Deterministic and random single machine sequencing with variance minimization
Operations Research
On the minimization of completion time variance with a bicriteria extension
Operations Research
Computers and Operations Research
Pseudopolynomial algorithms for CTV minimization in single machine scheduling
Computers and Operations Research
Multi-machine scheduling with variance minimization
Discrete Applied Mathematics
Tabu search for a class of single-machine scheduling problems
Computers and Operations Research
QoS-Centric Stateful Resource Management in Information Systems
Information Systems Frontiers
A branch and bound algorithm to minimize completion time variance on a single processor
Computers and Operations Research
Jitter Control in QoS Networks
FOCS '98 Proceedings of the 39th Annual Symposium on Foundations of Computer Science
Job scheduling methods for reducing waiting time variance
Computers and Operations Research
Completion time variance minimization in single machine and multi-machine systems
Computers and Operations Research
Minimization of Job Waiting Time Variance on Identical Parallel Machines
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Proof of a conjecture of Schrage about the completion time variance problem
Operations Research Letters
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This paper addresses a job scheduling problem on multiple identical parallel machines so as to minimize job completion time variance (CTV). CTV minimization is closely related to the Just-In-Time philosophy and the service stability concept since it penalizes both earliness and tardiness. Its applications can be found in many real-life areas such as Internet data packet dispatching and production planning. This paper focuses on the unrestricted case of the problem where idle times are allowed to exist before machines start to process jobs. We prove several dominant properties about the optimal solution to the problem. For instance, we prove that the mean completion time (MCT) on each machine should be the same under an optimal schedule. Based on these properties, an efficient heuristic algorithm is proposed. Computational experiments are conducted to test the performance of the proposed algorithm. The outputs demonstrate that the proposed algorithm is near optimal for small problem instances and greatly outperforms some existing algorithms for large problem instances.