Optimization of Logistics and Distribution of the Supply Chain, Taking into Account Transport Costs, Inventory and Customer Demand

Abstract

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.

Reviewing the use of big data in supply chain optimization in the USA

“…The use of big data technology allows enterprises to monitor and visualize information flow through supply chains in real time, enabling them to make informed decisions and improve supply chain resilience (Liu et al, 2023;Akindote et al, 2023). Furthermore, the utilization of big data in supply chain management can optimize logistics and distribution, taking into account transport costs, inventory, and customer demand, thereby improving overall supply chain network performance (Golabek et al, 2021;Wu & Shi, 2017).…”

Descriptive Analysis of Supply Chain Data: Patterns, Relationships, and Strategic Insights

“…Advanced optimization algorithms further boost logistics efficiency. For instance, a study introduced a comprehensive algorithm to reduce transport costs and the time Descriptive Analysis of Supply Chain Data: Patterns,Relationships,and Strategic Insights 116 required for logistics tasks by incorporating the traveling salesperson problem (Golabek et al, 2021;Zampeta and Chondrokoukis, 2023a;2023b). Additionally, predictive analytics using Long Short-Term Memory (LSTM) networks have revolutionized inventory management, enabling accurate sales forecasting and efficient stock management (Pliszczuk et al, 2021).…”