Intelligent routing and scheduling strategies for heterogeneous instant delivery services
Optimizing efficiency, customer satisfaction, and sustainability
DOI:
https://doi.org/10.35335/emod.v16i1.55Keywords:
Efficiency, Heterogeneous instant delivery services, Intelligent routing, Scheduling strategies, SustainabilityAbstract
Intelligent routing and scheduling strategies play a crucial role in optimizing efficiency, customer satisfaction, and sustainability in heterogeneous instant delivery services. This research focuses on developing a mathematical formulation and algorithm to address these challenges. The proposed model considers various factors, including delivery orders, vehicle capacities, time windows, and environmental impact, to minimize cost, delivery time, and emissions. The research also explores the integration of multi-objective optimization techniques to strike a balance between conflicting objectives. A numerical example is presented to illustrate the application of the mathematical formulation, showcasing the benefits of the proposed strategies in terms of efficient vehicle assignment, timely deliveries, and reduced environmental footprint. The findings highlight the potential for improving instant delivery services through intelligent routing and scheduling strategies, leading to enhanced operational efficiency, customer satisfaction, and sustainability. Further research is recommended to validate the proposed strategies in real-world scenarios and explore additional factors that may impact the routing and scheduling process in heterogeneous instant delivery services
References
Abbasi, M., & Nilsson, F. (2016). Developing environmentally sustainable logistics: Exploring themes and challenges from a logistics service providers’ perspective. Transportation Research Part D: Transport and Environment, 46, 273–283.
Aljohani, K., & Thompson, R. G. (2018). A stakeholder-based evaluation of the most suitable and sustainable delivery fleet for freight consolidation policies in the inner-city area. Sustainability, 11(1), 124.
Arinez, J. F., Chang, Q., Gao, R. X., Xu, C., & Zhang, J. (2020). Artificial intelligence in advanced manufacturing: Current status and future outlook. Journal of Manufacturing Science and Engineering, 142(11).
Atitallah, S. Ben, Driss, M., Boulila, W., & Ghézala, H. Ben. (2020). Leveraging Deep Learning and IoT big data analytics to support the smart cities development: Review and future directions. Computer Science Review, 38, 100303.
Bag, S., Wood, L. C., Mangla, S. K., & Luthra, S. (2020). Procurement 4.0 and its implications on business process performance in a circular economy. Resources, Conservation and Recycling, 152, 104502.
Baharmand, H., Comes, T., & Lauras, M. (2017). Managing in-country transportation risks in humanitarian supply chains by logistics service providers: Insights from the 2015 Nepal earthquake. International Journal of Disaster Risk Reduction, 24, 549–559.
Bodaghi, B., Shahparvari, S., Fadaki, M., Lau, K. H., Ekambaram, P., & Chhetri, P. (2020). Multi-resource scheduling and routing for emergency recovery operations. International Journal of Disaster Risk Reduction, 50, 101780.
Butrina, P., del Carmen Girón-Valderrama, G., Machado-León, J. L., Goodchild, A., & Ayyalasomayajula, P. C. (2017). From the last mile to the last 800 ft: key factors in urban pickup and delivery of goods. Transportation Research Record, 2609(1), 85–92.
Carofiglio, G., Morabito, G., Muscariello, L., Solis, I., & Varvello, M. (2013). From content delivery today to information centric networking. Computer Networks, 57(16), 3116–3127.
Chang, I.-C., Tai, H.-T., Yeh, F.-H., Hsieh, D.-L., & Chang, S.-H. (2013). A vanet-based a* route planning algorithm for travelling time-and energy-efficient gps navigation app. International Journal of Distributed Sensor Networks, 9(7), 794521.
Chase, C. W. (2013). Demand-driven forecasting: a structured approach to forecasting. John Wiley & Sons.
Chow, C. K. W. (2014). Customer satisfaction and service quality in the Chinese airline industry. Journal of Air Transport Management, 35, 102–107.
