Abstract
The management of ship-generated oily waste is subject to international regulations due to its high environmental impact and significant valorisation potential. The advancement of research leads port authorities to think of emerging technologies to add value to existing systems. In light of this, the objective of this paper is to devise and simulate a collection system based on Internet of Things technology. It is primarily an intelligent simulator with capabilities of sensors’ imitation, relaying data, evaluating vehicle routing algorithms and calculation of performance indicators. Using a numerical experience adapted from the regional context in Morocco, the metrics about collected quantities, transportation distances and tank storage levels tend to prefer the intelligent scenario over the status quo. The total distance travelled has decreased by 45.25%, and the average quantity collected per round has risen by 24.22%. On average, every cubic meter stored in a port saves 16.4 km of monthly travelled distances. These results warrant additional study to evaluate the impact of a national coverage extent. Nevertheless, additional tests on investment requirements in terms of network implementation and storage resources are essential to prove the acquisition of this solution is viable in the long range.
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References
AbouRizk, S. M., Halpin, D. W., & Wilson, J. R. (1994). Fitting BETA distributions based on sample data. 120(2), 288–305. https://doi.org/10.1061/(asce)0733-9364(1994)120:2(288)
Adjadj, D., MAACHE, I., Akram, D. A., & Kahia, C. (2017). Optimisation de la gestion des stocks par la méthode de Wilson : étude de cas d’un dépôt de pièce de rechange. Costantine.
Ai-Sakran, A., Qattous, H., & Hijjawi, M. (2018). A proposed performance evaluation of NoSQL databases in the field of IoT. 2018 8th International Conference on Computer Science and Information Technology, CSIT 2018, 32–37. https://doi.org/10.1109/CSIT.2018.8486199
Amghar, S., Cherdal, S., & Mouline, S. (2018). Which NoSQL database for IoT applications? 2018 International Conference on Selected Topics in Mobile and Wireless Networking, MoWNeT 2018, 131–137. https://doi.org/10.1109/MoWNet.2018.8428922
Arango, C., Cortés, P., Muñuzuri, J., & Onieva, L. (2011). Berth allocation planning in Seville inland port by simulation and optimisation. Advanced Engineering Informatics, 25(3), 452–461. https://doi.org/10.1016/j.aei.2011.05.001
Balusamy, B., Abirami, N. R., Kadry, S., & Gandome, A. H. (2021). Introduction to NoSQL. In Big Data: Concepts, Technology, and Architecture (pp. 53–81). https://doi.org/10.1007/978-1-4842-2985-9_7
Bányai, T., Tamás, P., Illés, B., Stankevičiūtė, Ž., & Bányai, Á. (2019). Optimization of municipal waste collection routing: Impact of industry 4.0 technologies on environmental awareness and sustainability. International Journal of Environmental Research and Public Health, 16(4). https://doi.org/10.3390/ijerph16040634
Batsidis, A., Economou, P., & Tzavelas, G. (2016). Tests of fit for a lognormal distribution. Journal of Statistical Computation and Simulation, 86(2), 215–235. https://doi.org/10.1080/00949655.2014.1003138
Caldeira dos Santos, M., & Pereira, F. H. (2021). Development and application of a dynamic model for road port access and its impacts on port-city relationship indicators. Journal of Transport Geography, 96(September). https://doi.org/10.1016/j.jtrangeo.2021.103189
Chitu, C., & Song, H. (2019). Data analytics and processing platforms in CPS. Machine learning for the Internet of Things. Elsevier Inc. https://doi.org/10.1016/B978-0-12-816637-6.00001-4
Chu, F., Gailus, S., Liu, L., Ni, L., 2018. The future of automated ports. McKinsey & Company – Travel, Transport & Logistics. report of Mckinsey.
