A simulation approach to modelling baggage handling systems at an international airport

Cavada, Juan Pablo; Cortés, Cristián E.; Rey, P.

doi:10.1016/j.simpat.2017.01.006

Cited by 32 publications

(12 citation statements)

References 6 publications

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“…Based on the obtained results, the manager can prepare detailed plans of the areas to be modified or expanded. In this case study, for example, the passenger security screening and passport control in the departure area and immigration facilities in the arrival area need detailed projects that can be supported by more sophisticated method such as the simulation model [25][26][27][28][29][30][31][32][33][34][35].…”

Section: Discussionmentioning

confidence: 99%

“…where G is the number of gates; AS is the total number of seats offered by all aircraft (Schengen/extra-Schengen); SRf is the seat ratio adjustment factor; Gf is the open gate lounge factor; S(S/ST) is the space for seated/standing person (m 2 /pax); and X is the additional space ratio to account for boarding operation (% of global gate area). Equation (34) gives the width of the pier on side 1 or side 2 (GLd1/2), which depends on the area of side 1 or 2 of the pier (A1/2) and on the pier length (PL): Equation (30) gives the number of gates for Schengen (GS) or extra-Schengen (GX) passengers:…”

Section: Spreadsheet N5-boarding Gatesmentioning

confidence: 99%

“…Indeed, they can analyze passenger flow and queuing processes within airport terminals, allowing them to reproduce the operations of actual processes including implicit variables such as human behavior and attitudes [31]. Simulation approaches have been used to model every subsystem in the airport terminal and many studies can be found in the literature [32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

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Airport Landside Sustainable Capacity and Level of Service of Terminal Functional Subsystems

2020

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The airport terminal is a complex building composed of many functional subsystems dimensioned to host passenger traffic, considering an appropriate level of service (LoS). The most widely known method to assess the LoS and design the terminal areas is the Airport Development Reference Manual by the International Air Transport Association (IATA). Based on this, a calculation tool in Microsoft Excel® was implemented to assess the capacity and dimension of the facilities for each functional subsystem of the terminal. The tool, composed of nine correlated spreadsheets, is a useful model to design new structures, evaluate the LoS currently offered, plan interventions, and face the new rules of passenger distancing due to the COVID-19 emergency. An international airport terminal with 20 million/year including Schengen and extra-Schengen passengers was studied. The LoS of the terminal subsystems was assessed and the areas needed for each subsystem were calculated. In the analyzed case study, most subsystems (departure hall, check-in, boarding gates, baggage claim, and arrival halls) were over-designed, according to the definition of the IATA LoS. This means that available spaces for queues and holding are sufficiently large to easily face the new rules of social distancing for passengers due to the recent COVID-19 emergency.

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Section: Discussionmentioning

confidence: 99%

Section: Spreadsheet N5-boarding Gatesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Airport Landside Sustainable Capacity and Level of Service of Terminal Functional Subsystems

2020

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“…Much work is dedicated to building tools for designing BHS [15][16][17]. Simulation models are mainly used.…”

Section: State Of the Artmentioning

confidence: 99%

Simulation Model for the Estimation of Energy Consumption of the Baggage Handling System in the Landside Area of the Airport

Kierzkowski

Kisiel

2021

Energies

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The purpose of this paper was to develop a simulation model to perform a sensitivity analysis of the energy consumption of an airport baggage handling system to a change in resource allocation strategy. This is a novel approach as this aspect has not been considered until now. This aspect, in turn is very important in terms of sustainability. The paper presents the detailed structure of the model and the data on which it operates. It is universal and can be the basis for analyzing any structure of the baggage handling system in the landside of any airport. An example analysis has shown that even up to 35% benefits can be gained by using the model. Three scenarios were analyzed in the model (dedicated check-in desks scenario, common desks scenario and mixed strategy scenario). However, the model is not limited to these strategies and any resource allocation is possible. The model is useful both for planning a new system as well as optimizing an existing system during its operation.

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“…In [16] an alternative transport and scheduling method, as well as the application of a prototype of a partly automated baggage loading and unloading vehicle have been investigated using simulation. [17] presents a microscopic simulation model for a BHTS that fully integrates all baggage-related subsystems. These include passenger arrival to check-in queues, baggage check-in, security screening, sorting, transport to the aircraft and loading.…”

Section: Introductionmentioning

confidence: 99%

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

Lodewijks

Cao²,

Zhao

et al. 2021

IEEE Access

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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 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.

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A simulation approach to modelling baggage handling systems at an international airport

Cited by 32 publications

References 6 publications

Airport Landside Sustainable Capacity and Level of Service of Terminal Functional Subsystems

Airport Landside Sustainable Capacity and Level of Service of Terminal Functional Subsystems

Simulation Model for the Estimation of Energy Consumption of the Baggage Handling System in the Landside Area of the Airport

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

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