Chowdhury, M. M. H., & Quaddus, M. A. (2016). A multi-phased QFD based optimization approach to sustainable service design. International Journal of Production Economics, 171, 165–178.
Crawford, F., Watling, D. P., & Connors, R. D. (2017). A statistical method for estimating predictable differences between daily traffic flow profiles. Transportation Research Part B: Methodological, 95, 196–213.
Davis, J., Edgar, T., Porter, J., Bernaden, J., & Sarli, M. (2012). Smart manufacturing, manufacturing intelligence and demand-dynamic performance. Computers & Chemical Engineering, 47, 145–156.
de la Torre, R., Corlu, C. G., Faulin, J., Onggo, B. S., & Juan, A. A. (2021). Simulation, optimization, and machine learning in sustainable transportation systems: models and applications. Sustainability, 13(3), 1551.
Fan, H., Zhang, Y., Tian, P., Lv, Y., & Fan, H. (2021). Time-dependent multi-depot green vehicle routing problem with time windows considering temporal-spatial distance. Computers & Operations Research, 129, 105211.
Fazlollahtabar, H., & Saidi-Mehrabad, M. (2015). Methodologies to optimize automated guided vehicle scheduling and routing problems: a review study. Journal of Intelligent & Robotic Systems, 77, 525–545.
Ganji, M., Kazemipoor, H., Molana, S. M. H., & Sajadi, S. M. (2020). A green multi-objective integrated scheduling of production and distribution with heterogeneous fleet vehicle routing and time windows. Journal of Cleaner Production, 259, 120824.
Gyulai, D., Pfeiffer, A., Nick, G., Gallina, V., Sihn, W., & Monostori, L. (2018). Lead time prediction in a flow-shop environment with analytical and machine learning approaches. IFAC-PapersOnLine, 51(11), 1029–1034.
Handler, R. M., Shonnard, D. R., Lautala, P., Abbas, D., & Srivastava, A. (2014). Environmental impacts of roundwood supply chain options in Michigan: life-cycle assessment of harvest and transport stages. Journal of Cleaner Production, 76, 64–73.
Hofmann, E., & Rutschmann, E. (2018). Big data analytics and demand forecasting in supply chains: a conceptual analysis. The International Journal of Logistics Management, 29(2), 739–766.
Hsu, C.-C., Tan, K.-C., & Mohamad Zailani, S. H. (2016). Strategic orientations, sustainable supply chain initiatives, and reverse logistics: Empirical evidence from an emerging market. International Journal of Operations & Production Management, 36(1), 86–110.
Hussain, R., Al Nasser, A., & Hussain, Y. K. (2015). Service quality and customer satisfaction of a UAE-based airline: An empirical investigation. Journal of Air Transport Management, 42, 167–175.
Jie, Y. U., Subramanian, N., Ning, K., & Edwards, D. (2015). Product delivery service provider selection and customer satisfaction in the era of internet of things: A Chinese e-retailers’ perspective. International Journal of Production Economics, 159, 104–116.
Jin, Y., & Oriaku, N. (2013). E-service flexibility: meeting new customer demands online. Management Research Review, 36(11), 1123–1136.
Kazancoglu, Y., Kazancoglu, I., & Sagnak, M. (2018). A new holistic conceptual framework for green supply chain management performance assessment based on circular economy. Journal of Cleaner Production, 195, 1282–1299.
Kumar, R., & Chandrakar, R. (2012). Overview of green supply chain management: operation and environmental impact at different stages of the supply chain. International Journal of Engineering and Advanced Technology, 1(3), 1–6.
Lalani, B., Bechoff, A., & Bennett, B. (2019). Which choice of delivery model (s) works best to deliver fortified foods? Nutrients, 11(7), 1594.
