A Predictive Data Analytics Model for Enhancing Last-Mile Delivery Efficiency in Urban Logistics Networks

References

[1] [1]J. Andelmin and E. Bartolini, “An exact algorithm for the green vehicle routing problem,” Transportation Science, vol. 51, no. 4, pp. 1288–1303, Nov. 2017, doi: 10.1287/TRSC.2016.0734.

[2] [2]A. A. Rahmani Hosseinabadi, A. Slowik, M. Sadeghilalimi, M. Farokhzad, M. Babazadeh Shareh, and A. K. Sangaiah, “An Ameliorative Hybrid Algorithm for Solving the Capacitated Vehicle Routing Problem,” IEEE Access, vol. 7, pp. 175454–175465, 2019, doi: 10.1109/ACCESS.2019.2957722.

[3] [3]A. E. Gutierrez-Rodríguez, S. E. Conant-Pablos, J. C. Ortiz-Bayliss, and H. Terashima-Marín, “Selecting meta-heuristics for solving vehicle routing problems with time windows via meta-learning,” Expert Syst Appl, vol. 118, pp. 470–481, Mar. 2019, doi: 10.1016/J.ESWA.2018.10.036.

[4] [4]E. Jabir, V. V. Panicker, and R. Sridharan, “Design and development of a hybrid ant colony-variable neighbourhood search algorithm for a multi-depot green vehicle routing problem,” Transp Res D Transp Environ, vol. 57, pp. 422–457, Dec. 2017, doi: 10.1016/J.TRD.2017.09.003.

[5] [5]L. Barreto, A. Amaral, and T. Pereira, “Industry 4.0 implications in logistics: an overview,” Procedia Manuf, vol. 13, pp. 1245–1252, 2017, doi: 10.1016/j.promfg.2017.09.045.

[6] [6]C. F. Lynch, T. P. Stank, and S. Scott, “Logistics outsourcing,” Handbook of Global Supply Chain Management, pp. 373–392, Jan. 2007, doi: 10.4135/9781412976169.N22.

[7] [7]I. Sadler, “Logistics in Manufacturing Organisations,” Logistics and Supply Chain Integration, pp. 31–69, May 2012, doi: 10.4135/9781446214312.N2.

[8] [8]L. Costa, C. Contardo, and G. Desaulniers, “Exact branch-price-and-cut algorithms for vehicle routing,” Transportation Science, vol. 53, no. 4, pp. 946–985, 2019, doi: 10.1287/TRSC.2018.0878.

[9] [9]V. Ghilas, E. Demir, and T. Van Woensel, “An adaptive large neighborhood search heuristic for the Pickup and Delivery Problem with Time Windows and Scheduled Lines,” Comput Oper Res, vol. 72, pp. 12–30, Aug. 2016, doi: 10.1016/J.COR.2016.01.018.

[10] [10]J. Euchi, A. Yassine, and H. Chabchoub, “The dynamic vehicle routing problem: Solution with hybrid metaheuristic approach,” Swarm Evol Comput, vol. 21, pp. 41–53, Apr. 2015, doi: 10.1016/J.SWEVO.2014.12.003.

[11] [11]S. C. Liu, M. C. Lu, and C. H. Chung, “A hybrid heuristic method for the periodic inventory routing problem,” International Journal of Advanced Manufacturing Technology, vol. 85, no. 9–12, pp. 2345–2352, Aug. 2016, doi: 10.1007/S00170-015-8081-3.

[12] [12]P. Li et al., “Promoting secondary analysis of electronic medical records in china: Summary of the plagh-mit critical data conference and health datathon,” JMIR Med Inform, vol. 5, no. 4, Oct. 2017, doi: 10.2196/MEDINFORM.7380.

[13] [13]I. Eaton and M. McNett, “Protecting the data: Security and privacy,” Data for Nurses: Understanding and Using Data to Optimize Care Delivery in Hospitals and Health Systems, pp. 87–99, Jan. 2019, doi: 10.1016/B978-0-12-816543-0.00006-6.

