Requirements from vehicle routing software: perspectives from literature, developers and the freight industry

References

• Ando, N., & Taniguchi, E. (2006). Travel time reliability in vehicle routing and scheduling with time windows. Networks and Spatial Economics, 6, 293–311. doi: 10.1007/s11067-006-9285-8
   Web of Science ®Google Scholar
• de Armas, J., & Melián-Batista, B. (2015). Variable neighborhood search for a dynamic rich vehicle routing problem with time windows. Computers & Industrial Engineering, 85, 120–131. doi: 10.1016/j.cie.2015.03.006
   Web of Science ®Google Scholar
• Baldacci, R., Mingozzi, A., & Roberti, R. (2011). New route relaxation and pricing strategies for the vehicle routing problem. Operations Research, 59(5), 1269–1283. doi: 10.1287/opre.1110.0975
   Web of Science ®Google Scholar
• Baldacci, R., Mingozzi, A., & Roberti, R. (2012). Recent exact algorithms for solving the vehicle routing problem under capacity and time window constraints. European Journal of Operational Research, 218(1), 1–6. doi: 10.1016/j.ejor.2011.07.037
   Web of Science ®Google Scholar
• Balseiro, S., Loiseau, I., & Ramonet, J. (2011). An ant colony algorithm hybridized with insertion heuristics for the time dependent vehicle routing problem with time windows. Computers & Operations Research, 38(6), 954–966. doi: 10.1016/j.cor.2010.10.011
   Web of Science ®Google Scholar
• Bektaş, T., & Laporte, G. (2011). The pollution-routing problem. Transportation Research Part B: Methodological, 45, 1232–1250. doi: 10.1016/j.trb.2011.02.004
   Web of Science ®Google Scholar
• Bozkaya, B., Yanik, S., & Balcisoy, S. (2010). A GIS-based optimization framework for competitive multi-facility location-routing problem. Networks and Spatial Economics, 10(3), 297–320. doi: 10.1007/s11067-009-9127-6
   Web of Science ®Google Scholar
• Bräysy, O., & Gendreau, M. (2005a). Vehicle routing problem with time windows, part I: Route construction and local search algorithms. Transportation Science, 39(1), 104–118. doi: 10.1287/trsc.1030.0056
   Web of Science ®Google Scholar
• Bräysy, O., & Gendreau, M. (2005b). Vehicle routing problem with time windows, part II: Metaheuristics. Transportation Science, 39(1), 119–139. doi: 10.1287/trsc.1030.0057
   Web of Science ®Google Scholar
• Bräysy, O., & Hasle, G. (2014). Software tools and emerging technologies for vehicle routing and intermodal transportation. Vehicle Routing: Problems, Methods, and Applications, 18, 351–380. doi: 10.1137/1.9781611973594.ch12
   Google Scholar
• Cattaruzza, D., Absi, N., Feillet, D., & González-Feliu, J. (2015). Vehicle routing problems for city logistics. EURO Journal on Transportation and Logistics, 1–29. doi:10.1007/s13676-014-0074-0
   Web of Science ®Google Scholar
• Cegarra, J., Gacias, B., & Lopez, P. (2012). Implications of technological changes in vehicle routing interfaces for planners’ constraint processing. Human Factors and Ergonomics in Manufacturing & Service Industries, 22(5), 468–480. doi: 10.1002/hfm.20367
   Web of Science ®Google Scholar
• Chang, Y. S., Lee, Y. J., & Choi, S. B. (2015). More traffic congestion in larger cities? – Scaling analysis of the large 101 US urban centers(March 25, 2015). Gachon GCCR Working Paper Series No.15-5. Available at SSRN: https://ssrn.com/abstract=2584878 or http://dx.doi.org/10.2139/ssrn.2584878.