Coffield, D., & Shepherd, D. (1987). Tutorial guide to Unix sockets for network communications. Computer Communications, 10(1), 21–29. https://doi.org/10.1016/0140-3664(87)90311-2
Cordeau, J. F., Laporte, G., Savelsbergh, M. W. P., & Vigo, D. (2007). Vehicle routing. In Handbooks in operations research and management science (Vol. 14, Issue C, pp. 367–428). https://doi.org/10.1016/S0927-0507(06)14006-2
Deng, Y., Sheng, D., & Liu, B. (2021). Managing ship lock congestion in an inland waterway: A bottleneck model with a service time window. Transport Policy, 112(December 2020), 142–161. https://doi.org/10.1016/j.tranpol.2021.08.017
Enström, J., Eriksson, A., Eliasson, L., Larsson, A., & Olsson, L. (2021). Wood chip supply from forest to port of loading – A simulation study. Biomass and Bioenergy, 152(May). https://doi.org/10.1016/j.biombioe.2021.106182
Eyada, M. M., Saber, W., El Genidy, M. M., & Amer, F. (2020). Performance evaluation of IoT data management using MongoDB Versus MySQL databases in different cloud environments. IEEE Access, 8, 110656–110668. https://doi.org/10.1109/ACCESS.2020.3002164
Fahim, P. B. M., Rezaei, J., Montreuil, B., & Tavasszy, L. (2021). Port performance evaluation and selection in the Physical Internet. Transport Policy, April. https://doi.org/10.1016/j.tranpol.2021.07.013
Feng, M., Shaw, S. L., Peng, G., & Fang, Z. (2020). Time efficiency assessment of ship movements in maritime ports: A case study of two ports based on AIS data. Journal of Transport Geography, 86(November 2018). https://doi.org/10.1016/j.jtrangeo.2020.102741
Fransen, R. W., & Davydenko, I. Y. (2021). Empirical agent-based model simulation for the port nautical services: A case study for the Port of Rotterdam. In Maritime Transport Research (Vol. 2, p. 100040). https://doi.org/10.1016/j.martra.2021.100040
Ilie, A., Oprea, C., Olteanu, S., Dinu, O., & Ruscǎ, F. (2019). An heuristic model for port optimization. Procedia Manufacturing, 32, 975–982. https://doi.org/10.1016/j.promfg.2019.02.311
Issa, A., Hatiboglu, B., Bildstein, A., & Bauernhansl, T. (2018). Industrie 4.0 roadmap: Framework for digital transformation based on the concepts of capability maturity and alignment. Procedia CIRP, 72, 973–978. https://doi.org/10.1016/j.procir.2018.03.151
Jiang, L., Xu, L. D., Cai, H., Jiang, Z., Bu, F., & Xu, B. (2014). An IoT-oriented data storage framework in cloud computing platform. IEEE Transactions on Industrial Informatics, 10(2), 1443–1451. https://doi.org/10.1109/TII.2014.2306384
Jiang, M., Lu, J., Qu, Z., & Yang, Z. (2021). Port vulnerability assessment from a supply chain perspective. Ocean and Coastal Management, 213(September). https://doi.org/10.1016/j.ocecoaman.2021.105851
Klink, S., Sender, J., & Flügge, W. (2021). Simulation-based logistics planning for the optimization of ship occupancies. Procedia CIRP, 99, 45–49. https://doi.org/10.1016/j.procir.2021.03.008
Li, Z. C., Wang, M. R., & Fu, X. (2021). Strategic planning of inland river ports under different market structures: Coordinated vs. independent operating regime. Transportation Research Part E: Logistics and Transportation Review, 156(September), 1–33. https://doi.org/10.1016/j.tre.2021.102547
Lindqvist, D., Salman, M., & Bergqvist, R. (2020). A cost benefit model for high capacity transport in a comprehensive line-haul network. European Transport Research Review, 12(1). https://doi.org/10.1186/s12544-020-00451-5
Lun, Y. H. V., Lai, K.-H., & Cheng, T. C. E. (2010). Port operations. In Shipping and logistics management (pp. 179–191). https://doi.org/10.1007/978-1-84882-997-8_13
Maata, R. L., Cordova, R., Sudramurthy, B., & Halibas, A. (2018). Design and implementation of client-server based application using socket programming in a distributed computing environment. 2017 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2017, 1–4. https://doi.org/10.1109/ICCIC.2017.8524573
Nisbet, R., Elder, J., & Miner, G. (2009). The data mining process. Handbook of statistical analysis and data mining applications, 33–48. https://doi.org/10.1016/b978-0-12-374765-5.00003-6
Önsel Ekici, Ş., Kabak, Ö., & Ülengin, F. (2019). Improving logistics performance by reforming the pillars of Global Competitiveness Index. Transport Policy, 81(July 2018), 197–207. https://doi.org/10.1016/j.tranpol.2019.06.014
Ramos, T. R. P., de Morais, C. S., & Barbosa-Póvoa, A. P. (2018). The smart waste collection routing problem: Alternative operational management approaches. Expert Systems with Applications, 103, 146–158. https://doi.org/10.1016/j.eswa.2018.03.001