[14] [14]J. Hemerly, “Public policy considerations for data-driven innovation,” Computer (Long Beach Calif), vol. 46, no. 6, pp. 25–31, 2013, doi: 10.1109/MC.2013.186.

[15] [15]E. W. K. See-To and E. W. T. Ngai, “Customer reviews for demand distribution and sales nowcasting: a big data approach,” Ann Oper Res, vol. 270, no. 1–2, pp. 415–431, Nov. 2018, doi: 10.1007/S10479-016-2296-Z.

[16] [16]G. Qin, F. Tao, and L. Li, “A vehicle routing optimization problem for cold chain logistics considering customer satisfaction and carbon emissions,” Int J Environ Res Public Health, vol. 16, no. 4, Feb. 2019, doi: 10.3390/IJERPH16040576.

[17] [17]J. Gualandris and M. Kalchschmidt, “Customer pressure and innovativeness: Their role in sustainable supply chain management,” Journal of Purchasing and Supply Management, vol. 20, no. 2, pp. 92–103, 2014, doi: 10.1016/j.pursup.2014.03.001.

[18] [18]W. Gu, D. Cattaruzza, M. Ogier, and F. Semet, “Adaptive large neighborhood search for the commodity constrained split delivery VRP,” Comput Oper Res, vol. 112, Dec. 2019, doi: 10.1016/J.COR.2019.07.019.

[19] [19]S. MULUKUNTLA, “Interoperability in Electronic Medical Records: Challenges and Solutions for Seamless Healthcare Delivery,” International Journal of Medical and Health Science, vol. 1, no. 1, pp. 31–38, Jan. 2015, doi: 10.53555/EIJMHS.V1I1.214.

[20] [20]I. Orenstein, T. Raviv, and E. Sadan, “Flexible parcel delivery to automated parcel lockers: models, solution methods and analysis,” EURO Journal on Transportation and Logistics, vol. 8, no. 5, pp. 683–711, Dec. 2019, doi: 10.1007/S13676-019-00144-7.

[21] [21]M. Avci and S. Topaloglu, “A hybrid metaheuristic algorithm for heterogeneous vehicle routing problem with simultaneous pickup and delivery,” Expert Syst Appl, vol. 53, pp. 160–171, Jul. 2016, doi: 10.1016/J.ESWA.2016.01.038.

[22] [22]G. Perboli, M. Rosano, M. Saint-Guillain, and P. Rizzo, “Simulation-optimisation framework for City Logistics: An application on multimodal last-mile delivery,” IET Intelligent Transport Systems, vol. 12, no. 4, pp. 262–269, May 2018, doi: 10.1049/IET-ITS.2017.0357.

[23] [23]J. Wang, Y. Zhou, Y. Wang, J. Zhang, C. L. P. Chen, and Z. Zheng, “Multiobjective Vehicle Routing Problems with Simultaneous Delivery and Pickup and Time Windows: Formulation, Instances, and Algorithms,” IEEE Trans Cybern, vol. 46, no. 3, pp. 582–594, Mar. 2016, doi: 10.1109/TCYB.2015.2409837.

[24] [24]D. J. Flint and B. Gammelgaard, “Value and customer service management,” Handbook of Global Supply Chain Management, pp. 51–64, Jan. 2007, doi: 10.4135/9781412976169.N4.

[25] [25]D. Weraikat, M. K. Zanjani, and N. Lehoux, “Two-echelon pharmaceutical reverse supply chain coordination with customers incentives,” Int J Prod Econ, vol. 176, pp. 41–52, Jun. 2016, doi: 10.1016/j.ijpe.2016.03.003.

[26] [26]A. Pienaar, “Integrated logistics management,” Handbook of Global Supply Chain Management, pp. 169–184, Jan. 2007, doi: 10.4135/9781412976169.N11.

[27] [27]I. Sadler, “International Logistics (with David Taylor),” Logistics and Supply Chain Integration, pp. 99–123, May 2012, doi: 10.4135/9781446214312.N4.

