As a result of globalization and economic growth, there is an increasing demand for the transport of goods worldwide, which has a negative impact on the environment due to higher fuel consumption and increased wear and tear on roads and vehicles. Increased freight traffic leads to higher greenhouse gas emissions and increased levels of particulate matter in the atmosphere. I approached this problem from the perspective of increasing the efficiency of individual trucks and logistics companies by using an algorithmically composed transport plan. The algorithm delegates which truck will take on individual loads from a given set of transport orders and a set of trucks, thus creating a transport plan for a given time period. The goal of the algorithm is to minimize empty space on trucks and, consequently, minimize empty LDM kilometers. I compared the results of the algorithm with data on the transport plan compiled by a large transport company from Slovenia in May 2024. I found that the efficiency of the transport plan could definitely be improved, but good planning requires very accurate data on transport orders, which can be used to develop a sufficiently good heuristic algorithm for transport planning using an adapted nearest neighbor method.
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