Software Agent Simulation Design on the Efficiency of Food Delivery

Shahrinaz Ismail - Albukhary International University, Kedah, Malaysia
Salama Mostafa - Universiti Tun Hussein Onn Malaysia, Johor, Malaysia
Zirawani Baharum - Malaysian Institute of Industrial Technology, Universiti Kuala Lumpur,Johor Bahru, Malaysia
Aldo Erianda - Politeknik Negeri Padang, Sumatera Barat, Indonesia
Mustafa Jaber - Al-Turath University, Baghdad, Iraq
Mohammed Jubair - College of Information Technology, Imam Ja'afar Al-Sadiq University, Al-Muthanna, Iraq
M. Adiya - Institut Bisnis dan Teknologi Pelita Pekanbaru, Pekanbaru, Indonesia

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Food delivery services have gained popularity since the emergence of online food delivery. Since the recent pandemic, the demand for service has increased tremendously. Due to several factors that affect how much time additional riders spend on the road; food delivery companies have no control over the location or timing of the delivery riders. There is a need to study and understand the food delivery riders' efficiency to estimate the service system's capacity. The study can ensure that the capacity is sufficient based on the number of orders, which usually depends on the number of potential customers within a territory and the time each rider takes to deliver the orders successfully. This study is an opportunity to focus on the efficiency of the riders since there is not much work at the operational level of the food delivery structure. This study takes up the opportunity to design a software agent simulation on the efficiency of riders' operations in food service due to the lack of simulation to predict this perspective, which could be extended to efficiency prediction. The results presented in this paper are based on the system design phase using the Tropos methodology. At movement in the simulation, the graph of the efficiency is calculated. Upon crossing the threshold, it is considered that the rider agents have achieved the efficiency rate required for decision-making. The simulation's primary operations depend on frontline remotely mobile workers like food delivery riders. It can benefit relevant organizations in decision-making during strategic capacity planning.


Food delivery efficiency; software agent simulation; system design; Tropos methodology

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A. F. Jola-Sanchez, A. J. Pedrazza-Martinez, K. M. Bretthauer and R. A. Britto. "Effect of armed conflicts on humanitarian operations: Total factor productivity and efficiency of rural hospitals", Journal of Operations Management, Vol. 45, pp. 73-85, 2016.

R. Md Zani and M. R. M. Khir, Operations Management, 1st ed., Ch. 10, pp. 223-259, 2016. Published by Oxford Fajar: Shah Alam, Malaysia.

R. R. Dholakia and M. Zhao, "Effects of online store attributes on customer satisfaction and repurchase intentions", International Journal of Retail & Distribution Management, Vol. 38, No. 7, pp. 482-496, 2010.

X. Liu, M. He, F. Gao and P. Xie. "An empirical study of online shopping customer satisfaction in China: a holistic perspective", International Journal of Retail & Distribution Management, Vol. 36, No. 11, pp. 919-940, 2008.

H. Sayadi, N. Patel, A. Sasan, H. Homayoun, "Machine Learning-based Approaches for Energy-Efficiency Prediction and Scheduling in Composite Cores Architectures", in Proceedings of IEEE 35th International Conference on Computer Design, pp. 129-136, 2017.

A. F. Mashaly and A.A. Alazba, "MLP and MLR models for instantaneous thermal efficiency prediction of solar still under hyper-arid environment", Computers and Electronics in Agriculture, Vol. 122, pp. 146-155, 2016.

M. Tomoskozi, P. Seeling, P. Ekler and F. H.P. Fitzek, "Regression Model Building and Efficiency Prediction of RoHCv2 Compressor Implementations for VoIP", in 2016 IEEE Global Communications Conference (GLOBECOM), pp. 1-6, 2016.

E. Zarei, I. Mohammadfam, M. M. Aliabadi, A. Jamshidi, and F. Ghasemia, "Efficiency Prediction of Control Room Operators Based on Human Reliability Analysis and Dynamic Decision-Making Style in the Process Industry", American Institute of Chemical Engineers, Vol. 35, No. 2, pp. 192-199, 2015.

A. Abdelaziz, M. Elhoseny, A. S. Salama and A.M. Riad, "A Machine Learning Model for Improving Healthcare services on Cloud Computing Environment", Measurement, Vol. 119, pp. 117-128, 2018.

S. A. Mostafa, M. S. Ahmad, A. Ahmad, M. Annamalai, and S. S. Gunasekaran, “A Flexible Human-Agent Interaction model for supervised autonomous systems,” In 2016 2nd International Symposium on Agent, Multi-Agent Systems and Robotics (ISAMSR) (pp. 106-111). IEEE, 2016.

C. M. Macal and M. J. North, "Tutorial on agent-based modelling and simulation", Journal of Simulation, Vol. 4, pp. 151–162, 2010. Published by Springer, Berlin, Heidelberg.

