Материалы пятой международной конференции «Актуальные проблемы системной и программной инженерии», сборник научных трудов
The paper is devoted to describing of the methods applied to create models of educational processes. These models was prepared to be implemented in a secure distributed system. The methods for model search, implementation and optimization were inspected in relation to the quality of education and distance learning. In order to improve metadata, the feedback process modeling was introduced.
The Metric Travelling Salesman Problem is a subcase of the Travelling Salesman Problem (TSP), where the triangle inequality holds. It is a key problem in combinatorial optimization. Solutions of the Metric TSP are generally used for costs minimization tasks in logistics, manufacturing and genetics. Since this problem is NP-hard, heuristic algorithms providing near optimal solutions in polynomial time will be considered. The aim of this article is to find Pareto optimal heuristics for Metric TSP under criteria of error rate and run time efficiency. Two real-life kinds of inputs are intercompared - VLSI Data Sets based on very large scale integration schemes and National TSPs that use geographic coordinates. There is a classification of algorithms for Metric TSP in the article. Feasible heuristics and their prior estimates are described. The details of the research methodology are provided. Finally, results and prospective research are discussed.
The routing problems are important for logistic and transport sphere. Basically, the routing problems related to determining the optimal set of routes in the multigraph. The Chinese postman problem (CPP) is a special case of the routing problem, which has many potential applications. We propose to solve the MCPP (special NP-hard case of CPP, which defined on mixed multigraph) using the reduction of the original problem into General Travelling Salesman Problem (GTSP). The variants of CPP are pointed out. The mathematical formulations of some problems are presented. The algorithm for reduction the MCPP in multigraph into GTSP is shown. The experimental results of solving MCPP in multigraph through the reduction into GTSP are presented.