Computational Ergo-Design for a Real-Time Baggage Handling System in an Airport

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors Overall, this paper is good, but I have the following comments: 1）The paper proposes a computational ergo-design approach for a real-time baggage handling system, but it lacks a clear comparison with existing systems. What are the specific advantages of the proposed ARTEMIS architecture over current baggage handling systems in terms of efficiency, energy consumption, and operational effectiveness? 2）The role of the operator is emphasized as crucial in minimizing delays and ensuring smooth operation. However, the paper does not discuss any training or support systems for operators to enhance their decision-making and response times. What specific measures are recommended to improve operator efficiency? 3) Various types of AGV and operator faults were discussed, but does not provide a comprehensive solution or mitigation strategy for these faults. What are the proposed solutions or technologies to prevent or quickly resolve these faults? 4) The human-computer interface (HCI) design is mentioned, but there is no user feedback or usability testing results provided. 5) The paper concludes that the Mixed Advance/Delay Strategy is the best overall option. However, it does not explore the potential integration of machine learning or AI to further optimize the system. What are the possibilities for incorporating AI to predict and manage baggage flow more effectively? 6) The paper suggests future research could explore improving operator decision-making through intelligent agents and automation. What specific areas of intelligent agents and automation are recommended for future research, and how would they integrate with the existing system? 7）The quality of some pictures still needs to be improved. Some fonts are too small and very difficult to read.

Author Response

Comments and Suggestions for Authors

Overall, this paper is good, but I have the following comments: 

1）The paper proposes a computational ergo-design approach for a real-time baggage handling system, but it lacks a clear comparison with existing systems. What are the specific advantages of the proposed ARTEMIS architecture over current baggage handling systems in terms of efficiency, energy consumption, and operational effectiveness?

Thank you for your constructive comment. The following text has been included:

The ARTEMIS architecture introduces several features that distinguish it from conventional baggage handling systems, especially in terms of efficiency, energy consumption, and operational effectiveness.

Unlike traditional BHS that rely on static rules and limited feedback, ARTEMIS employs simulation-driven strategies to optimize baggage flow. It integrates MATLAB & Simulink simulations to test performance under various failure and load conditions, allowing for proactive bottleneck identification and scenario planning. A distinctive feature is ARTEMIS’s suite of AGV dispatching strategies—such as Turnstile, On-Demand, Delay Parking, Needs Prediction, and Mixed Advance/Delay—which enable dynamic adjustment to real-time baggage demand. These strategies allow ARTEMIS to minimize queue wait times and improve throughput efficiency more effectively than fixed-route AGV systems typically used in current BHS.

ARTEMIS includes a dedicated data analysis component that continuously evaluates AGV travel distances, usage rates, and speed to estimate and visualize energy consumption over time. Unlike most existing systems, where energy use is only indirectly considered, ARTEMIS actively links energy metrics to operational strategies. By adjusting AGV deployment based on predicted load, the system reduces idle time and unnecessary motion, improving energy efficiency while maintaining throughput.

ARTEMIS places strong emphasis on usability and decision support through a robust Human-Computer Interaction (HCI) layer. Its interface, grounded in established usability principles, enables operators to configure simulation scenarios, monitor real-time AGV behavior, and receive alerts about performance thresholds.

 

 

2）The role of the operator is emphasized as crucial in minimizing delays and ensuring smooth operation. However, the paper does not discuss any training or support systems for operators to enhance their decision-making and response times. What specific measures are recommended to improve operator efficiency?

Thank you for this remark, which we found very relevant. In response, we have added two paragraphs in section 3.2 addressing measures we recommend to improve operator efficiency. These measures mainly concern training and awareness. The following text has been included:

Improving operator efficiency also involves training. Training the human operator in techniques for managing complex situations they may face is indeed a positive approach in preparing this key actor in the baggage management system. Certain training methods for human operators in highly constrained production environments make use of augmented reality scenarios, combined with a voice-based digital assistant, to immerse the trainee operator in use cases with the desired level of complexity. A good illustration of this approach can be found in [LONG_2017].

Beyond the training, which is undoubtedly valuable for the operator, it also seems useful to observe them (with their consent, of course) during their real-world interventions in baggage management. This observation should be followed by a debriefing and discussion with the operator, to identify areas for improvement in their practical actions. This exchange also serves as an opportunity to raise the operator’s awareness of the risks of accumulating delays due to slow response times, which are themselves often caused by the occurrence of critical situations that were insufficiently anticipated.