S. A. Mostafa, S. S. Gunasekaran, M. S. Ahmad, A. Ahmad, M. Annamalai, and A. Mustapha, “Defining tasks and actions complexity-levels via their deliberation intensity measures in the layered adjustable autonomy model,” In 2014 International Conference on Intelligent Environments (pp. 52-55). IEEE, 2014.

S. A. Mostafa, M. S. Ahmad, M. Annamalai, A. Ahmad, and S. S. Gunasekaran, “Formulating dynamic agents' operational state via situation awareness assessment,” In Advances in Intelligent Informatics (pp. 545-556). Springer, Cham, 2015

S. Olariu and A. Y. Zomaya, Handbook of Bioinspired Algorithms, p. 679, 2006, Published by Chapman & Hall/CRC: Florida, USA.

E. Bonabeau. "Agent-based modeling: Methods and techniques for simulating human systems", in Proceedings of the National Academy of Sciences (PNAS), 2002, pp. 7280-7287.

E. Bonabeau. In G. Ballot & G. Weisbuch (Eds.). Application of Simulation to Social Sciences, Hermès Sciences: Paris, 2000, pp. 451-461.

R.R. Brüngger, C. Kadar and I.P. Cvijikj, "Design of an agent-based model to predict crime (WIP)", in Proceedings of the Summer Computer Simulation Conference (SCSC '16), pp. 1-6, Article 55, 2016.

P. Bresciani, P. Giorgini, F. Giunchiglia, J. Mylopoulos and A. Perini, “Tropos: An agent-oriented software development methodology”, AAMAS Journal, pp. 203–236, Vol. 8, No. 3, 2004.

H.L. Zhang, C. Pang, X. Li, B. Shen and Y. Jiang "A Topological Description Language for Agent Networks", in Sheng Q.Z., Wang G., Jensen C.S., Xu G. (eds) Web Technologies and Applications. APWeb 2012, in Lecture Notes in Computer Science, pp. 759-766, Vol. 7235, 2012, Published by Springer, Berlin, Heidelberg.

S. Ismail and M.S. Ahmad, "A goal-based framework on contextual requirements modelling for agent-mediated continual quality improvement (aCQI) in curriculum design", in Proceedings of the 9th International Conference on Ubiquitous Information Management and Communication (IMCOM '15), pp. 1–8, Article 16, 2015, Published by Association for Computing Machinery, New York, NY, USA, DOI: 10.1145/2701126.2701178.

G. Zou, M. Gao, J. Tang, and L. Yilmaz. "Simulation of online food ordering delivery strategies using multi-agent system models." Journal of Simulation, Vol. 17, no. 3, pp.297-311, 2023.

G. Zou, J. Tang, L Yilmaz, and X. Kong. "Online food ordering delivery strategies based on deep reinforcement learning." Applied Intelligence, pp. 1-13, 2022.

G. Pezzotta, A. Rondini, F. Pirola, and R. Pinto. "Evaluation of discrete event simulation software to design and assess service delivery processes." Service Supply Chain Systems: A Systems Engineering Approach, vol. 8, no. 86, pp. 83-100, 2016.

W. J. Chin, M. J. C. E. Lim, A. A. M. Yong, Al-Talib, and K. H. Chaw. "Service time performance analysis of improved automated restaurant by layout reconfiguration and conveyor system." In IOP Conference Series: Materials Science and Engineering, vol. 692, no. 1, p. 012003. IOP Publishing, 2019.

E. Mangina, and I. P. Vlachos. "The changing role of information technology in food and beverage logistics management: beverage network optimisation using intelligent agent technology." Journal of food engineering, vol. 70, no. 3, pp. 403-420, 2005.

S. Vongbunyong, S. P. Tripathi, K. Thamrongaphichartkul, N. Worrasittichai, A. Takutruea, and T. Prayongrak. "Simulation of autonomous mobile robot system for food delivery in In-patient ward with unity." In 2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP), pp. 1-6. IEEE, 2020.

H. Fotouhi, N. Mori, E. Miller-Hooks, V. Sokolov, and S. Sahasrabudhe. “Assessing the effects of limited curbside pickup capacity in meal delivery operations for increased safety during a pandemic”, Transportation Research Record, vol. 2675, no. 5, pp. 436-452, 2021.

S. Abahussein, D. Ye, C. Zhu, Z. Cheng, U. Siddique, and S. Shen. "Multi-Agent Reinforcement Learning for Online Food Delivery with Location Privacy Preservation." Information, vol. 14, no. 11, p. 597, 2021.

G. I. Fragapane, C. Zhang, F. Sgarbossa, and J. O. Strandhagen. “An agent-based simulation approach to model hospital logistics”, International Journal of Simulation Modelling, vol. 18, no.4, pp. 654-665, 2019.

D. J. McClements. “The future of food colloids: Next-generation nanoparticle delivery systems,” Current Opinion in Colloid & Interface Science, vol. 28, pp. 7-14, 2017.