On the Integrated Job Scheduling and Constrained Network Routing Problem

This paper examines the NP-hard problem of scheduling a number of jobs on a finite set of machines such that the overall profit of executed jobs is maximized. Each job demands a number of resources, which must be sent to the executing machine via constrained paths. Furthermore, two resource demand transmissions cannot use the same edge in the same time period. An exact solution approach based on Dantzig-Wolfe decomposition is proposed along with several heuristics. The methods are computationally evaluated on test instances arising from telecommunications with up to 500 jobs and 500 machines. Results show that solving the problem to optimality is very difficult. The proposed heuristics have good performance with an average solution value gap of 3% and with very small running times

Greedy and metaheuristics for the offline scheduling problem in grid computing

In grid computing a number of geographically distributed resources connected through a wide area network, are utilized as one computations unit. The NP-hard offline scheduling problem in grid computing consists of assigning jobs to resources in advance. In this paper, five greedy heuristics and two metaheuristics for solving the offline scheduling problem are introduced. Computationally evaluating the heuristics shows that all heuristics find useful solutions with a gap of 20\% between upper and lower bounds. The metaheuristics give better results than the greedy heuristics, but also have larger time usage. All heuristics solve instances with up to 2000 jobs and 1000 resources, thus the results are useful both with respect to running times and to solution values