It seems useful to point out that the objective here was not to highlight, in a comparative approach, the performance of the ARTEMIS system, but rather to present its relevance, characteristics, and functioning. Moreover, to the best of our knowledge, the literature does not offer any system that is perfectly comparable to ARTEMIS in terms of its features. This article positions itself on a dual concept that includes, on the one hand, baggage handling, and on the other hand, a particular focus on ergo-design aspects, which also determine important user-related dimensions such as acceptability and sustainability [TAUF_2019].

 

Longo, F.; Nicoletti L.; Padovano, A. Smart operators in industry 4.0: A human-centered approach to enhance operators’ capabilities and competencies within the new smart factory context. Computers & Industrial Engineering 2017, 113, 144-159.

Taufik, N.; Hanafiah, M.H. Airport passengers' adoption behaviour towards self-check-in Kiosk Services: the roles of perceived ease of use, perceived usefulness and need for human interaction. Heliyon 2019, 5(12).

 

 

3) Various types of AGV and operator faults were discussed, but does not provide a comprehensive solution or mitigation strategy for these faults. What are the proposed solutions or technologies to prevent or quickly resolve these faults?

Thank you for your observation. While system failures are not discussed in the article, predictive maintenance methods, such as observers (Fragkoulis, 2008) or Bayesian techniques (Tabella et al., 2024), could be used for effective fault detection and localization.

 

Fragkoulis, D. Détection et localisation des défauts provenant des capteurs et des actionneurs: application sur un système non linéaire. Diss. Université Paul Sabatier-Toulouse III, 2008.

Tabella, Gianluca, et al. "Bayesian fault detection and localization through wireless sensor networks in industrial plants." IEEE Internet of Things Journal 11.8 (2024): 13231-13246.

 

 

4) The human-computer interface (HCI) design is mentioned, but there is no user feedback or usability testing results provided.

Thank you for your comment. It is important to clarify that our focus in this article does not include any HCI other than the one used for simulation purposes. Consequently, it is expected that we do not address it in the context of the production system.

 

 

5) The paper concludes that the Mixed Advance/Delay Strategy is the best overall option. However, it does not explore the potential integration of machine learning or AI to further optimize the system. What are the possibilities for incorporating AI to predict and manage baggage flow more effectively?

Thank you for your suggestion. While our study did not utilize machine learning or AI techniques for baggage flow prediction and management, it reflects the real-time nature of airport operations, where flight schedules and baggage counts are known in advance, enabling dynamic responses without the need for historical data.

 

 

6) The paper suggests future research could explore improving operator decision-making through intelligent agents and automation. What specific areas of intelligent agents and automation are recommended for future research, and how would they integrate with the existing system?

Thank you for your constructive comment. The agent paradigm is well-suited either to providing user assistance (activity assistance, or even mediation) or to improving user decision-making (particularly through the development of multi-agent systems reinforcement learning). We explored the first type of assistance about ten years ago and have recently begun exploring the second (the following two articles present an initial state-of-the-art).

Agrawal, A., Won, S. J., Sharma, T., Deshpande, M., & McComb, C. (2021). A multi-agent reinforcement learning framework for intelligent manufacturing with autonomous mobile robots. Proceedings of the Design Society, 1, 161-170.

Bahrpeyma, F., & Reichelt, D. (2022). A review of the applications of multi-agent reinforcement learning in smart factories. Frontiers in Robotics and AI, 9, 1027340.

 

 

7）The quality of some pictures still needs to be improved. Some fonts are too small and very difficult to read.

Thank you for your comment. All figures were checked for quality and readability. In addition, Figure 4 was translated into English, and Figures 5, 6, 7, 8, 9, and 10 were reorganized for better visibility.

 

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

1. It is not clear how computational ergo-design is different from traditional optimization methods. It would help if the paper showed that computational ergo-design works better than other smart transport systems.

2. It is important to check if the simulation results are reliable. The paper needs to show that the results stay the same when tested many times.

3. The paper should compare the ARTEMIS system with real-world baggage handling systems. 

 

Comments on the Quality of English Language

Somewhat.

Author Response

Quality of English Language

(x)       The English could be improved to more clearly express the research.
( )        The English is fine and does not require any improvement.

