Reducing CO₂ Emissions of an Airport Baggage Handling Transport System Using a Particle Swarm Optimization Algorithm

Abstract

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 Capital Expenditure (CapEx) and Operational Expenditure (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'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-bycase. Optimizing an airport BHTS design leads to a significant reduction in CO2 emission and thus costs.INDEX TERMS Baggage handling transport system, airport operations, belt conveyors, CO2 emission, particle swarm optimization.

Test Case Prioritization, Selection, and Reduction Using Improved Quantum-Behaved Particle Swarm Optimization

“…Table 1 shows the application of PSO Algorithms for regression testing methods along with their optimization criteria. PSO algorithms have become one of the state-of-the-art algorithms and show promising results in various domains, e.g., reduction of CO 2 emissions in air baggage systems [24]. The original PSO, on the other hand, had issues, such as getting trapped in local optima and premature convergence [25].…”

Solving flexible job shop scheduling problems in manufacturing with Quantum Annealing

“…al minimize the total costs and energy consumption of the design of an airport baggage handling transport system using several particle swarm optimization algorithms. The results show that their algorithms can reduce CO 2 emission and costs within a short amount of time [7]. Moreover, such metaheuristics are used for many other optimization problems, such as the parameter estimation of solar cells [8].…”

Multi-Objective Dynamic Economic Emission Dispatch with Electric Vehicle–Wind Power Interaction Based on a Self-Adaptive Multiple-Learning Harmony-Search Algorithm

“…In recent years, swarm intelligence optimization algorithms were widely used to solve the practical optimization problem in many fields [26,27] because of their good global search and convergence performance and strong robustness. Meanwhile, such algorithms were also applied to solve the DEED problem.…”