Application of The Grasshopper Optimization Algorithm for Route Optimization in Food Pickup-Delivery
Keywords: double order scheme, food pickup-delivery service, grasshopper optimization, route optimization
Abstract
Food pickup and delivery is one of the services provided by online transportation platforms, where couriers collect food orders from restaurants and deliver them to customers. As the demand for this service continues to increase, a larger number of couriers is required to meet customer requests. To address this challenge, a double-order scheme is introduced, allowing a single courier to simultaneously handle two orders destined for nearby customers. In this study, the double-order scheme is employed to optimize food delivery routes with the objectives of minimizing operational costs while maintaining food quality within the specified delivery time windows. This optimization problem is formulated as the Pickup and Delivery Problem with Time Windows (PDPTW). The Grasshopper Optimization Algorithm (GOA), a metaheuristic optimization method inspired by the foraging behavior of grasshoppers, is used to solve the problem. GOA simulates how grasshoppers search for food and communicate food locations to one another through a chemical signal known as the 4-VA pheromone. The proposed method was evaluated using simulation data consisting of 50 customer orders, 300 iterations, and a population of 10 grasshoppers. The results demonstrate that the proposed approach can reduce operational costs by up to 33.71% and decrease the number of active couriers by 50% compared with the conventional single-order delivery scheme.
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References
Anton, H., & Rorres, C. (2014). Elementary Linear Algebra, Applications Version (11th ed.). John Wiley & Sons, Inc.
Bermond, J.-C., & Bermond Hamiltonian, J.-C. (1979). Selected topics in graph theory (Issue 6). Academic Press. https://inria.hal.science/hal-02352666
Boro, S., & Behera, S. (2021). An Approach for Vehicle Routing Problem using Grasshopper Optimization Algorithm and Simulated Annealing. International Journal of Advanced Research, 9, 59–64. https://doi.org/10.21474/IJAR01/12554
Budiyanti, E. (2022). IMPLEMENTASI METODE HAVERSINE FORMULA PADA SISTEM INFORMASI GEOGRAFIS RUMAH KOST DAERAH TANGERANG SELATAN. LIAISON JOURNAL OF BEST, 1(1).
Burhan, H., Soehodho, S., & Nahry. (2021). Model Development of Ride Splitting Service with Resource Sharing Scheme on Ride Sourcing (Online Taxi) Services in Jakarta. IIUM Engineering Journal, 22(1), 175–190. https://doi.org/10.31436/IIUMEJ.V22I1.1520
Chartrand, Gary., & Zhang, Ping. (2012). A First Course in Graph Theory. Dover Publications.
Dantzig, G. B., & Ramser, J. H. (1959). The Truck Dispatching Problem. Management Science, 6(1), 80–91. http://remote-lib.ui.ac.id:2063/stable/2627477
Garside, A., & Cahyanti, D. (2018). Penyelesaian Vehicle Routing Problem with Simultaneous Pick Up and Delivery dengan Algoritma Tabu Search. Jurnal Ilmiah Teknik Industri, 17, 125. https://doi.org/10.23917/jiti.v17i2.6703
Gonzalez-Feliu, J. (2008). Models and Methods for the City Logistics: The Two-Echelon Capacitated Vehicle Routing Problem.
Google, Temasek, & Bain & Company. (2021). e-Conomy SEA 2021 Roaring 20s: The SEA Digital Decade.
Google, Temasek, & Bain & Company. (2024). e-Conomy SEA 2024 Profits On The Rise, Harnessing SEA’s Advantage.
Hermansyah, B. (2011). PENYELESAIAN VEHICLE ROUTING PROBLEM (VRP) MENGGUNAKAN ALGORITMA GENETIKA. Universitas Islam Negeri Sultan Syarif Kasim Riau.
Hillier, F. S., & Lieberman, G. J. (2015). Introduction to Operations Researc (10th ed.). Mc Graw Hill Education. www.solver.com/using-frontline-solvers-macintosh.
