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Dynamic collection scheduling using remote asset monitoring: A case study in the charity sector

McLeod, F, Erdogan, G, Cherrett, T, Bektas, T, Davies, N, Speed, C, Dickinson, J and Norgate, SH 'Dynamic collection scheduling using remote asset monitoring: A case study in the charity sector' , Transportation Research Record . (In Press)

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Abstract

In the waste collection sector, remote sensing technology is now coming onto the market, allowing waste and recycling receptacles to report their fill levels at regular intervals. This enables collection schedules to be dynamically optimised to better meet true servicing needs, so reducing transport costs and ensuring that visits to clients are made in a timely fashion. This paper describes a real-life logistics problem faced by a leading UK charity in servicing its textile and book donation banks and its High Street stores using a common fleet of vehicles with varying carrying capacities. This gives rise to a vehicle routing problem whereby visits to stores are on fixed days of the week, with time window constraints, and visits to banks (fitted with remote fill monitoring technology) are made in a timely fashion to avoid them becoming full before collection. A tabu search algorithm was developed to provide vehicles routes for the next day of operation, based on maximising profit. A longer look-ahead period was not considered on the basis that donation rates to banks are highly variable. The algorithm included parameters specifying the minimum fill level (e.g. 50%) required to allow a visit to a bank and a penalty function used to encourage visits to banks that are becoming full. The results showed that the algorithm significantly reduced visits to banks and increased profit by up to 2.4% with best performance obtained the more variable the donation rates.

