%0 Journal Article %1 Lee:2004:Omega %A Lee, Sang M. %A Asllani, Arben A. %D 2004 %J Omega %K 0-1 Dual Sequence algorithms, criteria dependent genetic mathematical programming programming, scheduling, setup times, %N 2 %P 145--153 %R doi:10.1016/j.omega.2003.10.001 %T Job scheduling with dual criteria and sequence-dependent setups: mathematical versus genetic programming %U http://www.sciencedirect.com/science/article/B6VC4-4B42761-1/2/94e937150cd10b51245fedaa40f1d3cc %V 32 %X Flexibility, speed, and efficiency are major challenges for operations managers in today's knowledge-intensive organisations. Such requirements are converted into three production scheduling criteria: (a) minimise the impact of setup times in flexible production lines when moving from one product to another, (b) minimize number of tardy jobs, and (c) minimize overall production time, or makespan, for a given set of products or services. There is a wide range of solution methodologies for such NP-hard scheduling problems. While mathematical programming models provide optimal solutions, they become too complex to model for large scheduling problems. Simultaneously, heuristic approaches are simpler and very often independent of the problem size, but provide "good" rather than optimal solutions. This paper proposes and compares two alternative solutions: 0-1 mixed integer linear programming and genetic programming. It also provides guidelines that can be used by practitioners in the process of selecting the appropriate scheduling methodology.

@article{Lee:2004:Omega, abstract = {Flexibility, speed, and efficiency are major challenges for operations managers in today's knowledge-intensive organisations. Such requirements are converted into three production scheduling criteria: (a) minimise the impact of setup times in flexible production lines when moving from one product to another, (b) minimize number of tardy jobs, and (c) minimize overall production time, or makespan, for a given set of products or services. There is a wide range of solution methodologies for such NP-hard scheduling problems. While mathematical programming models provide optimal solutions, they become too complex to model for large scheduling problems. Simultaneously, heuristic approaches are simpler and very often independent of the problem size, but provide {"}good{"} rather than optimal solutions. This paper proposes and compares two alternative solutions: 0-1 mixed integer linear programming and genetic programming. It also provides guidelines that can be used by practitioners in the process of selecting the appropriate scheduling methodology.}, added-at = {2008-06-19T17:35:00.000+0200}, author = {Lee, Sang M. and Asllani, Arben A.}, biburl = {https://www.bibsonomy.org/bibtex/2fa35c319534f2946c7b1b06eea551c0f/brazovayeye}, doi = {doi:10.1016/j.omega.2003.10.001}, interhash = {39d020b2017ab6d40adb6551b851f117}, intrahash = {fa35c319534f2946c7b1b06eea551c0f}, journal = {Omega}, keywords = {0-1 Dual Sequence algorithms, criteria dependent genetic mathematical programming programming, scheduling, setup times,}, month = {April 2004}, number = 2, owner = {wlangdon}, pages = {145--153}, timestamp = {2008-06-19T17:45:15.000+0200}, title = {Job scheduling with dual criteria and sequence-dependent setups: mathematical versus genetic programming}, url = {http://www.sciencedirect.com/science/article/B6VC4-4B42761-1/2/94e937150cd10b51245fedaa40f1d3cc}, volume = 32, year = 2004 }