TY - GEN
T1 - Weighted sum coloring in batch scheduling of conflicting jobs
AU - Epstein, Leah
AU - Halldórsson, Magnús M.
AU - Levin, Asaf
AU - Shachnai, Hadas
PY - 2006
Y1 - 2006
N2 - Motivated by applications in batch scheduling of interval jobs, processes in manufacturing systems and distributed computing, we study two related problems. Given is a set of jobs {J1,. Jn}, where Jj has the processing time Pj, and an undirected intersection graph G = ({1, 2,., n}, E); there is an edge (i, j) φ E if the pair of jobs Ji and J j cannot be processed in the same batch. At any period of time, we can process a batch of jobs that forms an independent set in G. The batch completes its processing when the last job in the batch completes its execution. The goal is to minimize the sum of job completion times. Our two problems differ in the definition of completion time of a job within a given batch. In the first variant, a job completes its execution when its batch is completed, whereas in the second variant, a job completes execution when its own processing is completed. For the first variant, we show that an adaptation of the greedy set cover algorithm gives a 4-approximation for perfect graphs. For the second variant, we give new or improved approximations for a number of different classes of graphs. The algorithms are of widely different genres (LP, greedy, subgraph covering), yet they curiously share a common feature in their use of randomized geometric partitioning.
AB - Motivated by applications in batch scheduling of interval jobs, processes in manufacturing systems and distributed computing, we study two related problems. Given is a set of jobs {J1,. Jn}, where Jj has the processing time Pj, and an undirected intersection graph G = ({1, 2,., n}, E); there is an edge (i, j) φ E if the pair of jobs Ji and J j cannot be processed in the same batch. At any period of time, we can process a batch of jobs that forms an independent set in G. The batch completes its processing when the last job in the batch completes its execution. The goal is to minimize the sum of job completion times. Our two problems differ in the definition of completion time of a job within a given batch. In the first variant, a job completes its execution when its batch is completed, whereas in the second variant, a job completes execution when its own processing is completed. For the first variant, we show that an adaptation of the greedy set cover algorithm gives a 4-approximation for perfect graphs. For the second variant, we give new or improved approximations for a number of different classes of graphs. The algorithms are of widely different genres (LP, greedy, subgraph covering), yet they curiously share a common feature in their use of randomized geometric partitioning.
UR - http://www.scopus.com/inward/record.url?scp=33750041337&partnerID=8YFLogxK
U2 - 10.1007/11830924_13
DO - 10.1007/11830924_13
M3 - Conference contribution
AN - SCOPUS:33750041337
SN - 3540380442
SN - 9783540380443
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 116
EP - 127
BT - Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques - 9th International Workshop on Approximation Algorithms for Combinatorial Optimization Problems, APPROX 2006 a
PB - Springer Verlag
T2 - 9th International Workshop on Approximation Algorithms for Combinatorial Optimization Problems, APPROX 2006 and 10th International Workshop on Randomization and Computation, RANDOM 2006
Y2 - 28 August 2006 through 30 August 2006
ER -