The aim of the article is to develop an algorithm to optimize logistics and distribution of the supply chain, taking into account transport costs, inventory and customer demand. Design/Methodology/Approach: To solve the problem, the class of optimization problems and the traveling salesman problem were used. The boundary conditions and the objective function were determined. The optimization criterion was to minimize the total transport costs, kilometers traveled and the time needed to complete the task. Findings: As a result of the analyzes and calculations performed, the optimization task was performed. With the help of the prepared software, a map of goods delivery points was determined, the total route and the route with individual points marked, and various solution methods were tested. Practical Implications: The model presented in the article can be used in a supply chain application to optimize routes, costs and delivery time. Originality/Value: A novelty is the preparation of algorithms and universal software using the R language, which was used in the application of an intelligent IT system in a distributed model, controlling the supply chain, enabling personalization and identification of products in real time.
Purpose:The main purpose of the article is to identify the demand curve and to forecast demand in subsequent periods using the Metropolis-Hastings algorithm. Design/Methodology/Approach: The Metropolis-Hastings algorithm belonging to the Markov Chain Monte Carlo was used to identify the demand curve and to forecast the demand in subsequent periods. This method consists in generating (drawing) a sample in accordance with the modified distribution and the possibility of rejecting a new sample in case of insufficient improvement of the quality index. Findings: The results of the conducted research indicate that the presented solution of generating a sample in accordance with the modified distribution and the possibility of rejecting a new sample in the event of insufficient improvement of the quality index is effective in identifying and forecasting the demand.Practical Implications: The algorithm presented in the article can be used to forecast stays taking into account the product life curve. Originality/Value: A novelty is the use of the Metropolis-Hastings algorithm to identify the demand curve and the forecast of demand in subsequent periods to determine the strategy of long-term products by analyzing the sales volume of the product.
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