming KW - jsp KW - robustness KW - single-machine AU - Yin, Wen-Jun AU - Liu, Min AU - Wu, Cheng PY - 2003/// UR - http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1299784 ER -