   Google Scholar
• Cherrett, T., Allen, J., McLeod, F., Maynard, S., Hickford, A., & Browne, M. (2012). Understanding urban freight activity – key issues for freight planning. Journal of Transport Geography, 24, 22–32. doi: 10.1016/j.jtrangeo.2012.05.008
   Web of Science ®Google Scholar
• Cherrett, T., Shingleton, D., Norton, B., McLeod, F., Forey, C., Dickinson, J., … Norgate, S. (2015). Developing a smartphone app to enhance Oxfam’s supply chain visibility. International Journal of Logistics Research and Applications, 18(2), 155–167. doi: 10.1080/13675567.2014.980794
   Web of Science ®Google Scholar
• Christopher, M. (2010). New directions in logistics. In D. Waters (Ed.), Global logistics: New directions in supply chain management (pp. 1–11). London: KoganPage.
   Google Scholar
• Cohn, N. (2009). Real-time traffic information and navigation. Transportation Research Record: Journal of the Transportation Research Board, 2129(1), 129–135. doi: 10.3141/2129-15
   Web of Science ®Google Scholar
• Cordeau, J.-F., Gendreau, M., Laporte, G., Potvin, J.-Y., & Semet, F. (2002). A guide to vehicle routing heuristics. Journal of the Operational Research Society, 53(5), 512–522. doi: 10.1057/palgrave.jors.2601319
   Web of Science ®Google Scholar
• Crainic, T. G., Gendreau, M., & Potvin, J.-Y. (2009). Intelligent freight-transportation systems: Assessment and the contribution of operations research. Transportation Research Part C: Emerging Technologies, 17(6), 541–557. doi: 10.1016/j.trc.2008.07.002
   Web of Science ®Google Scholar
• Dabia, S., Ropke, S., Van Woensel, T., & De Kok, T. (2013). Branch and price for the time-dependent vehicle routing problem with time windows. Transportation Science, 47(3), 380–396. doi: 10.1287/trsc.1120.0445
   Web of Science ®Google Scholar
• Dantzig, G. B., & Ramser, J. H. (1959). The truck dispatching problem. Management Science, 6(1), 80–91. doi: 10.1287/mnsc.6.1.80
   Web of Science ®Google Scholar
• Davies, I., Mason, R., & Lalwani, C. (2007). Assessing the impact of ICT on UK general haulage companies. International Journal of Production Economics, 106(1), 12–27. doi: 10.1016/j.ijpe.2006.04.007
   Web of Science ®Google Scholar
• DFT. (2005). Computerised vehicle routing and scheduling (CVRS) for efficient logistics. Author.
   Google Scholar
• DFT. (2008). Transport statistics Great Britain. Author.
   Google Scholar
• DFT. (2010). Integrated research study: HGV satellite navigation and route planning. Author.
   Google Scholar
• Dion, F., & Rakha, H. (2006). Estimating dynamic roadway travel times using automatic vehicle identification data for low sampling rates. Transportation Research Part B: Methodological, 40(9), 745–766. doi: 10.1016/j.trb.2005.10.002
   Web of Science ®Google Scholar
• Donati, A. V., Montemanni, R., Casagrande, N., Rizzoli, A. E., & Gambardella, L. M. (2008). Time dependent vehicle routing problem with a multi ant colony system. European Journal of Operational Research, 185(3), 1174–1191. doi: 10.1016/j.ejor.2006.06.047
   Web of Science ®Google Scholar
• Drexl, M. (2012). Rich vehicle routing in theory and practice. Logistics Research, 5(1–2), 47–63. doi: 10.1007/s12159-012-0080-2
   Google Scholar
• Eglese, R., Maden, W., & Slater, A. (2006). A road timetable to aid vehicle routing and scheduling. Computers & Operations Research, 33(12), 3508–3519. doi: 10.1016/j.cor.2005.03.029
   Web of Science ®Google Scholar
• Ehmke, J. F., Steinert, A., & Mattfeld, D. C. (2012). Advanced routing for city logistics service providers based on time-dependent travel times. Journal of Computational Science, 3(4), 193–205. doi: 10.1016/j.jocs.2012.01.006
   Web of Science ®Google Scholar
• Erdoğan, G., McLeod, F., Cherrett, T., & Bektaş, T. (2015). Matheuristics for solving a multi-attribute collection problem for a charity organisation. Journal of the Operational Research Society, 66(2), 177–190. doi: 10.1057/jors.2013.172
   Web of Science ®Google Scholar
• European-Commission. (2011). Open data: An engine for innovation, growth and transparent governance. Retrieved from http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=COM:2011:0882:FIN:EN:PDF
   Google Scholar
• European-Commission. (2012). Road transport: A change of gear. Author.
