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Research Article

A heuristic for improving clustering in biomass supply chains

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Article: 2378859 | Received 27 Dec 2023, Accepted 07 Jul 2024, Published online: 17 Jul 2024

References

  • Abdussalam, O., Fello, N., & Chaabane, A. (2021). Exploring options for carbon abatement in the petroleum sector: A supply chain optimization-based approach. International Journal of Systems Science: Operations & Logistics, 10(1), 2005174. https://doi.org/10.1080/23302674.2021.2005174
  • Aloise, D., Hansen, P., & Liberti, L. (2012). An improved column generation algorithm for minimum sum of squares clustering. Mathematical Programming, 131, 195–220. https://doi.org/10.1007/s10107-010-0349-7
  • Arthur, D., & Vassilvitskii, S.. (2007). K-means++: The advantages of careful seeding. Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, SODA’07, 1027–1035.
  • Ayoub, N., Martins, R., Wang, K., Seki, H., & Naka, Y. (2007). Two levels decision system for efficient planning and implementation of bioenergy production. Energy Conversion and Management, 48(3), 709–723. https://doi.org/10.1016/j.enconman.2006.09.012
  • Bahmani, B., Moseley, B., Vattani, A., Kumar, R., & Vassilvitskii, S. (2012). Scalable κ-means++. Proceedings of the VLDB Endowment, 5(7), 622–633. https://doi.org/10.14778/2180912.2180915
  • Burgard, J. P., Costa, C. M., Hojny, C., Kleinert, T., & Schmidt, M. (2023). Mixed-integer programming techniques for the minimum sum of squares clustering problem. Journal of Global Optimization, 87, 133–189. https://doi.org/10.1007/s10898-022-01267-4
  • CERESiS. ContaminatEd land Remediation through Energy crops for Soil improvement to liquid biofuel Strategies. [Online] Retrieved December 17, 2023, from https://ceresis.eu/
  • Christou, I. T. (2011). Coordination of cluster ensembles via exact methods. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(2), 279–293. https://doi.org/10.1109/TPAMI.2010.85
  • Christou, I. T. (2012). Quantitative methods in supply chain management: Models and algorithms. London: Springer. https://doi.org/10.1007/978-0-85729-766-2
  • Derya, T., Keçeci, B., & Dinler, E. (2023). Selective clustered traveling salesman problem. International Journal of Systems Science: Operations & Logistics, 10(1). https://doi.org/10.1080/23302674.2023.2235266
  • European Commission. (2020). Long-term low greenhouse gas emission development strategy of the European Union and its Member States. UNFCCC, 2020 (March). [Online]. https://unfccc.int/documents/210328
  • European Parliament. (2021). The European Green Deal - European Parliament resolution of 15 January 2020 on the European Green Deal (2019/2956(RSP)). Official Journal of The European Union
  • Fattahi, M., & Govindan, K. (2018). A multi-stage stochastic program for the sustainable design of biofuel supply chain networks under biomass supply uncertainty and disruption risk: A real-life case study. Transportation Research Part E: Logistics and Transportation Review, 118, 534–567. https://doi.org/10.1016/j.tre.2018.08.008
  • Fisher, M.L., & Jaikumar, R. (1981). A generalized assignment heuristic for vehicle routing. Networks, 11(2), 109–124. https://doi.org/10.1002/net.3230110205
  • Grossmann, I. E. (2012). Advances in mathematical programming models for enterprise-wide optimization. Computers & Chemical Engineering, 47, 2–18. https://doi.org/10.1016/j.compchemeng.2012.06.038
  • Hansen, P., & Mladenović, N. (2001). J-Means: A new local search heuristic for minimum sum of squares clustering. Pattern Recognition, 34(2), 405–413. https://doi.org/10.1016/S0031-3203(99)00216-2
  • Hassan, S. S., Williams, G. A., & Jaiswal, A. K. (2019). Moving towards the second generation of lignocellulosic biorefineries in the EU: Drivers, challenges, and opportunities. Renewable and Sustainable Energy Reviews, 101, 590–599. https://doi.org/10.1016/j.rser.2018.11.041
