COMBINED APPROACH FOR SOLVING RESOURCE ALLOCATION PROBLEM
The paper is devoted to solving the computative resource allocation problem. In general, the resource allocation problem involves allocating the limited resource among processes in optimal way. Authors formulated the problem statement and its mathematical model. The resource allocation problem is referred as NP-complete, thus, the usage of traditional mathematical means is impossible or limited in terms of solving this problem. The developed approach is based on simulation modeling usage and Petri Nets tools to allocate resources. Since allocated resources are considered as computative, time spent for all processes execution and all Petri Net transition performance is taken as the main optimization criterion. Thus, authors apply temporal Petri Nets to model the resource allocation problem. The combined approach for solving computative resource allocation problem involves simultaneous application of Petri Net tools to model the problem, and genetic algorithms, which have proved their success in solving NP-complete problems. To optimize the Petri Net model authors use well-known genetic algorithms and modified genetic operators of crossover and mutation. Authors developed software representing the resource allocation subsystem. The paper describes main function of the subsystem and shows developed module structure of it. To represent main information processes flowing in subsystem, two information models on the basis of the IDEF0 standard are built and analyzed. Authors carried out computational experiments based on test benchmarks, which proved the effectiveness of the developed approach.