A Restaurant Management System Based on Multi-Agent Oriented Programming Paradigm

Abstract

In this study, a restaurant management system was designed based on the Multi-Agent Oriented Programming (MAOP) paradigm and developed with the JaCaMo agent development platform. The MAOP paradigm constitutes of the Agent Oriented Programming (AOP), Environment Oriented Programming (EOP), Organization Oriented Programming (OOP), and Interaction Oriented Programming (IOP) paradigms and focuses on modeling the agent, environment, organization, and interaction dimensions as first-class abstractions, thus bringing out more than the total of each dimension. The JaCaMo platform is a combination of the Jason, CArtAgO, and Moise platforms, which are associated with the AOP, EOP, and OOP paradigms, respectively. Therefore, JaCaMo is a holistic platform that is able to take advantage of each integrated paradigm and tool. The main contribution of this paper is, to the best of our knowledge, being the first study in the literature that applies the MAOP paradigm for the management of the services provided by restaurants. The proposed system is deployed with Docker and supports web-based real-time monitoring of the restaurant services via REST API. We also applied the Blackboard design pattern for coordination of the agents. Besides, orders have been ranked according to their priority scores based on the prices and the timing requirements, thus optimizing restaurant profitability.

Keywords

• JaCaMo
• Multi-Agent Oriented Programming Paradigm
• Multi-Agent System
• Restaurant Management

Çoklu Etmen Odaklı Programlama Paradigmasına Dayalı Bir Restoran Yönetim Sistemi

Abstract

Bu çalışmada, Çoklu Etmen Odaklı Programlama (MAOP) paradigmasına dayalı bir restoran yönetim sistemi tasarlanmış ve JaCaMo etmen geliştirme platformu ile geliştirilmiştir. MAOP paradigması, Etmen Odaklı Programlama (AOP), Ortam Odaklı Programlama (EOP), Organizasyon Odaklı Programlama (OOP) ve Etkileşim Odaklı Programlama (IOP) paradigmalarını bir araya getirmektedir, ve etmen, ortam, organizasyon ve etkileşim boyutlarını birinci sınıf soyutlamalar olarak modellemeye odaklanarak her boyutun toplamından daha fazlasını ortaya çıkarmaktadır. JaCaMo platformu, sırasıyla AOP, EOP ve OOP paradigmalarıyla ilişkili olan Jason, CArtAgO ve Moise platformlarının birleşimidir. Bu sayede, JaCaMo, entegre edilmiş her paradigma ve araçtan yararlanabilen bütünsel bir platformdur. Bu çalışmanın temel katkısı, bildiğimiz kadarıyla, restoranlar tarafından sunulan hizmetlerin yönetimi için MAOP paradigmasını uygulayan literatürdeki ilk çalışma olmasıdır. Önerilen sistem Docker ile dağıtılmıştır ve REST API aracılığıyla restoran hizmetlerinin web tabanlı gerçek zamanlı izlenmesini desteklemektedir. Ayrıca, etmenlerin koordinasyonu için Blackboard tasarım modeli uygulanmıştır. Bunun yanı sıra, siparişler fiyat ve zamanlama gereksinimlerine göre öncelik puanlarına göre sıralanarak restoran karlılığı optimize edilmiştir.

Keywords

• JaCaMo
• Çoklu Etmen Odaklı Programlama Paradigması
• Çoklu Etmen Sistemi
• Restoran Yönetimi

References

1. Bordini, R. H., Braubach, L., Dastani, M., Seghrouchni, A. E. F., Gomez-Sanz, J. J., Leite, J., ... & Ricci, A. (2006). A survey of programming languages and platforms for multi-agent systems. Informatica, 30(1).
2. Pal, C. V., Leon, F., Paprzycki, M., & Ganzha, M. (2020). A review of platforms for the development of agent systems. arXiv preprint arXiv:2007.08961.
3. Wrona, Z., Buchwald, W., Ganzha, M., Paprzycki, M., Leon, F., Noor, N., & Pal, C. V. (2023). Overview of software agent platforms available in 2023. Information, 14(6), 348.
4. Boissier, O., Bordini, R. H., Hubner, J., & Ricci, A. (2020). Multi-agent oriented programming: programming multi-agent systems using JaCaMo. Mit Press.
5. Shoham, Y. (1993). Agent-oriented programming. Artificial intelligence, 60(1), 51-92.
6. Ricci, A., Piunti, M., & Viroli, M. (2011). Environment programming in multi-agent systems: an artifact-based perspective. Autonomous Agents and Multi-Agent Systems, 23(2), 158-192.
7. Pynadath, D. V., Tambe, M., Chauvat, N., & Cavedon, L. (1999, July). Toward team-oriented programming. In International Workshop on Agent Theories, Architectures, and Languages (pp. 233-247). Berlin, Heidelberg: Springer Berlin Heidelberg.
8. Huhns, M. N. (2001). Interaction-oriented software development. International Journal of Software Engineering and Knowledge Engineering, 11(03), 259-279.

