A comparison of metaheuristic algorithms for solving three-dimensional bin packing problems

Multi-dimensional bin packing problems play a central role in the planning of freight transportation and supply chain systems to reduce costs and improve the utilization of facilities and equipment. They arise as a component of operational decision support or as part of more complex strategic decision processes. Therefore, there is a need for solution methods capable of handling large instances. This doctoral thesis aims to compare metaheuristic algorithms used in solving three-dimensional bin packing problems. A dataset consisting of 8 classes, 32 subclasses, and 320 instances is employed for the problem's solution. The thesis focuses on solving three-dimensional bin packing problems using four different metaheuristic algorithms: Firefly Algorithm, Cuckoo Search Algorithm, Tuna Swarm Optimization, and Honey Badger Algorithm. Each algorithm offers swarm intelligence-based optimization approaches that mimic natural behavior. Tuna Swarm Optimization and Honey Badger Algorithm contribute to the literature as they have not been previously applied to bin packing problems. The Firefly Algorithm and Cuckoo Search Algorithm, being commonly used for one and two-dimensional bin packing problems, are expected to provide valuable insights into the solution quality of the more challenging three-dimensional problem.