We solve the problem of petroleum products distribution through oil pipelines networks. This problem is modelled and solved using two techniques: A heuristic method like a multiobjective evolutionary algorithm and Mathematical Programming. In the multiobjective evolutionary algorithm, several objective functions are defined to express the goals of the solutions as well as the preferences among them. Some constraints are included as hard objective functions and some are evaluated through a repairing function to avoid infeasible solutions. In the Mathematical Programming approach the multiobjective optimization is solved using the Constraint Method in Mixed Integer Linear Programming. Some constraints of the mathematical model are nonlinear, so they are linearized. The results obtained with both methods for one concrete network are presented. They are compared with a hybrid solution, where we use the results obtained by Mathematical Programming as the seed of the evolutionary algorithm.

Parallel machine scheduling problems, which are important discrete optimization problems, may occur in many applications. For example, load balancing in network communication channel assignment, parallel processing in large-size computing, task arrangement in flexible manufacturing systems, etc., are multiprocessor scheduling problem. In the traditional parallel machine scheduling problems, it is assumed that the problems are considered in offline or online environment. But in practice, problems are often not really offline or online but somehow in-between. This means that, with respect to the online problem, some further information about the tasks is available, which allows the improvement of the performance of the best possible algorithms. Problems of this class are called semi-online ones. In this paper, the semi-online problem P2|decr|l_p (p>1) is considered where jobs come in non-increasing order of their processing times and the objective is to minimize the sum of the l_p norm of every machine’s load. It is shown that LS algorithm is optimal for any l_p norm, which extends the results known in the literature. Furthermore, randomized lower bounds for the problems P2|online|l_p and P2|decr|l_p are presented.

The C programming language is expressive and flexible, but not safe; as its expressive power and flexibility are obtained through unsafe language features, and improper use of these features can lead to program bugs whose causes are hard to identify. Since C is widely used, and it is impractical to rewrite all existing C programs in safe languages, so ways must be found to make C programs safe. This paper deals with the unsafe features of C and presents a survey on existing solutions to make C programs safe. We have studied binary-level instrumentation tools, source checkers, source-level instrumentation tools and safe dialects of C, and present a comparison of different solutions, summarized the strengths and weaknesses of different classes of solutions, and show measures that could possibly improve the accuracy or alleviate the overhead of existing solutions.