Informatica

This paper deals with the two-stage transportation problem with fixed charges, denoted by TSTPFC. We propose a fast solving method, designed for parallel environments, that allows solving real-world applications efficiently. The proposed constructive heuristic algorithm is iterative and its primary feature is that the solution search domain is reduced at each iteration. Our achieved computational results were compared with those of the existing solution approaches. We tested the method on two sets of instances available in literature. The outputs prove that we have identified a very competitive approach as compared to the methods than one can find in literature.

The present paper describes the development and the performance of parallel FEM software for solving various CFD problems. Domain decomposition strategy and parallel iterative GMRES solver have been adapted to the universal space‐time FEM code FEMTOOL, which allows implementation of any partial differential equation with minor expenses. The developed data structures, the static load balancing and the inter‐processor communication algorithms have been particularly suited for homogeneous distributed memory PC clusters. The universality of the considered parallel algorithms has been validated solving applications described by the Poisson equation, by the convective transport equation and by the Navier–Stokes equations. Three typical benchmark problems have been solved in order to perform the efficiency study. The performance of parallel computations, the speed‐up and the efficiency have been measured on three BEOWULF PC clusters as well as on the cluster of IBM RISC workstations and on the IBM SP2 supercomputer.

The analysis of the method for multiple criteria optimization problems applying a computer network has been presented in the paper. The essence of the proposed method is the distribution of the concrete optimization problem into the network rather than the parallelization of some optimization method. The aim of the authors is to design and investigate the interactive strategies to solve complex multiple criteria problems by applying a computer network. The optimized objective function is the weight sum of the criteria. The multiple criteria problem is iterated by selecting interactively different weight coefficients of the criteria. Therefore, the process is organized by designating the computers as the master (that coordinates the process of other computers) and the slaves (that execute different tasks). In the beginning of the process the researcher allocates a certain number of optimization problems to the network. The objective function optimization problems differ only in weight coefficients of the criteria. As soon as the task of a slave has been executed, the result is sent to the master. Every computer of the network behaves in analogous way. Whenever the researcher receives an immediate result from one of the computers, he gives a decision taking into consideration the latter and all the previous results, i.e., he selects new weight coefficients for the criteria and assigns a new task to the network. Likewise the multiple criteria problem is solved until the result is acceptable for the researcher. The application of the proposed method is illustrated on the basis of the problem for the selection of the optimal nutritive value. Message Passing Interface (MPI) software has been used. The trials have been carried out with the network of computers under the operation system Windows NT.

We consider a possibility of automating the analysis of a computer program realizing the objective function of an extremal problem, and of distributing the calculation of the function value into parallel processes on the basis of results of the analysis. The first problem is to recognize the constituent parts of the function. The next one is to determine their computing times. The third problem is to distribute the calculation of these parts among independent processes. A special language similar to PASCAL has been used to describe the objective function. A new scheduling algorithm, seeking to minimize the maximal finishing time of processing units, was proposed and investigated. Experiments are performed using a computer network.