9. Boissier, O., Bordini, R. H., Hübner, J. F., Ricci, A., & Santi, A. (2013). Multi-agent oriented programming with JaCaMo. Science of Computer Programming, 78(6), 747-761.
10. Bordini, R. H., Hübner, J. F., Wooldridge, M. J., & Wooldridge, M. J. (2007). Programming multi-agent systems in AgentSpeak using Jason (Vol. 8). Chichester, Hoboken: J. Wiley.
11. Ricci, A., Viroli, M., & Omicini, A. (2006, May). Carta go: A framework for prototyping artifact-based environments in mas. In International Workshop on Environments for Multi-Agent Systems (pp. 67-86). Berlin, Heidelberg: Springer Berlin Heidelberg.
12. Hubner, J. F., Sichman, J. S., & Boissier, O. (2007). Developing organised multiagent systems using the MOISE+ model: programming issues at the system and agent levels. International Journal of Agent-Oriented Software Engineering, 1(3-4), 370-395.
13. Khemakhem, F., Ellouzi, H., Ltifi, H., Ayed, M. B. (2022). Agent-based intelligent decision support systems: a systematic review. IEEE Transactions on Cognitive and Developmental Systems, 14(1), 20-34.
14. Cavada, J. P., Cortés, C. E., & Rey, P. A. (2017). A simulation approach to modelling baggage handling systems at an international airport. Simulation Modelling Practice and Theory, 75, 146-164.
15. Wang, S., Wan, J., Zhang, D., Li, D., & Zhang, C. (2016). Towards smart factory for industry 4.0: a self-organized multi-agent system with big data based feedback and coordination. Computer networks, 101, 158-168.
16. Ciloglugil, B., Alatli, O., Inceoglu, M. M., & Erdur, R. C. (2021, September). A multi-agent based adaptive e-learning system. In International Conference on Computational Science and Its Applications (pp. 693-707). Cham: Springer International Publishing.
17. Marik, V., Gorodetsky, V., Skobelev, P. (2020). Multi-Agent Technology for Industrial Applications: Barriers and Trends. In 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 1980-1987). IEEE Press.
18. Wooldridge, M., & Jennings, N. R. (1995). Intelligent agents: Theory and practice. The knowledge engineering review, 10(2), 115-152.
19. Boissier, O., Bordini, R. H., Hübner, J. F., & Ricci, A. (2019). Dimensions in programming multi-agent systems. The Knowledge Engineering Review, 34, e2.
20. Omicini, A., Ricci, A., & Viroli, M. (2008). Artifacts in the A&A meta-model for multi-agent systems. Autonomous agents and multi-agent systems, 17(3), 432-456.
21. Ricci, A., Piunti, M., Viroli, M., & Omicini, A. (2009). Environment programming in CArtAgO. In Multi-agent programming: Languages, tools and applications (pp. 259-288). Boston, MA: Springer US.
22. Boissier, O., Hübner, J. F., Ricci, R. K. M. P. A., & Sichman, J. S. (2009). The Moise Framework Organisation Oriented Programming of MAS. Framework.
23. Teixeira, B., Martinho, D., Novais, P., Corchado, J., & Marreiros, G. (2022, August). Diabetic-Friendly Multi-agent Recommendation System for Restaurants Based on Social Media Sentiment Analysis and Multi-criteria Decision Making. In EPIA Conference on Artificial Intelligence (pp. 361-373). Springer International Publishing.
24. Liu, L., Chen, S., Jin, H., Deng, X., Liu, Y., & Lin, Y. (2025). Optimizing on-demand food delivery with BDI-based multi-agent systems and Monte Carlo tree search scheduling. Scientific Reports, 15(1), 25083.
25. Xue, G., Wang, Z., & Wang, Y. (2024). The restaurant delivery problem with uncertain cooking time and travel time. Computers & Industrial Engineering, 190, 110039.
26. Tanizaki, T., & Shimmura, T. (2017). Modeling and analysis method of restaurant service process. Procedia CIRP, 62, 84-89.
