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Optimizing the design of an airport baggage handling transport system (BHTS) with respect to the minimization of the total costs and energy consumption is essential to reduce costs and Carbon dioxide (chemical formula CO₂) emissions in airport operations. This paper introduces a mathematical model that comprehensively considers relevant costs regarding the operation of belt conveyors in a BHTS. Specifically, the <bold>Cap</bold>ital <bold>Ex</bold>penditure (CapEx) and <bold>Ope</bold>rational <bold>Ex</bold>penditure (OpEx) are considered in the airport BHTS cost function. Furthermore, to include the impact of CO₂ emissions, the offsetting costs of CO₂ emissions are included in the airport BHTS cost function. This function forms the basis of an objective function that can be used to optimize the airport BHTS&#x2019;s design by metaheuristic algorithms. Three state-of-the-art particle swarm optimization (PSO) algorithms are utilized to solve the airport BHTS optimization problem. The results of experiments show that the three PSO variants can solve the optimization problem effectively and efficiently. The self-regulation PSO algorithm performed the best in terms of CPU time and has been used for the case studies. Extensive tests of the impact of key parameters, e.g., capacity and system length, on the optimized solutions have been conducted. Experiments show that a system with several belt conveyors of shorter lengths performs better than a system with one long conveyor. In reality however, more parameters play a role like the varying baggage throughput per hour and therefore the BHTS problem needs to be optimized case-by-case. Optimizing an airport BHTS design leads to a significant reduction in CO₂ emission and thus costs.