[28] [28]J. Yeung, X. Zhao, and K. Lam, “DCH Logistics – Planning for La Cafetière,” DCH Logistics – Planning for La Cafetière, Jan. 2019, doi: 10.4135/9781526462077.

[29] [29]M. Hesse, “Freight distribution centres, freight clusters and logistics parks,” The SAGE Handbook of Transport Studies, pp. 161–178, Jan. 2013, doi: 10.4135/9781446247655.N10.

[30] [30]D. Knoll, M. Prüglmeier, and G. Reinhart, “Predicting Future Inbound Logistics Processes Using Machine Learning,” Procedia CIRP, vol. 52, pp. 145–150, 2016, doi: 10.1016/J.PROCIR.2016.07.078.

[31] [31]T. Feng and H. J. P. Timmermans, “Detecting activity type from gps traces using spatial and temporal information,” European Journal of Transport and Infrastructure Research, vol. 15, no. 4, pp. 662–674, Sep. 2015, doi: 10.18757/EJTIR.2015.15.4.3103.

[32] [32]C. Liu, Z. Zhang, X. Su, and H. Qin, “A hybrid algorithm for the vehicle routing problem with compatibility constraints,” ICNSC 2018 - 15th IEEE International Conference on Networking, Sensing and Control, pp. 1–6, May 2018, doi: 10.1109/ICNSC.2018.8361368.

[33] [33]K. Liu, N. Li, I. Kolmanovsky, and A. Girard, “A vehicle routing problem with dynamic demands and restricted failures solved using stochastic predictive control,” Proceedings of the American Control Conference, vol. 2019-July, pp. 1885–1890, Jul. 2019, doi: 10.23919/ACC.2019.8814997.

[34] [34]K. J. Ferreira, B. H. A. Lee, and D. Simchi-Levi, “Analytics for an online retailer: Demand forecasting and price optimization,” Manufacturing and Service Operations Management, vol. 18, no. 1, pp. 69–88, Dec. 2016, doi: 10.1287/MSOM.2015.0561.

[35] [35]B. Baesens, R. Bapna, J. R. Marsden, J. Vanthienen, and J. L. Zhao, “Transformational Issues of Big Data and Analytics in Networked Business,” MIS Quarterly, vol. 40, no. 4, pp. 807–818, Apr. 2016, doi: 10.25300/MISQ/2016/40:4.03;

[36] [36]G. Wang, A. Gunasekaran, E. W. T. Ngai, and T. Papadopoulos, “Big data analytics in logistics and supply chain management: Certain investigations for research and applications,” Int J Prod Econ, vol. 176, pp. 98–110, Jun. 2016, doi: 10.1016/j.ijpe.2016.03.014.

[37] [37]A. Sammani et al., “UNRAVEL: big data analytics research data platform to improve care of patients with cardiomyopathies using routine electronic health records and standardised biobanking,” Netherlands Heart Journal, vol. 27, no. 9, pp. 426–434, Sep. 2019, doi: 10.1007/S12471-019-1288-4.

[38] [38]M. Bar-Sinai, L. Sweeney, and M. Crosas, “DataTags, Data Handling Policy Spaces and the Tags Language,” Proceedings - 2016 IEEE Symposium on Security and Privacy Workshops, SPW 2016, pp. 1–8, Aug. 2016, doi: 10.1109/SPW.2016.11.

[39] [39]S. Kumar, E. Dieveney, and A. Dieveney, “Reverse logistic process control measures for the pharmaceutical industry supply chain,” International Journal of Productivity and Performance Management, vol. 58, no. 2, pp. 188–204, 2009, doi: 10.1108/17410400910928761.

[40] [40]E. Hofmann and M. Rüsch, “Industry 4.0 and the current status as well as future prospects on logistics,” Comput Ind, vol. 89, pp. 23–34, Aug. 2017, doi: 10.1016/j.compind.2017.04.002.

[41] [41]E. Kongar, E. Haznedaroglu, O. Abdelghany, and M. O. Bahtiyar, “A novel IT infrastructure for reverse logistics operations of end-of-life pharmaceutical products,” Information Technology and Management, vol. 16, no. 1, pp. 51–65, Mar. 2015, doi: 10.1007/S10799-014-0195-Z.