  • IEA. (2017). Technology roadmap: Delivering sustainable bioenergy. IEA Paris. https://www.iea.org/reports/technology-roadmap-delivering-sustainable-bioenergy, Licence: CC BY 4.0
  • Laszlo, M., & Mukherjee, S. (2006). A genetic algorithm using hyper-quadtrees for low-dimensional k-means clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(4), 533–543. https://doi.org/10.1109/TPAMI.2006.66
  • Lozano-García, D. F., Santibañez-Aguilar, J. E., Lozano, F. J., & Flores-Tlacuahuac, A. (2020). GIS-based modeling of residual biomass availability for energy and production in Mexico. Renewable and Sustainable Energy Reviews, 120, 109610. https://doi.org/10.1016/j.rser.2019.109610
  • Martín, M., Taifouris, M., & Galán, G. (2023). Lignocellulosic biorefineries: A multiscale approach for resource exploitation. Bioresource Technology, 385, 129397. https://doi.org/10.1016/j.biortech.2023.129397
  • Momenitabar, M., Dehdari Ebrahimi, Z., Abdollahi, A., Helmi, W., Bengtson, K., & Ghasemi, P. (2023). An integrated machine learning and quantitative optimization method for designing sustainable bioethanol supply chain networks. Decision Analytics Journal, 7, 100236. https://doi.org/10.1016/j.dajour.2023.100236
  • O’Neill, E. G., Martinez-Feria, R. A., Basso, B., & Maravelias, C. T. (2022). Integrated spatially explicit landscape and cellulosic biofuel supply chain optimization under biomass yield uncertainty. Computers & Chemical Engineering, 160, 107724. https://doi.org/10.1016/j.compchemeng.2022.107724
  • Peng, J., & Xia, Y.. (2005). A cutting algorithm for the minimum sum-of-squared error clustering. Proceedings of the 2005 SIAM International Conference on Data Mining, 150–160.
  • Piccialli, V., Sudoso, A. M., & Wiegele, A. (2022). SOS-SDP: An exact solver for minimum sum of squares clustering. INFORMS Journal on Computing, 34(4), 2144–2162. https://doi.org/10.1287/ijoc.2022.1166
  • Potrč, S., Čuček, L., Martin, M., & Kravanja, Z. (2020). Synthesis of European Union biorefinery supply networks considering sustainability objectives. Processes, 8(12), 1588. https://doi.org/10.3390/pr8121588
  • Psathas, F., Georgiou, P. N., & Rentizelas, A. (2022). Optimizing the design of a biomass-to-biofuel supply chain network using a decentralized processing approach. Energies, 15(14). https://doi.org/10.3390/en15145001
  • Sosa, A., Acuna, M., McDonnell, K., & Devlin, G. (2015). Controlling moisture content and truck configurations to model and optimise biomass supply chain logistics in Ireland. Applied Energy, 137, 338–351. https://doi.org/10.1016/j.apenergy.2014.10.018
  • SVDLS. (2022). Scottish vacant and derelict land survey. Retrieved November 25, 2023, from https://www.gov.scot/publications/scottish-vacant-and-derelict-land-survey—site-register/
  • Theodoridis, S., & Koutroumbas, K. (2006). Pattern recognition (3rd ed.). Academic Press.
  • Tziolas, E., Manos, B., & Bournaris, T. (2017). Planning of agro-energy districts for optimum farm income and biomass energy from crops residues. Operational Research, 17(2), 535–546. https://doi.org/10.1007/s12351-016-0236-y
  • Uen, T. S., & Rodríguez, L. F. (2023). An integrated approach for sustainable food waste management towards renewable resource production and GHG reduction. Journal of Cleaner Production, 412, 137251. https://doi.org/10.1016/j.jclepro.2023.137251
  • Usman, M. (2023). An application of geospatial clustering for assets optimisation in forest harvesting [M.Sc. Thesis]. Tampere University.
  • Zamar, D. S., Bhushan, G., & Sokhansanj, S. (2017). A constrained K-means and nearest neighbor approach for route optimization in the bale collection problem. IFAC-PapersOnLine, 50(1), 12125–12130. https://doi.org/10.1016/j.ifacol.2017.08.2148
  • Zamar, D. S., Gopaluni, B., & Sokhansanj, S. (2017). Optimization of sawmill residues collection for bioenergy production. Applied Energy, 202, 487–495. https://doi.org/10.1016/j.apenergy.2017.05.156