Leyerer, M., Sonneberg, M.-O., Heumann, M., & Breitner, M. H. (2020). Shortening the last mile in urban areas: Optimizing a smart logistics concept for e-grocery operations. Smart Cities, 3(3), 585–603.
Li, J., Wang, F., & He, Y. (2020). Electric vehicle routing problem with battery swapping considering energy consumption and carbon emissions. Sustainability, 12(24), 10537.
Li, X., Wan, J., Dai, H.-N., Imran, M., Xia, M., & Celesti, A. (2019). A hybrid computing solution and resource scheduling strategy for edge computing in smart manufacturing. IEEE Transactions on Industrial Informatics, 15(7), 4225–4234.
Lin, J., Yu, W., Yang, X., Yang, Q., Fu, X., & Zhao, W. (2016). A real-time en-route route guidance decision scheme for transportation-based cyberphysical systems. IEEE Transactions on Vehicular Technology, 66(3), 2551–2566.
Liu, C., Kou, G., Zhou, X., Peng, Y., Sheng, H., & Alsaadi, F. E. (2020). Time-dependent vehicle routing problem with time windows of city logistics with a congestion avoidance approach. Knowledge-Based Systems, 188, 104813.
Luthra, S., & Mangla, S. K. (2018). Evaluating challenges to Industry 4.0 initiatives for supply chain sustainability in emerging economies. Process Safety and Environmental Protection, 117, 168–179.
Manavalan, E., & Jayakrishna, K. (2019). A review of Internet of Things (IoT) embedded sustainable supply chain for industry 4.0 requirements. Computers & Industrial Engineering, 127, 925–953.
Mansouri, S. A., Lee, H., & Aluko, O. (2015). Multi-objective decision support to enhance environmental sustainability in maritime shipping: A review and future directions. Transportation Research Part E: Logistics and Transportation Review, 78, 3–18.
Melo, S., Macedo, J., & Baptista, P. (2017). Guiding cities to pursue a smart mobility paradigm: An example from vehicle routing guidance and its traffic and operational effects. Research in Transportation Economics, 65, 24–33.
Mikalef, P., Krogstie, J., Pappas, I. O., & Pavlou, P. (2020). Exploring the relationship between big data analytics capability and competitive performance: The mediating roles of dynamic and operational capabilities. Information & Management, 57(2), 103169.
Moons, S., Ramaekers, K., Caris, A., & Arda, Y. (2017). Integrating production scheduling and vehicle routing decisions at the operational decision level: a review and discussion. Computers & Industrial Engineering, 104, 224–245.
Morganti, E., Seidel, S., Blanquart, C., Dablanc, L., & Lenz, B. (2014). The impact of e-commerce on final deliveries: alternative parcel delivery services in France and Germany. Transportation Research Procedia, 4, 178–190.
Nguyen, Q., Nisar, T. M., Knox, D., & Prabhakar, G. P. (2018). Understanding customer satisfaction in the UK quick service restaurant industry: The influence of the tangible attributes of perceived service quality. British Food Journal, 120(6), 1207–1222.
Orel, F. D., & Kara, A. (2014). Supermarket self-checkout service quality, customer satisfaction, and loyalty: Empirical evidence from an emerging market. Journal of Retailing and Consumer Services, 21(2), 118–129.
Panahi, F. H., Panahi, F. H., Hattab, G., Ohtsuki, T., & Cabric, D. (2018). Green heterogeneous networks via an intelligent sleep/wake-up mechanism and D2D communications. IEEE Transactions on Green Communications and Networking, 2(4), 915–931.
Parida, V., Sjödin, D., & Reim, W. (2019). Reviewing literature on digitalization, business model innovation, and sustainable industry: Past achievements and future promises. In Sustainability (Vol. 11, Issue 2, p. 391). MDPI.
Park, H., Haghani, A., Samuel, S., & Knodler, M. A. (2018). Real-time prediction and avoidance of secondary crashes under unexpected traffic congestion. Accident Analysis & Prevention, 112, 39–49.