27. Zhu, S., Ren, Y., Liu, M., Gong, L., Zhang, Y., & Liu, X. (2024, August). Multi-agent reinforcement learning-based real-time cooking task scheduling optimization for multi-chef collaborative cooking. In 2024 International Conference on Automation in Manufacturing, Transportation and Logistics (ICaMaL) (pp. 1-9). IEEE.
28. Ahamad, S. N. S., & Ghani, N. H. A. (2023). A binary integer programming model for personnel scheduling: a case study at fast-food restaurant in Johor. Journal of Advanced Research in Applied Sciences and Engineering Technology, 30(3), 334-347.
29. Kabala, K., Dziurzanski, P., & Konrad, A. (2025). Development of a reinforcement learning-based adaptive scheduling algorithm for commercial smart kitchens. Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska, 15(3), 117-122.
30. Sparnaaij, M., Yuan, Y., Daamen, W., & Duives, D. C. (2022). A novel activity choice and scheduling model to model activity schedules of customers and staff in Dutch restaurants. arXiv preprint arXiv:2204.06775.
31. Saveant, D., Davidenko, O., Maurice, A., Taillandier, P., & Darcel, N. (2025). Factors influencing meal composition in university restaurant settings: a multi-agent simulation approach. Available at SSRN 5276186.
32. Koide, T., & Kikuda, M. (2017, October). An analysis on the effect of speed seats for congestion mitigation in a campus restaurant using multi-agent simulation. In Proceedings of the World Congress on Engineering and Computer Science 2017 (pp. 25-27).
33. Koide, T., Kobayashi, T., & Kikuda, M. (2017, October). A multi-agent simulation study for congestion minigation in a university campus restaurant. In The World Congress on Engineering and Computer Science (pp. 99-109). Springer Singapore.
34. René, A. O. N., Tanizaki, T., Shimmura, T., Fujii, N., & Takenaka, T. (2019). Multi-agent simulation-based analysis for restaurant service. Procedia CIRP, 79, 673-678.
35. Lee, W., & Lu, L. (2025). Lined up? Examining a “waiting line” effect in technology-enabled restaurant menu ordering. Journal of Hospitality & Tourism Research, 49(3), 433-448.
36. Nurkamila, H. H., Sharone, J. R., Setyawati, T. I. B., Hanifah, Y., Nurcahyo, R., & Nurdini, A. (2024). Facility layout planning optimization in a family restaurant using systematic layout planning and ergonomic approach. IEOM Society International, 6.
37. Motowilowa, J., Vergari, M., Kojić, T., Warsinke, M., Möller, S., & Voigt-Antons, J. N. (2024, December). Exploring augmented table setup and lighting customization in a simulated restaurant to improve the user experience. In 2024 International Symposium on Multimedia (ISM) (pp. 227-231). IEEE.
38. Farias, A. R., Lane, H., Killingsworth, J., Warden, J. M., & Wais, S. (2023). Exploring the interplay of pro-environmental attitudes, dietary choices, and packaging preferences: A virtual reality restaurant scenario study. Challenges, 15(1), 1.
39. Bujalance-López, L., González-Serrano, L., Lechuga Sancho, M. P., & Talon-Ballestero, P. (2025). Restaurant revenue management: a systematic literature review and future challenges. British Food Journal, 127(6), 2169-2196.
40. Nii, H. P. (1986). The blackboard model of problem solving and the evolution of blackboard architectures. AI magazine, 7(2), 38-38.
41. Wang, X., Zhang, L., Lin, T., Zhao, C., Wang, K., & Chen, Z. (2022). Solving job scheduling problems in a resource preemption environment with multi-agent reinforcement learning. Robotics and Computer-Integrated Manufacturing, 77, 102324.
42. Turetken, O., Stojanov, I., & Trienekens, J. J. (2017). Assessing the adoption level of scaled agile development: a maturity model for Scaled Agile Framework. Journal of Software: Evolution and process, 29(6), e1796.