[42] [42]R. Stake, “Multiple case study analysis,” 2006, The Guilford Press.

[43] [43]S. Elo and H. Kyngäs, “The qualitative content analysis process,” J Adv Nurs, vol. 62, no. 1, pp. 107–115, Apr. 2008, doi: 10.1111/j.1365-2648.2007.04569.x.

[44] [44]I. G. Kryvonos, I. V. Krak, O. V. Barmak, and A. I. Kulias, “Methods to Create Systems for the Analysis and Synthesis of Communicative Information,” Cybern Syst Anal, vol. 53, no. 6, pp. 847–856, Nov. 2017, doi: 10.1007/S10559-017-9986-7.

[45] [45]N. Lin, Y. Shi, T. Zhang, and X. Wang, “An Effective Order-Aware Hybrid Genetic Algorithm for Capacitated Vehicle Routing Problems in Internet of Things,” IEEE Access, vol. 7, pp. 86102–86114, 2019, doi: 10.1109/ACCESS.2019.2925831.

[46] [46]S. W. Lin, Z. J. Lee, K. C. Ying, and C. Y. Lee, “Applying hybrid meta-heuristics for capacitated vehicle routing problem,” Expert Syst Appl, vol. 36, no. 2 PART 1, pp. 1505–1512, 2009, doi: 10.1016/J.ESWA.2007.11.060.

[47] [47]T. Vidal, M. Battarra, A. Subramanian, and G. Erdoʇan, “Hybrid metaheuristics for the Clustered Vehicle Routing Problem,” Comput Oper Res, vol. 58, pp. 87–99, Aug. 2015, doi: 10.1016/J.COR.2014.10.019.

[48] [48]M. Kheirkhahzadeh and A. A. Barforoush, “A hybrid algorithm for the vehicle routing problem,” 2009 IEEE Congress on Evolutionary Computation, CEC 2009, pp. 1791–1798, 2009, doi: 10.1109/CEC.2009.4983158.

[49] [49]M. Mavrovouniotis and S. Yang, “Ant algorithms with immigrants schemes for the dynamic vehicle routing problem,” Inf Sci (N Y), vol. 294, pp. 456–477, Feb. 2015, doi: 10.1016/J.INS.2014.10.002.

[50] [50]R. K. Goel and S. R. Bansal, “Hybrid algorithms for rich vehicle routing problems: A survey,” Smart Delivery Systems: Solving Complex Vehicle Routing Problems, pp. 157–184, Jan. 2019, doi: 10.1016/B978-0-12-815715-2.00011-7.

[51] [51]P. M. Hartmann, M. Zaki, N. Feldmann, and A. Neely, “Capturing value from big data–a taxonomy of data-driven business models used by start-up firms,” International Journal of Operations & Production Management, vol. 36, no. 10, pp. 1382–1406, 2016, doi: 10.1108/ijopm-02-2014-0098.

[52] [52]M. Singh, S. Sachan, A. Singh, and K. K. Singh, “Internet of Things in pharma industry: Possibilities and challenges,” Emergence of Pharmaceutical Industry Growth with Industrial IoT Approach, pp. 195–216, Jan. 2019, doi: 10.1016/B978-0-12-819593-2.00007-8.

[53] [53]K. Witkowski, “Internet of Things, Big Data, Industry 4.0 - Innovative Solutions in Logistics and Supply Chains Management,” Procedia Eng, vol. 182, pp. 763–769, 2017, doi: 10.1016/j.proeng.2017.03.197.

[54] [54]C. Legner et al., “Digitalization: Opportunity and challenge for the business and information systems engineering community,” Business & Information Systems Engineering, vol. 59, no. 4, pp. 301–308, Aug. 2017, doi: 10.1007/s12599-017-0484-2.