Thank you for your comment. We have carefully reviewed the entire article and made numerous revisions and corrections to improve its fluency and clarity.

 

Comments and Suggestions for Authors 

1. It is not clear how computational ergo-design is different from traditional optimization methods. It would help if the paper showed that computational ergo-design works better than other smart transport systems.

Thank you for your constructive comment. The following text has been included in the conclusion:

Unlike traditional optimization methods, which typically focus on efficiency and performance without integrating human factors, computational ergo-design emphasizes the interaction between humans, technology, and infrastructure. It continuously integrates human factors into the design process, ensuring that systems are not only effective but also aligned with human needs and behavior. By incorporating real-time data processing and feedback, it enables dynamic adaptation to changing conditions and evolving human interactions. This approach accounts for both human behavior and system feedback, as well as environmental factors, facilitating ongoing optimization. While traditional optimization methods may lack this dynamic, human-centered approach, computational ergo-design brings a more comprehensive and adaptable solution for complex transport systems.

 

2. It is important to check if the simulation results are reliable. The paper needs to show that the results stay the same when tested many times.

Thank you for your comment. Multiple tests confirmed the simulation results. The strategies ensure that AGVs can flexibly adapt to variable baggage flow.

 

3. The paper should compare the ARTEMIS system with real-world baggage handling systems. 

Thank you for your comment, which we fully understand. However, we would like to clarify that our aim was not to highlight the performance of the ARTEMIS system in a comparative perspective, but rather to present its relevance, characteristics, and functioning. Referring to existing systems primarily served to highlight the specific features of our own system.

 

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

This is a very interesting paper about the proposal of a real-time baggage handling monitoring system using a computational ergo-design approach. There is merit to publishing this paper in the journal. However, some comments can be addressed by the authors:

 

Page 1, Line 11-30: A brief problem statement should be presented at the start of the abstract which flows accurately to connect with the focus of the paper in the abstract.

 

Page 2, Line 52-53: For the statement “While human-centered design and ergonomics are widely discussed in various fields…”, perhaps it would be good to give a number of examples of non-robotic systems or studies to be more comprehensive before moving into the problem statement. A suggested group of literature that can be cited alongside this include the following articles. Please strongly consider citing them:

• https://thescipub.com/abstract/ajassp.2016.451.458: Ng, P. K.* and Jee, K. S. (2016). Design and Development of an Ergonomic Milling Machine Control Knob Using TRIZ Principles. American Journal of Applied Sciences, 13(4), 451-458.
• https://www.mdpi.com/2411-5134/6/2/35: Lim, S. H. and Ng, P. K.* (2021). The Design and Development of a Foldable Wheelchair Stretcher. Inventions, 6(2), 35.

 

Under the conclusion, perhaps the authors could also include the potential use of the theory of inventive problem solving (TRIZ) as a possible strategy to improve the design and innovation of the proposed system.

Author Response

Comments and Suggestions for Authors

This is a very interesting paper about the proposal of a real-time baggage handling monitoring system using a computational ergo-design approach. There is merit to publishing this paper in the journal. However, some comments can be addressed by the authors:

 

Page 1, Line 11-30: A brief problem statement should be presented at the start of the abstract which flows accurately to connect with the focus of the paper in the abstract.

Thank you for your constructive comment.

Autonomous robots used in airport logistics face significant challenges in dynamic, human-shared environments, such as baggage handling areas, where unpredictable events and human interactions introduce additional complexity. Despite the growing importance of human-centered design and ergonomics in various fields, a significant gap exists in applying these principles to robotic systems in airport environments. Specifically, there is a lack of research focused on designing systems that effectively balance task efficiency, energy consumption, and human safety in both robot-only and human-robot shared contexts.

So, the following text has been included in the abstract :

Despite the growing importance of human-centered design and ergonomics in various fields, a significant gap exists in applying these principles to robotic systems in airport environments.