Rautmare, S., & Bhalerao, D. M. (2017). MySQL and NoSQL database comparison for IoT application. 2016 IEEE International Conference on Advances in Computer Applications, ICACA 2016, 235–238. https://doi.org/10.1109/ICACA.2016.7887957
Ross, S. M. (2009). Goodness of fit tests and categorical data analysis. Introduction to Probability and Statistics for Engineers and Scientists, 485–516. https://doi.org/10.1016/b978-0-12-370483-2.00016-3
Rossit, D. G., & Nesmachnow, S. (2022). Waste bins location problem: A review of recent advances in the storage stage of the Municipal Solid Waste reverse logistic chain. Journal of Cleaner Production, 342(January). https://doi.org/10.1016/j.jclepro.2022.130793
Ruiz-Aguilar, J. J., Turias, I. J., Cerbán, M., Jiménez-Come, M. J., González, M. J., & Pulido, A. (2016). Time analysis of the containerized cargo flow in the logistic chain using simulation tools: The case of the Port of Seville (Spain). Transportation Research Procedia, 18(June), 19–26. https://doi.org/10.1016/j.trpro.2016.12.003
Sarkar, B. D., & Shankar, R. (2021). Understanding the barriers of port logistics for effective operation in the Industry 4.0 era: Data-driven decision making. International Journal of Information Management Data Insights, 1(2), 100031. https://doi.org/10.1016/j.jjimei.2021.100031
Sawalha, S., & Al-Naymat, G. (2021). Towards an efficient big data management schema for IoT. Journal of King Saud University - Computer and Information Sciences. https://doi.org/10.1016/j.jksuci.2021.09.013
Si, H., Sun, C., Chen, B., Shi, L., & Qiao, H. (2019). Analysis of socket communication technology based on machine learning algorithms under TCP/IP protocol in network virtual laboratory system. IEEE Access, 7, 80453–80464. https://doi.org/10.1109/ACCESS.2019.2923052
Soeffker, N., Ulmer, M. W., & Mattfeld, D. C. (2022). Stochastic dynamic vehicle routing in the light of prescriptive analytics: A review. European Journal of Operational Research, 298(3), 801–820. https://doi.org/10.1016/j.ejor.2021.07.014
Stahlbock, R., & Voß, S. (2008). Operations research at container terminals: A literature update. Or Spectrum, 30(1), 1–52. https://doi.org/10.1007/s00291-007-0100-9
Steed, A., & Oliveira, M. F. (2010). Sockets and middleware. Networked Graphics, 195–216. https://doi.org/10.1016/b978-0-12-374423-4.00006-9
Sulewski, P. (2020). Recognizing distributions rather than goodness-of-fit testing. Communications in Statistics - Simulation and Computation. https://doi.org/10.1080/03610918.2020.1812647
Triska, Y., Frazzon, E. M., & Silva, V. M. D. (2020). Proposition of a simulation-based method for port capacity assessment and expansion planning. Simulation Modelling Practice and Theory, 103(April). https://doi.org/10.1016/j.simpat.2020.102098
van Hassel, E., Meersman, H., Van de Voorde, E., & Vanelslander, T. (2020). Impact of investing in new port capacity from a shipper and a shipowner perspective: The case of Maasvlakte II. Case Studies on Transport Policy, 8(4), 1170–1180. https://doi.org/10.1016/j.cstp.2020.07.015
Vidal, T., Laporte, G., & Matl, P. (2020). A concise guide to existing and emerging vehicle routing problem variants. European Journal of Operational Research, 286(2), 401–416. https://doi.org/10.1016/j.ejor.2019.10.010
Zhu, Y., Wu, W., & Li, D. (2016). Efficient client assignment for client-server systems. IEEE Transactions on Network and Service Management, 13(4), 835–847. https://doi.org/10.1109/TNSM.2016.2597269
Funding
This research work was supported by the Ministry of National Education, Vocational Training, Higher Education and Scientific Research (MENFPESRS, Morocco), the Digital Development Agency (ADD, Morocco) and National Centre for Scientific and Technical Research (CNRST, Morocco).
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B.A.: conceptualization, investigation, methodology, writing—original draft. M.S. and S.L.E.: project administration, funding acquisition, work supervision. H.E.-c., A.R., and A.M. helped shape the direction of the research. All authors have read and reviewed the manuscript.
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Abdellaoui, B., Ech-cheikh, H., Sadik, M. et al. Improvement of ship-generated oily waste collection process from ports through the use of virtual Internet of Things system. Environ Monit Assess 195, 896 (2023). https://doi.org/10.1007/s10661-023-11517-x
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DOI: https://doi.org/10.1007/s10661-023-11517-x