   Google Scholar
• Figliozzi, M. A. (2008, May). An iterative route construction and improvement algorithm for the vehicle routing problem with soft and hard time windows. Paper presented at the applications of advanced technologies in transportation (AATT) 2008 conference proceedings, Athens, Greece.
   Google Scholar
• Figliozzi, M. A. (2012). The time dependent vehicle routing problem with time windows: Benchmark problems, an efficient solution algorithm, and solution characteristics. Transportation Research Part E: Logistics and Transportation Review, 48(3), 616–636. doi:10.1016/j.tre.2011.11.006
   Web of Science ®Google Scholar
• Fleischmann, B., Gietz, M., & Gnutzmann, S. (2004). Time-varying travel times in vehicle routing. Transportation Science, 38(2), 160–173. doi: 10.1287/trsc.1030.0062
   Web of Science ®Google Scholar
• FTA. (2002). The London Lorry Ban: Exempt route network study. Retrieved from http://www.fta.co.uk/export/sites/fta/_galleries/downloads/lorry_ban_case_study.pdf
   Google Scholar
• FTA. (2015). Logistics report 2015: Delivering safe, efficient, sustainable logistics. About. Tunbridge Wells: Author.
   Google Scholar
• Gehring, H., & Homberger, J. (1999). A parallel hybrid evolutionary metaheuristic for the vehicle routing problem with time windows. Paper presented at the proceedings of EUROGEN99.
   Google Scholar
• Gendreau, M., Ghiani, G., & Guerriero, E. (2015). Time-dependent routing problems: A review. Computers & Operations Research, 64, 189–197. doi: 10.1016/j.cor.2015.06.001
   Web of Science ®Google Scholar
• Gevaers, R., Van de Voorde, E., & Vanelslander, T. (2011). Characteristics and typology of last-mile logistics from an innovation perspective in an urban context. In City distribution and urban freight transport: Multiples perspectives (pp. 56–71). Cheltenham: Edward Elgar Publishing Ltd.
   Google Scholar
• Gibbs, N. (2014). The app that lets you improve Sat-Navs. The Telegraph. Retrieved from http://www.telegraph.co.uk/motoring/caraccessories/10909576/That-app-that-lets-you-improve-sat-navs.html
   Google Scholar
• Glover, F., & Kochenberger, G. A. (2003). Handbook of metaheuristics. Dordrecht: Springer.
   Google Scholar
• Goel, A. (2009). Vehicle scheduling and routing with drivers’ working hours. Transportation Science, 43(1), 17–26. doi: 10.1287/trsc.1070.0226
   Web of Science ®Google Scholar
• Goel, A., & Vidal, T. (2014). Hours of service regulations in road freight transport: An optimization-based international assessment. Transportation Science, 48(3), 391–412. doi: 10.1287/trsc.2013.0477
   Web of Science ®Google Scholar
• Golob, T. F., & Regan, A. C. (2001). Impacts of highway congestion on freight operations: Perceptions of trucking industry managers. Transportation Research Part A: Policy and Practice, 35(7), 577–599.
   Web of Science ®Google Scholar
• Gong, H., Chen, C., Bialostozky, E., & Lawson, C. T. (2012). A GPS/GIS method for travel mode detection in New York city. Computers, Environment and Urban Systems, 36(2), 131–139. doi: 10.1016/j.compenvurbsys.2011.05.003
   Web of Science ®Google Scholar
• Haghani, A., & Jung, S. (2005). A dynamic vehicle routing problem with time-dependent travel times. Computers & Operations Research, 32(11), 2959–2986. doi: 10.1016/j.cor.2004.04.013
   Web of Science ®Google Scholar
• Hallamaki, A., Hotokka, P., Brigatti, J., Nakari, P., Bräysy, O., & Ruohonen, T. (2007). Vehicle routing software: A survey and case studies with finish data. Technical Report.
