Variable neighborhood search (VNS) is a metaheuristic for solving combinatorial and global
optimization problems whose basic idea is a systematic change of neighborhood both within …

A new model for task scheduling that dramatically improves the efficiency of parallel systems
Task scheduling for parallel systems can become a quagmire of heuristics, models, and …

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for
operations management with the aim of finding solutions to problems that are increasing in …

To exploit a heterogeneous computing (HC) environment, an application task may be
decomposed into subtasks that have data dependencies. Subtask matching and scheduling …

We have developed a genetic algorithm (GA) approach to the problem of task scheduling for
multiprocessor systems. Our approach requires minimal problem specific information and no …

In the multiprocessor scheduling problem, a given program is to be scheduled in a given
multiprocessor system such that the program's execution time is minimized. This problem …

Proposed just a few years ago, Variable Neighborhood Search (VNS) is a new metaheuristic
for combinatorial and global optimization, based upon systematic change of neighborhood …

Given a parallel program represented by a task graph, the objective of a scheduling
algorithm is to minimize the overall execution time of the program by properly assigning the …

Effective task scheduling, which is essential for achieving high performance in a
heterogeneous multiprocessor system, remains a challenging problem despite extensive …

This paper presents a problem-space genetic algorithm (PSGA)-based technique for
efficient matching and scheduling of an application program that can be represented by a …