The scheduling of computer and manufacturing systems has been the subject of extensive research for over forty years. In addition to computers and manufacturing, scheduling theory can be applied to many areas including agriculture, hospitals and transport. The main focus is on the efficient allocation of one or more resources to activities over time. Adopting manufacturing terminology, a job consists of one or more activities, and a machine is a resource that can perform at most one activity at a time. We concentrate on deterministic machine scheduling for which it is assumed that all data that define a problem instance are known with certainty.

A Review of Machine Scheduling: Complexity, Algorithms and Approximability

Scheduling multiprocessor tasks — An overview☆

Scheduling is concerned with allocating limited resources to tasks to optimize certain objective functions. Due to the popularity of the Total Quality Management concept, on-time delivery of jobs has become one of the crucial factors for customer satisfaction. Scheduling plays an important role in achieving this goal. Recent developments in scheduling theory have focused on extending the models to include more practical constraints. Furthermore, due to the complexity studies conducted during the last two decades, it is now widely understood that most practical problems are NP-hard. This is one of the reasons why local search methods have been studied so extensively during the last decade. In this paper, we review briefly some of the recent extensions of scheduling theory, the recent developments in local search techniques and the new developments of scheduling in practice. Particularly, we survey two recent extensions of theory: scheduling with a 1-job-on-r-machine pattern and machine scheduling with availability constraints. We also review several local search techniques, including simulated annealing, tabu search, genetic algorithms and constraint guided heuristic search. Finally, we study the robotic cell scheduling problem, the auto-mated guided vehicles scheduling problem, and the hoist scheduling problem.

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Current trends in deterministic scheduling

The hybrid flow-shop scheduling problem with multiprocessor tasks finds its applications in real-time machine-vision systems among others. Motivated by this application and the computational complexity of the problem, we propose a genetic algorithm in this paper. We first describe the implementation details, which include a new crossover operator. We then perform a preliminary test to set the best values of the control parameters, namely the population size, crossover rate and mutation rate. Next, given these values, we carry out an extensive computational experiment to evaluate the performance of four versions of the proposed genetic algorithm in terms of the percentage deviation of the solution from the lower bound value. The results of the experiments demonstrate that the genetic algorithm performs the best when the new crossover operator is used along with the insertion mutation. This genetic algorithm also outperforms the tabu search algorithm proposed in the literature for the same problem.

A Genetic Algorithm for Hybrid Flow-shop Scheduling with Multiprocessor Tasks

This paper adresses certain types of scheduling problems that arise when a parallel computation is to be executed on a multiprocessor. We define a model that allows for communication delays between precedence-related tasks, and propose a classification of various submodels. We also review complexity results and optimization and approximation algorithms that have been presented in the literature.

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Multiprocessor scheduling with communication delays

The problem of diagnostic test scheduling (DTS) is to assign to each edge e of a diagnostic graph G a time interval of length l(e) so that intervals corresponding to edges at any given vertex do not overlap and the overall finishing time is minimum. In this correspondence we show that the DTS problem is NP-complete. Then we present a longest, first sequential scheduling algorithm which runs in worst case time O(dm log n) and uses O(m) space to produce a solution of length less than four times optimal. Then we show that the general performance bound can be strengthened to 3 * OPT(G) for low-degree graphs and to 2 ·OPT(G) in some special cases of binomial diagnostic graphs.

Krawczyk

Papers

An Approximation Algorithm for Diagnostic Test Scheduling in Multicomputer Systems