[55] [55]N. Konduri et al., “Digital health technologies to support access to medicines and pharmaceutical services in the achievement of sustainable development goals,” Digit Health, vol. 4, p. 205520761877140, Jan. 2018, doi: 10.1177/2055207618771407.

[56] [56]J. Evans, S. McKemmish, and G. Rolan, “Participatory information governance: Transforming recordkeeping for childhood out-of-home Care,” Records Management Journal, vol. 29, no. 1–2, pp. 178–193, Mar. 2019, doi: 10.1108/RMJ-09-2018-0041/FULL/PDF.

[57] [57]S. Rosenbaum, “Data governance and stewardship: Designing data stewardship entities and advancing data access,” Health Serv Res, vol. 45, no. 5 PART 2, pp. 1442–1455, Oct. 2010, doi: 10.1111/J.1475-6773.2010.01140.X.

[58] [58]A. Sharma and P. Kaur, “A Multitenant Data Store Using a Column Based NoSQL Database,” 2019 12th International Conference on Contemporary Computing, IC3 2019, Aug. 2019, doi: 10.1109/IC3.2019.8844906.

[59] [59]S. Barocas and H. Nissenbaum, “Big data’s end run around procedural privacy protections,” Commun ACM, vol. 57, no. 11, pp. 31–33, Nov. 2014, doi: 10.1145/2668897.

[60] [60]S. Tiwari, H. M. Wee, and Y. Daryanto, “Big data analytics in supply chain management between 2010 and 2016: Insights to industries,” Comput Ind Eng, vol. 115, pp. 319–330, Jan. 2018, doi: 10.1016/j.cie.2017.11.017.

[61] [61]S. Purkayastha and J. Braa, “Big data analytics for developing countries-using the cloud for operational bi in health,” Electronic Journal of Information Systems in Developing Countries, vol. 59, no. 1, pp. 1–17, Oct. 2013, doi: 10.1002/J.1681-4835.2013.TB00420.X.

[62] [62]L. Belone et al., “Community-Based Participatory Research Conceptual Model: Community Partner Consultation and Face Validity,” Qual Health Res, vol. 26, no. 1, pp. 117–135, Jan. 2016, doi: 10.1177/1049732314557084.

[63] [63]G. Sanchez, “PLS Path Modeling with R,” 2013, Trowchez Editions.

[64] [64]A. Al-Shammari, R. Zhou, M. Naseriparsaa, and C. Liu, “An effective density-based clustering and dynamic maintenance framework for evolving medical data streams,” Int J Med Inform, vol. 126, pp. 176–186, Jun. 2019, doi: 10.1016/j.ijmedinf.2019.03.016.

[65] [65]E. S. Kirkendall, Y. Ni, T. Lingren, M. Leonard, E. S. Hall, and K. Melton, “Data challenges with real-time safety event detection and clinical decision support,” J Med Internet Res, vol. 21, no. 5, May 2019, doi: 10.2196/13047.

[66] [66]D. Classen, M. Li, S. Miller, and D. Ladner, “An electronic health record–based real-time analytics program for patient safety surveillance and improvement,” Health Aff, vol. 37, no. 11, pp. 1805–1812, Nov. 2018, doi: 10.1377/HLTHAFF.2018.0728;CTYPE:STRING:JOURNAL.

[67] [67]G. Kappes, A. Hatzieleftheriou, and S. V. Anastasiadis, “Multitenant Access Control for Cloud-Aware Distributed Filesystems,” IEEE Trans Dependable Secure Comput, vol. 16, no. 6, pp. 1070–1085, Nov. 2019, doi: 10.1109/TDSC.2017.2715839.

[68] [68]A. Hudic et al., “A multi-layer and multitenant cloud assurance evaluation methodology,” Proceedings of the International Conference on Cloud Computing Technology and Science, CloudCom, vol. 2015-February, no. February, pp. 386–393, Feb. 2015, doi: 10.1109/CLOUDCOM.2014.85.

[69] [69]L. Tawalbeh, R. Mehmood, E. Benkhlifa, and H. Song, “Cloud Computing Model and Big Data Analysis for Healthcare Applications,” IEEE Access, vol. 4, 2016.