Kisanrao Nichat, M., RChopde, N., & Nichat, M. K. (2013). Landmark based shortest path detection by using A* Algorithm and Haversine Formula Landmark Based Shortest Path Detection by Using A* and Haversine Formula. In International Journal of Innovative Research in Computer and Communication Engineering (Vol. 1, Issue 2). www.ijircce.com
Kohar, A., & Jakhar, S. K. (2021). A capacitated multi pickup online food delivery problem with time windows: a branch-and-cut algorithm. Annals of Operations Research. https://doi.org/10.1007/s10479-021-04145-6
Liu, W.-Y., Lin, C.-C., Chiu, C.-R., Tsao, Y.-S., & Wang, Q. (2014). Minimizing the Carbon Footprint for the Time-Dependent Heterogeneous-Fleet Vehicle Routing Problem with Alternative Paths. Sustainability, 6(7), 4658–4684. https://doi.org/10.3390/su6074658
Lovani, S. M. (2024). Optimasi Rute Pesan-Antar Makanan yang Mempertimbangkan Kepuasan Pelanggan Menggunakan Metode Ant Colony Optimization (ACO) [Skripsi]. Universitas Indonesia.
Mahessar, S. Q., & Mahessar, A. A. (2020). The Locust Ravages & Food Security Challenges in Pakistan. Farming Outlook, 19(2), 13–17.
Meraihi, Y., Gabis, A. B., Mirjalili, S., & Ramdane-Cherif, A. (2021). Grasshopper Optimization Algorithm: Theory, Variants, and Applications. IEEE Access, 9, 50001–50024. https://doi.org/10.1109/ACCESS.2021.3067597
Montané, F. A. T., & Galvão, R. D. (2006). A tabu search algorithm for the vehicle routing problem with simultaneous pick-up and delivery service. Computers & Operations Research, 33(3), 595–619. https://doi.org/10.1016/J.COR.2004.07.009
Naomi, A., Pratiwi, A. B., & Suprajitno, H. (2022). Grasshopper Optimizaton Algorithm (GOA) untuk Menyelesaikan Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD). Tensor: Pure and Applied Mathematics Journal, 3(2), 73–84. https://doi.org/10.30598/tensorvol3iss2pp73-84
Parragh, S., Doerner, K., & Hartl, R. (2008). A survey on pickup and delivery problems: Part I: Transportation between customers and depot. Journal Für Betriebswirtschaft, 58, 21–51. https://doi.org/10.1007/s11301-008-0033-7
Putra Yusya, R. (2016). PERANCANGAN IKAT PINGGANG ELEKTRONIK UNTUK TUNANETRA MENGGUNAKAN MIKROKONTROLLER DAN GLOBAL POSITIONING SYSTEM (GPS) PADA SMARTPHONE ANDROID. Jurnal Teknik Elektro ITP, 5(2).
Rosen, K. H. (2012). Discrete Mathematics and Its Applications (7th ed.). McGraw-Hill.
Saremi, S., Mirjalili, S., & Lewis, A. (2017). Grasshopper Optimisation Algorithm: Theory and application. Advances in Engineering Software, 105, 30–47. https://doi.org/10.1016/J.ADVENGSOFT.2017.01.004
Soto-Mendoza, V., García, I., Ruiz, E., & Pérez-Terrazas, J. (2020). A Hybrid Grasshopper Optimization Algorithm Applied to the Open Vehicle Routing Problem. Algorithms, 13, 96. https://doi.org/10.3390/a13040096
Teng, R., Hong-bo, X., Kang-ning, J., Tian-yu, L., Ling, W., & Li-ning, X. (2021). Optimisation of takeaway delivery routes considering the mutual satisfactions of merchants and customers. Computers & Industrial Engineering, 162, 107728. https://doi.org/10.1016/J.CIE.2021.107728
Topaz, C., Bernoff, A., Logan, S., & Toolson, W. (2007). A model for rolling swarms of locusts. The European Physical Journal Special Topics, 157. https://doi.org/10.1140/epjst/e2008-00633-y
Toth, P., & Vigo, D. (2002). The Vehicle Routing Problem (P. Toth & D. Vigo, Eds.). Society for Industrial and Applied Mathematics. https://doi.org/10.1137/1.9780898718515
Winston, W. L. (2004). Operations Research: Applications and Algorithms (40th ed.). Thompson Brooks/Cole. www.duxbury.com
Yang, J., Yu, Q., Yu, J., Kang, L., & Guo, X. (2023). 4-Vinylanisole promotes conspecific interaction and acquisition of gregarious behavior in the migratory locust. Proceedings of the National Academy of Sciences, 120(37), e2306659120. https://doi.org/10.1073/pnas.2306659120
Zha, L., Yin, Y., & Xu, Z. (2018). Geometric matching and spatial pricing in ride-sourcing markets. Transportation Research Part C: Emerging Technologies, 92, 58–75. https://doi.org/https://doi.org/10.1016/j.trc.2018.04.015
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