Optimization Hub location and routing problem application for transportation network application for less than truck load transportation

Abstract

During the sensitive period of the Covid 19 pandemic, when the economy and other industries have been greatly affected, the fact that the Logistics and Supply Chain industry could have that little impact and remains its smooth development has raised the curiosity of many all around the world. Less than truckload (LTL) shipments are gaining more and more attention, especially in the goodstransport industry (fresh food, drinks, or parts), where freight companies must collect small freight or parcels from many different origins before distributing them to various destinations, due to the increasing pressure on logistics systems to perform in terms of cost and delivery time while reducing shipment loads. Hence, to solve this problem, the logistics systems need to minimize the cost by using optimal solution for shipping time and fixed costs. In this thesis, the optimization model for Hub Location And Vehicle Routing Problem is used to assist to find the optimum routes for the network. This paper introduces a modern that Mixed Integer linear Programing approach for solving complex optimization tasks and identifying potential trade-offs between conflicting objectives. It can help decision-makers better understand selection systems and develop sustainable pathways to selection targets. This case study is carried out by the optimization model and implemented by CPLEX Software, and it is then compared to previous approaches to have the best solution using real data from the study. The findings show that this modern algorithm can produce better results than heuristic and metaheuristic algorithms and is also capable of providing an exact solution.