[70] [70]M. Okulewicz and J. Mańdziuk, “A metaheuristic approach to solve Dynamic Vehicle Routing Problem in continuous search space,” Swarm Evol Comput, vol. 48, pp. 44–61, Aug. 2019, doi: 10.1016/J.SWEVO.2019.03.008.

[71] [71]D. Cattaruzza, N. Absi, and D. Feillet, “The multi-trip vehicle routing problem with time windows and release dates,” Transportation Science, vol. 50, no. 2, pp. 676–693, May 2016, doi: 10.1287/TRSC.2015.0608.

[72] [72]A. Kaushik and A. Raman, “The new data-driven enterprise architecture for e-healthcare: Lessons from the indian public sector,” Gov Inf Q, vol. 32, no. 1, pp. 63–74, 2015, doi: 10.1016/J.GIQ.2014.11.002.

[73] [73]V. Khatri and C. V. Brown, “Designing data governance,” Commun ACM, vol. 53, no. 1, pp. 148–152, Jan. 2010, doi: 10.1145/1629175.1629210.

[74] [74]S. Aral, E. Brynjolfsson, and M. Van Alstyne, “Information, technology and information worker productivity task level evidence,” Inf. Syst. Res., vol. 23, no. 3, part 2, pp. 849–867, 2012, doi: 10.1287/isre.1110.0408.

[75] [75]R. Bernardi, “Health information systems and accountability in Kenya: A structuration theory perspective,” J Assoc Inf Syst, vol. 18, no. 12, pp. 931–957, Dec. 2017, doi: 10.17705/1JAIS.00475.

[76] [76]L. Urquhart and T. Rodden, “New directions in information technology law: learning from human–computer interaction,” International Review of Law, Computers and Technology, vol. 31, no. 2, pp. 150–169, May 2017, doi: 10.1080/13600869.2017.1298501.

[77] [77]H. Zhang, Q. Zhang, L. Ma, Z. Zhang, and Y. Liu, “A hybrid ant colony optimization algorithm for a multi-objective vehicle routing problem with flexible time windows,” Inf Sci (N Y), vol. 490, pp. 166–190, Jul. 2019, doi: 10.1016/J.INS.2019.03.070.

[78] [78]X. Yan, B. Xiao, Y. Xiao, Z. Zhao, L. Ma, and N. Wang, “Skill Vehicle Routing Problem with Time Windows Considering Dynamic Service Times and Time-Skill-Dependent Costs,” IEEE Access, vol. 7, pp. 77208–77221, 2019, doi: 10.1109/ACCESS.2019.2919963.

[79] [79]V. Sivaram Kumar, M. R. Thansekhar, R. Saravanan, and S. Miruna Joe Amali, “Solving multi-objective vehicle routing problem with time windows by FAGA,” Procedia Eng, vol. 97, pp. 2176–2185, 2014, doi: 10.1016/J.PROENG.2014.12.461.

[80] [80]V. Sivaramkumar, M. R. Thansekhar, R. Saravanan, and S. Miruna Joe Amali, “Demonstrating the importance of using total time balance instead of route balance on a multi-objective vehicle routing problem with time windows,” International Journal of Advanced Manufacturing Technology, vol. 98, no. 5–8, pp. 1287–1306, Sep. 2018, doi: 10.1007/S00170-018-2346-6.

[81] [81]J. A. Burkell, “Remembering me: big data, individual identity, and the psychological necessity of forgetting,” Ethics Inf Technol, vol. 18, no. 1, pp. 17–23, Mar. 2016, doi: 10.1007/S10676-016-9393-1.

[82] [82]Y. Song, R. Routray, R. Jain, and C. H. Tan, “A data-driven storage recommendation service for multitenant storage management environments,” Proceedings of the 2015 IFIP/IEEE International Symposium on Integrated Network Management, IM 2015, pp. 1026–1040, Jun. 2015, doi: 10.1109/INM.2015.7140429.