 

Page 2, Line 52-53: For the statement “While human-centered design and ergonomics are widely discussed in various fields…”, perhaps it would be good to give a number of examples of non-robotic systems or studies to be more comprehensive before moving into the problem statement. A suggested group of literature that can be cited alongside this include the following articles. Please strongly consider citing them:

• https://thescipub.com/abstract/ajassp.2016.451.458: Ng, P. K.* and Jee, K. S. (2016). Design and Development of an Ergonomic Milling Machine Control Knob Using TRIZ Principles. American Journal of Applied Sciences, 13(4), 451-458.
• https://www.mdpi.com/2411-5134/6/2/35: Lim, S. H. and Ng, P. K.* (2021). The Design and Development of a Foldable Wheelchair Stretcher. Inventions, 6(2), 35.   (Tests d’utilisabilité, ex de l’article : The usability tests evaluated the (1) regular, (2) folding and (3) alternate functions of the stretcher.)

Under the conclusion, perhaps the authors could also include the potential use of the theory of inventive problem solving (TRIZ) as a possible strategy to improve the design and innovation of the proposed system.

Thank you for your suggestions. We have added the following references:

Examples in various fields:

Arkouli, Z.; Michalos, G.; Kokotinis, G.; Makris, S. Worker-centered evaluation and redesign of manufacturing tasks for ergonomics improvement using axiomatic design principles. CIRP Journal of Manufacturing Science and Technology 2024, 55, 188-209.

Nawi, A. M. ; Yusof, F. M. Form, Function, and Comfort : Rethinking Product Design through the Lens of Ergonomics and Aesthetics. International Journal of Research and Innovation in Social Science 2024, 8(9), 1358-1373.

Contreras-Cruz, A. ; Kirbac, A. ; Dennett, C. ; Daim, T. U. Human-centered design as a tool to improve employee experience : The case of a US plant-based food manufacturer. Technology in Society 2023, 73, 102248.

Lim, S. H.; Ng, P. K. The design and development of a foldable wheelchair stretcher. Inventions 2021, 6(2), 35.

Kadir, B. A. ; Broberg, O. Human-centered design of work systems in the transition to industry 4.0. Applied Ergonomics 2021, 92, 103334.

Sakthi Nagaraj, T. ; Jeyapaul ,R. ; Vimal ,K.E.K. ; Mathiyazhagan, K. Integration of human factors and ergonomics into lean implementation : Ergonomic-value stream map approach in the textile industry. Production Planning & Control 2019, 30(15), 1265-1282.

Lin, C. J. ; Belis, T. T. ; Kuo, T. C. Ergonomics-Based Factors or Criteria for the Evaluation of Sustainable Product Manufacturing. Sustainability 2019, 11(18), 4955.

Bernard, F. ; Bazzaro, F. ; Sagot, J.-C. ; Paquin, R. (2017). Consideration of human factor in aeronautical maintainability. 2017 Annual Reliability and Maintainability Symposium (RAMS), 1 7.

Boy, G. A. Human-centered design of complex systems : An experience-based approach. Design Science 2017, 3, e8.

Moussavi, S. E. ; Mahdjoub, M. ; Grunder, O. Reducing production cycle time by ergonomic workforce scheduling. IFAC-PapersOnLine 2016, 49(12), 419-424.

Zhou, D. ; Chen, J. ; Lv, C. ; Cao, Q. A method for integrating ergonomics analysis into maintainability design in a virtual environment. International Journal of Industrial Ergonomics 2016, 54, 154-163.

Karsh, B.-T. ; Holden, R. J. ; Alper, S. J. ; Or, C. K. L. A human factors engineering paradigm for patient safety : Designing to support the performance of the healthcare professional. BMJ Quality & Safety 2006, 15(suppl 1), i59-i65.

Schulze, H. ; Brau, H. ; Haasis, S. ; Weyrich, M. ; Rhatje, T. Human-Centered design of engineering applications: Success factors from a case study in the automotive industry. Human Factors and Ergonomics in Manufacturing & Service Industries 2005, 15(4), 421-443.

Jensen, P. L. Human factors and ergonomics in the planning of production. International Journal of Industrial Ergonomics 2002, 29(3), 121-131.

 

Examples in various creative methods :

Russo, D.; Spreafico, C. TRIZ-based guidelines for eco-improvement. Sustainability 2020, 12(8), 3412.

Armstrong, S. D.; Brewer, W. C.; Steinberg, R. K. Usability testing. In Handbook of human factors testing and evaluation (pp. 403-432), CRC Press, 2019.

Ng, P. K.; Jee, K. S. Design and development of an ergonomic milling machine control knob using TRIZ principles. American Journal of Applied Sciences 2016, 13(4), 451-458.

