Title :
New Hybrid Optimization Algorithms for Machine Scheduling Problems
Author :
Pan, Yunpeng ; Shi, Leyuan
Author_Institution :
CombineNet, Inc., Pittsburgh
fDate :
4/1/2008 12:00:00 AM
Abstract :
Dynamic programming, branch-and-bound, and constraint programming are the standard solution principles for finding optimal solutions to machine scheduling problems. We propose a new hybrid optimization framework that integrates all three methodologies. The hybrid framework leads to powerful solution procedures. We demonstrate our approach through the optimal solution of the single-machine total weighted completion time scheduling problem subject to release dates, which is known to be strongly NP-hard. Extensive computational experiments indicate that new hybrid algorithms use orders of magnitude less storage than dynamic programming, and yet can still reap the full benefit of the dynamic programming property inherent to the problem. We are able to solve to optimality all 1900 instances with up to 200 jobs. This more than doubles the size of problems that can be solved optimally by the previous best algorithm running on the latest computing hardware.
Keywords :
constraint handling; dynamic programming; single machine scheduling; tree searching; NP-hard; branch-and-bound; constraint programming; dynamic programming; hybrid optimization algorithms; machine scheduling problems; production scheduling; single-machine; time total weighted completion scheduling problem; Branch-and-bound; constraint programming; dynamic programming; hybrid algorithms;
Journal_Title :
Automation Science and Engineering, IEEE Transactions on
DOI :
10.1109/TASE.2007.895005