Item Type: Article
Uncontrolled Keywords: waste smart charity logistics
Themes: Built and Human Environment
Energy
Health and Wellbeing
Media, Digital Technology and the Creative Economy
Schools: Schools > School of Health Sciences
Schools > School of Nursing, Midwifery, Social Work & Social Sciences > Centre for Nursing, Midwifery, Social Work & Social Sciences Research
Journal or Publication Title: Transportation Research Record
Publisher: National Academy of Sciences
Refereed: Yes
ISSN: 0361-1981
Related URLs:
Funders: Non funded research
Depositing User: SH Norgate
Date Deposited: 05 Apr 2013 13:12
Last Modified: 30 Nov 2015 23:51
References: 1. Smartbin. Remote monitoring of bin fill level. www.smartbin.com. Accessed July 26, 2012. 2. Straightsol. Strategies and measures for smarter urban freight solutions. http:// www.straightsol.eu/index.htm. Accessed July 26, 2012. 3. 6th Sense Transport. www.sixthsensetransport.com. Accessed July 26, 2012. 4. Golden, B., S. Raghavan, and E. Wasil. The vehicle routing problem: latest advances and new challenges. Springer, New York, 2008. 5. Laporte, G. Fifty Years of Vehicle Routing. Transportation Science, Vol. 43, 2009, pp. 408-416. 6. Baldacci, R. and A. Mingozzi. A unified exact method for solving different classes of vehicle routing problems. Mathematical Programming, Vol. 120, 2009, pp. 347-380. 7. De Rosa, B., G. Improta, G. Ghiani, and R. Musmanno. The arc routeing and scheduling problem with transshipment. Transportation Science, Vol. 36, No. 3, 2002, pp. 301-313. 8. Viotti, P., A. Polettini, R. Porni, and C. Innocenti. Genetic algorithms as a promising tool for optimization of the MSW collection routes. Waste Management and Research, Vol. 21, No. 4, 2003, pp. 292-298. 9. Bautista, J. and J. Pereira. Ant algorithms for urban waste collection routing. vol. 3172, Springer, Berlin, 2004. 10. Kim, B.-I., S. Kim, and S. Sahoo. Waste collection vehicle routing problem with time windows. Computers & Operations Research, Vol. 33, No. 12, 2006, pp. 3624-3642. 11. Nuortio, T., J. Kytöjoki, H. Niska, and O. Bräysy. Improved route planning and scheduling of waste collection and transport. Expert Systems with Applications, Vol. 30, No. 2, 2006, pp. 223-232. 12. Yaman, H. Formulations and valid inequalities for the Heterogeneous Vehicle Routing Problem. Mathematical Programming, Vol. 106, No. 2, 2006, pp. 365–390. 13. Brandão, J. A tabu search algorithm for the heterogeneous fixed fleet vehicle routing problem. Computers & Operations Research, Vol. 38, No. 1, 2011, pp. 140–151. 14. Paraskevopoulos, D., P. P. Repoussis, C. D. Tarantilis, G. Ioannou, and G. P. Prastacos. A reactive variable neighbourhood tabu search for the heterogeneous fleet vehicle routing problem with time windows. Journal of Heuristics, Vol. 14, No. 5, 2008, pp. 425–455. 15. Ceschia, S., L. Gaspero, and A. Schaerf. Tabu search techniques for the heterogeneous vehicle routing problem with time windows and carrier-dependent costs. Journal of Scheduling, Vol. 14, No. 6, 2011, pp. 601-615. 16. Johansson, O. M. The effect of dynamic scheduling and routing in a solid waste management system. Waste Management, Vol. 26, No. 8, 2006, pp. 875-885. 17. Krikke, H., I. le Blanc, M. van Krieken, and H. Fleuren. Low-frequency collection of materials disassembled from end-of-life vehicles: On the value of on-line monitoring in optimizing route planning International Journal of Production Economics, Vol. 111, No. 2, 2008, pp. 209-228. 18. Faccio, M., A. Persona, and G. Zanin. Waste collection multi objective model with real time traceability data. Waste Management, Vol. 31, No. 12, 2011, pp. 2391-2405. 19. Rovetta, A., F. Xiumin, F. Vicentini, Z. Minghua, A. Giusti, and H. Qichang. Early detection and evaluation of waste through sensorized containers for a collection monitoring application. Waste Management, Vol. 29, No. 12, 2009, pp. 2939-2949. 20. Waste Management World. Bin Monitoring Allows for 'Smart' Refuse Collection, http:// www.waste-management-world.com/index/display/article-display.articles.waste-management-world.collection-transport.2011.12.Bin_Monitoring_Allows_for__Smart__Refuse_Collection .QP129867.dcmp=rss.page=1.html. Accessed 12 July 2012. 21. Angelelli, E., N. Bianchessi, R. Mansini, and M. G. Speranza. Short term strategies for a dynamic multi-period routing problem. Transportation Research Part C: Emerging Technologies, Vol. 17, No. 2, 2009, pp. 106-119. 22. Ichoua, S., M. Gendreau, and J. Potvin. Diversion Issues in Real-Time Vehicle Dispatching. Transportation Science, Vol. 34, No. 4, 2000, pp. 426-438. 23. Khouadjia, M. R., B. Sarasola, E. Alba, L. Jourdan, and E. Talbi. A comparative study between dynamic adapted PSO and VNS for the vehicle routing problem with dynamic requests. Applied Soft Computing, Vol. 12, 2012, pp. 1426-1439. 24. Souffriau, W., Vansteenwegen, P., Vanden Berghe, G. and D. Van Oudheusden. The Multiconstraint Team Orienteering Problem with Multiple Time Windows. Transportation Science, published online before print, October 5, 2011, doi: 10.1287/trsc.1110.0377 25. Garey, M.R. and D.S. Johnson. Computers and Intractability: A Guide to the Theory of NP-Completeness. W.H. Freeman, New York, 1979. 26. Glover, F. Tabu Search - Part 1. ORSA Journal on Computing, Vol. 1, No. 2, 1989, pp. 190–206. 27. Glover, F. Tabu Search - Part 2. ORSA Journal on Computing Vol. 2, No. 1, 1990, pp. 4–32.
URI: http://usir.salford.ac.uk/id/eprint/28536

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