In multi-cloud environment, task scheduling has attracted a lot of attention due to NP-Complete nature of the problem. Moreover, it is very challenging due to heterogeneity of the cloud resources with varying capacities and functionalities. Therefore, minimizing the makespan for task scheduling is a challenging issue. In this paper, we propose a genetic algorithm (GA) based approach for solving...

Genetic algorithms (GAs) have been used widely for such combinatorial optimization problems as the traveling salesman problem (TSP), the quadratic assignment problem (QAP), and job shop scheduling. In all of these problems there is usually a well defined representation which GA's use to solve the problem. We present a novel approach for solving two related problems-lot sizing and sequencing-con...

This study focuses on solving a special kind of job shop scheduling problem (JSP), where the job value is exponentially deteriorating over time. The current study attempted to find out whether the expected benefits of Lot Streaming (LS) can be found in solving the JSP with the objective of maximizing the total value of the jobs. LS is a process of splitting jobs into smaller sub-jobs such that ...

Scheduling problems have been studied a lot over the last decade. Due to the complexity and the variety of such problems, most works consider static problems in which activities are known in advance and constraints are fixed. However, every scheduling problem is subject to unexpected events (consider for example a new activity to schedule, or a machine breakdown). In these cases, a new solution...

The intent of this extended abstract is to briefly describe the work developed by the PhD student Carina Pimentel during her PhD studies, since 2007 until now. The core subject of this work is the development of models and methods for solving medium/short term production planning problems. Two different problems are studied. The first one is a classical lot sizing problem. For this problem seve...