   Google Scholar
• Harwood, K., Mumford, C., & Eglese, R. (2013). Investigating the use of metaheuristics for solving single vehicle routing problems with time-varying traversal costs. Journal of the Operational Research Society, 64, 34–47. doi: 10.1057/jors.2012.17
   Web of Science ®Google Scholar
• Herrera, J. C., Work, D. B., Herring, R., Ban, X. J., Jacobson, Q., & Bayen, A. M. (2010). Evaluation of traffic data obtained via GPS-enabled mobile phones: The mobile century field experiment. Transportation Research Part C: Emerging Technologies, 18(4), 568–583. doi: 10.1016/j.trc.2009.10.006
   Web of Science ®Google Scholar
• Ichoua, S., Gendreau, M., & Potvin, J.-Y. (2003). Vehicle dispatching with time-dependent travel times. European Journal of Operational Research, 144(2), 379–396. doi: 10.1016/S0377-2217(02)00147-9
   Web of Science ®Google Scholar
• Ioannou, G., Kritikos, M., & Prastacos, G. (2002). Map-route: A GIS-based decision support system for intra-city vehicle routing with time windows. Journal of the Operational Research Society, 53, 842–854. doi: 10.1057/palgrave.jors.2601375
   Web of Science ®Google Scholar
• Jarvenpaa, S. L., & Lang, K. R. (2005). Managing the paradoxes of mobile technology. Information Systems Management, 22(4), 7–23. doi: 10.1201/1078.10580530/45520.22.4.20050901/90026.2
   Web of Science ®Google Scholar
• Javelin-Group. (2011). How many stores will we really need? UK non-food retailing in 2020. White paper. London: Javelin Group. Retrieved from http://www.javelingroup.com/wp-content/uploads/2014/03/Javelin-Group_White_Paper_How_Many_Stores.pdf
   Google Scholar
• Jensen, A., & Dahl, S. (2009). Truck drivers hours-of-service regulations and occupational health. Work: A Journal of Prevention, Assessment and Rehabilitation, 33(3), 363–368.
   Web of Science ®Google Scholar
• Jung, H., Lee, K., & Chun, W. (2006). Integration of GIS, GPS, and optimization technologies for the effective control of parcel delivery service. Computers & Industrial Engineering, 51(1), 154–162. doi: 10.1016/j.cie.2006.07.007
   Web of Science ®Google Scholar
• Kallehauge, B., Larsen, J., & Madsen, O. B. (2006). Lagrangian duality applied to the vehicle routing problem with time windows. Computers & Operations Research, 33(5), 1464–1487. doi: 10.1016/j.cor.2004.11.002
   Web of Science ®Google Scholar
• Keenan, P. B. (1998). Spatial decision support systems for vehicle routing. Decision Support Systems, 22(1), 65–71. doi: 10.1016/S0167-9236(97)00054-7
   Web of Science ®Google Scholar
• Khattak, A. J., Targa, F., & Yim, Y. (2004). Advanced traveler information systems assessing the benefits and costs of ITS (pp. 217–240). Boston: Springer.
   Google Scholar
• Koç, Ç, Bektaş, T., Jabali, O., & Laporte, G. (2014). The fleet size and mix pollution-routing problem. Transportation Research Part B: Methodological, 70, 239–254. doi: 10.1016/j.trb.2014.09.008
   Web of Science ®Google Scholar
• Kok, A., Hans, E., & Schutten, J. (2012). Vehicle routing under time-dependent travel times: The impact of congestion avoidance. Computers & Operations Research, 39(5), 910–918. doi: 10.1016/j.cor.2011.05.027
   Web of Science ®Google Scholar
• Kok, A., Hans, E., Schutten, J., & Zijm, W. (2010). A dynamic programming heuristic for vehicle routing with time-dependent travel times and required breaks. Flexible Services and Manufacturing Journal, 22(1–2), 83–108. doi: 10.1007/s10696-011-9077-4
   Web of Science ®Google Scholar
• Laporte, G. (2009). Fifty years of vehicle routing. Transportation Science, 43(4), 408–416. doi: 10.1287/trsc.1090.0301
   Web of Science ®Google Scholar
• Laurila, J. K., Gatica-Perez, D., Aad, I., