Мультиагентні метаевристичні методи рішення задачі управління запасами

Abstract

Currently, the problem of insufficient efficiency of supply chain management is relevant. One of the problems solved within the limits of the specified problem is the optimization problem of inventory management. Optimization methods that find an approximate solution using a directed search have a high probability of reaching a local extremum. Optimization methods that find an exact solution have a high computational complexity. Random search methods do not guarantee convergence. In this connection, there is a problem of insufficient efficiency of optimization methods, which needs to be solved. The article considers the task of inventory management as a component of the task of effective supply chain management. To solve this problem, the existing multi-agent metaheuristic methods were investigated. To improve the quality of solving this problem, particle swarm optimization and artificial fish swarm algorithm were chosen, which are modified by introducing dynamic parameters and Cauchy and Gaussian distributions. Parallel algorithms based on CUDA technology are proposed for these methods. This made it possible to ensure high speed and accuracy of the decision. The proposed methods are designed for software implementation in the Matlab package using the Parallel Computing Toolbox, which speeds up the process of finding a solution. The software that implements the proposed methods was developed and researched based on the data of the logistics company "Ekol Ukraine". The conducted experiments confirmed the functionality of the developed software and allow us to recommend it for practical use in solving supply chain management problems. Prospects for further research are to test the proposed methods on a wider set of test databases.