Peng, L. S., & Moghavvemi, S. (2015). The dimension of service quality and its impact on customer satisfaction, trust, and loyalty: A case of Malaysian banks. Asian Journal of Business and Accounting, 8(2).
Reyes-Rubiano, L., Serrano-Hernandez, A., Montoya-Torres, J. R., & Faulin, J. (2021). The sustainability dimensions in intelligent urban transportation: a paradigm for smart cities. Sustainability, 13(19), 10653.
Rita, P., Oliveira, T., & Farisa, A. (2019). The impact of e-service quality and customer satisfaction on customer behavior in online shopping. Heliyon, 5(10), e02690.
Saarela, J. (2016). Optimising and automating work planning by approximating the vehicle routing problem.
Sarkis, J. (2020). Supply chain sustainability: learning from the COVID-19 pandemic. International Journal of Operations & Production Management, 41(1), 63–73.
Schweissguth, E., Danielis, P., Timmermann, D., Parzyjegla, H., & Mühl, G. (2017). ILP-based joint routing and scheduling for time-triggered networks. Proceedings of the 25th International Conference on Real-Time Networks and Systems, 8–17.
Seres, L., Pavlicevic, V., & Tumbas, P. (2018). Digital transformation of higher education: Competing on analytics. INTED2018 Proceedings, 9491–9497.
Silva, B. N., Khan, M., & Han, K. (2018). Towards sustainable smart cities: A review of trends, architectures, components, and open challenges in smart cities. Sustainable Cities and Society, 38, 697–713.
Sivakumar, K., Li, M., & Dong, B. (2014). Service quality: The impact of frequency, timing, proximity, and sequence of failures and delights. Journal of Marketing, 78(1), 41–58.
Sonneberg, M.-O., Leyerer, M., Kleinschmidt, A., Knigge, F., & Breitner, M. H. (2019). Autonomous unmanned ground vehicles for urban logistics: Optimization of last mile delivery operations.
Tong, L. C., Zhou, L., Liu, J., & Zhou, X. (2017). Customized bus service design for jointly optimizing passenger-to-vehicle assignment and vehicle routing. Transportation Research Part C: Emerging Technologies, 85, 451–475.
Uzir, M. U. H., Al Halbusi, H., Thurasamy, R., Hock, R. L. T., Aljaberi, M. A., Hasan, N., & Hamid, M. (2021). The effects of service quality, perceived value and trust in home delivery service personnel on customer satisfaction: Evidence from a developing country. Journal of Retailing and Consumer Services, 63, 102721.
Wang, Y., Kung, L., & Byrd, T. A. (2018). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change, 126, 3–13.
Wu, W., Tian, Y., & Jin, T. (2016). A label based ant colony algorithm for heterogeneous vehicle routing with mixed backhaul. Applied Soft Computing, 47, 224–234.
Xia, Y., Fu, Z., Pan, L., & Duan, F. (2018). Tabu search algorithm for the distance-constrained vehicle routing problem with split deliveries by order. PloS One, 13(5), e0195457.
Xiao, Y., Zhao, Q., Kaku, I., & Xu, Y. (2012). Development of a fuel consumption optimization model for the capacitated vehicle routing problem. Computers & Operations Research, 39(7), 1419–1431.
Zeadally, S., & Bello, O. (2021). Harnessing the power of Internet of Things based connectivity to improve healthcare. Internet of Things, 14, 100074.
Zhang, H., Ge, H., Yang, J., & Tong, Y. (2021). Review of vehicle routing problems: Models, classification and solving algorithms. Archives of Computational Methods in Engineering, 1–27.
Zhang, S., & Zhu, D. (2020). Towards artificial intelligence enabled 6G: State of the art, challenges, and opportunities. Computer Networks, 183, 107556.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Guo Wang Hou, Cen Zhou Fang
This work is licensed under a Creative Commons Attribution 4.0 International License.
