Dynamic batching policies for an on-demand video server
Multimedia Systems
A 2-Approximation Algorithm for the Undirected Feedback Vertex Set Problem
SIAM Journal on Discrete Mathematics
Scheduling a batch processing machine with incompatible job families
Computers and Industrial Engineering
A unified approach to approximating resource allocation and scheduling
Journal of the ACM (JACM)
Throughput maximization of real-time scheduling with batching
SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
Approximating the Throughput of Multiple Machines in Real-Time Scheduling
SIAM Journal on Computing
The Batch Loading and Scheduling Problem
Operations Research
Minimizing total weighted tardiness on a single batch process machine with incompatible job families
Computers and Operations Research
Improved approximation algorithms for broadcast scheduling
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
A Polynomial Time Approximation Scheme for the Multiple Knapsack Problem
SIAM Journal on Computing
Dependent rounding and its applications to approximation algorithms
Journal of the ACM (JACM)
Approximation Algorithms for the Job Interval Selection Problem and Related Scheduling Problems
Mathematics of Operations Research
Performance of batching schemes for multimedia-on-demand services
IEEE Transactions on Multimedia
How well can primal-dual and local-ratio algorithms perform?
ACM Transactions on Algorithms (TALG)
Brief announcement: online batch scheduling for flow objectives
Proceedings of the twenty-fifth annual ACM symposium on Parallelism in algorithms and architectures
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We consider the following scheduling with batching problem that has many applications, for example, in multimedia-on-demand and manufacturing of integrated circuits. The input to the problem consists of n jobs and k parallel machines. Each job is associated with a set of time intervals in which it can be scheduled (given either explicitly or nonexplicitly), a weight, and a family. Each family is associated with a processing time. Jobs that belong to the same family can be batched and executed together on the same machine. The processing time of each batch is the processing time of the family of jobs it contains. The goal is to find a nonpreemptive schedule with batching that maximizes the weight of the scheduled jobs. We give constant factor (4 or 4 + ϵ) approximation algorithms for two variants of the problem, depending on the precise representation of the input. When the batch size is unbounded and each job is associated with a time window in which it can be processed, these approximation ratios reduce to 2 and 2 + ϵ, respectively. We also give approximation algorithms for two special cases when all release times are the same.