Hartono, M.; Wahyudi, R. D.; Susilo, A. The applied model of kansei engineering, servqual, kano, and triz considering ergo-sustainability: A case study on international airport services. International Journal of Technology-Special Issue on SEANES 2016, 1-12.

Hurtado, N.; Ruiz, M.; Orta, E.; Torres, J. Using simulation to aid decision making in managing the usability evaluation process. Information and Software Technology 2015, 57, 509-526.

 

Author Response File: Author Response.pdf

Reviewer 4 Report

Comments and Suggestions for Authors

You set out to determine the optimal system architecture for real-time baggage monitoring and the baggage handling process. Was this accomplished?

Line # 187 – ARTEMIS components ensure smooth and efficient baggage handling operation.

What was your motivation behind choosing the five strategies and the six scenarios to test using simulation?

Why is energy consumption an issue for you to consider as part of your simulation, especially in today’s vast airport operations?

You mention that the Mixed Advanced/delay strategy is best as it keeps energy usage low. How much savings are we talking about in kWh?

Please provide a rubric for comparison when it comes to energy savings, smooth baggage flow, and waiting times. For example, how much time savings are we talking about? How much shorted are queues, etc.

Author Response

Comments and Suggestions for Authors

You set out to determine the optimal system architecture for real-time baggage monitoring and the baggage handling process. Was this accomplished?

Thank you for your constructive comment. Beyond determining an optimal architecture, one of the key challenges of our research is to determine an efficient system architecture for real-time baggage handling and facilitate the appropriate deployment of fleets of baggage conveyor robots. We are currently replicating the work presented in this article for a larger airport (this is the subject of the ALPHA project cited in the article for research funding).

 

 

Line # 187 – ARTEMIS components ensure smooth and efficient baggage handling operation.

What was your motivation behind choosing the five strategies and the six scenarios to test using simulation?

Thank you for your question.

Although we did not adopt a formal approach, our work was inspired by real-world examples reflecting variable baggage flow in an airport, specifically, different flow scenarios observed throughout the day.

 

 

Why is energy consumption an issue for you to consider as part of your simulation, especially in today’s vast airport operations?

Thank you for your comment. The following text has been included in the introduction section:

Energy consumption is a critical factor in our simulation as airports prioritize sustainability and efficiency. These complex environments have significant energy demands from multiple integrated systems. While baggage handling system data remains limited, optimizing energy use is essential to balance operational performance with environmental impact (Ortega Alba & Manana, 2016, 2017).

Airports worldwide are adopting energy-efficient designs to meet sustainability targets. Effective energy management reduces both environmental impact and operational costs, making it vital for modern airport system design and simulation (Mancinelli et al., 2021) (Yu et al., 2025). Efficient energy management not only supports environmental sustainability but also reduces operational costs, making it a vital component of airport design and operation.

 

Ortega Alba, S.; Manana, M. Energy Research in Airports: A Review. Energies 2016, 9(5).

Ortega Alba, S.; Manana, M. Characterization and Analysis of Energy Demand Patterns in Airports. Energies 2017, 10(1).

Mancinelli, E.; Canestrari, F.; Graziani, A.; Rizza, U.; Passerini, G. Sustainable Performances of Small to Medium-Sized Airports in the Adriatic Region. Sustainability 2021, 13(23).

Yu, H.; Bao, S.; Man, Q.; Xie, H.; Guo, J. A Study on the Measurement and Prediction of Airport Carbon Emissions Under the Perspective of Carbon Peak. Engineering Proceedings 2025, 80(1).

 

 

You mention that the Mixed Advanced/delay strategy is best as it keeps energy usage low. How much savings are we talking about in kWh?

Thank you for your comment. Tables 1 through 5 have been included to illustrate that the mixed advance/delay strategy achieves the most effective balance between minimizing energy consumption and reducing baggage waiting time.

 

 

Please provide a rubric for comparison when it comes to energy savings, smooth baggage flow, and waiting times. For example, how much time savings are we talking about? How much shorted are queues, etc.

Thank you for your suggestion. Tables 1 through 6 serve as a comparative framework for evaluating the performance of the different strategies.

 

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

no more comments

Reviewer 2 Report

Comments and Suggestions for Authors

The paper has been suitably revised. The authors have provided all the comments correctly.