[83] [83]J. P. A. Ioannidis, “Informed Consent, Big Data, and the Oxymoron of Research That Is Not Research,” American Journal of Bioethics, vol. 13, no. 4, pp. 40–42, Apr. 2013, doi: 10.1080/15265161.2013.768864.

[84] [84]B. Hofmann, “Broadening consent-and diluting ethics?,” J Med Ethics, vol. 35, no. 2, pp. 125–129, Feb. 2009, doi: 10.1136/JME.2008.024851.

[85] [85]B. D. Mittelstadt and L. Floridi, “The Ethics of Big Data: Current and Foreseeable Issues in Biomedical Contexts,” Sci Eng Ethics, vol. 22, no. 2, pp. 303–341, Apr. 2016, doi: 10.1007/S11948-015-9652-2.

[86] [86]S. A. Yawar and S. Seuring, “Management of Social Issues in Supply Chains: A Literature Review Exploring Social Issues, Actions and Performance Outcomes,” Journal of Business Ethics, vol. 141, no. 3, pp. 621–643, Mar. 2017, doi: 10.1007/S10551-015-2719-9.

[87] [87]G. Prause, “Sustainable business models and structures for industry 4.0,” Journal of Security and Sustainability Issues, vol. 5, no. 2, pp. 159–169, 2015, doi: 10.9770/JSSI.2015.5.2(3).

[88] [88]F. Caccioli, P. Barucca, and T. Kobayashi, “Network Models of Financial Systemic Risk: A Review,” SSRN Electronic Journal, Nov. 2017, doi: 10.2139/SSRN.3066722.

[89] [89]A. Hassanzadeh and M. Rasti-Barzoki, “Minimizing total resource consumption and total tardiness penalty in a resource allocation supply chain scheduling and vehicle routing problem,” Applied Soft Computing Journal, vol. 58, pp. 307–323, Sep. 2017, doi: 10.1016/J.ASOC.2017.05.010.

[90] [90]S. Wu, H. Legido-Quigley, J. Spencer, R. J. Coker, and M. S. Khan, “Designing evaluation studies to optimally inform policy: What factors do policy-makers in China consider when making resource allocation decisions on healthcare worker training programmes?,” Health Res Policy Syst, vol. 16, no. 1, Feb. 2018, doi: 10.1186/S12961-018-0292-2.

[91] [91]C. Barkman and L. Weinehall, “Policymakers and mHealth: Roles and expectations, with observations from Ethiopia, Ghana and Sweden,” Glob Health Action, vol. 10, 2017, doi: 10.1080/16549716.2017.1337356.

[92]  

@article{1709617,
      author = {Opeyemi Morenike Filani, John Oluwaseun Olajide, Grace Omotunde Osho, Patience Okpeke Paul},
      title = {A Predictive Data Analytics Model for Enhancing Last-Mile Delivery Efficiency in Urban Logistics Networks},
      journal = {Iconic Research And Engineering Journals},
      year = {2019},
      volume = {3},
      number = {3},
      pages = {181-192},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1709617.pdf},
      abstract = {Urban logistics networks have undergone rapid transformation due to increasing consumer demand, e-commerce expansion, and the growing complexity of last-mile delivery operations. Despite technological advancements, inefficiencies persist in the final leg of the delivery process, often leading to increased costs, environmental burdens, and diminished customer satisfaction. This paper proposes a Predictive Data Analytics Model (PDAM) to enhance last-mile delivery efficiency in urban logistics. By leveraging a literature-based methodology and analyzing over 100 peer-reviewed sources, the study synthesizes existing approaches in predictive analytics, logistics optimization, and urban freight systems. The model integrates real-time data, machine learning techniques, and geospatial intelligence to forecast delivery constraints, optimize routing, and improve service reliability. Structured into a detailed introduction, literature review, model design, discussion, and conclusion, this paper provides a strategic framework for logistics planners, policymakers, and technology implementers seeking to transform urban delivery ecosystems.},
      keywords = {last-mile delivery, predictive analytics, urban logistics, routing optimization, machine learning, delivery efficiency},
      month = {September},
  }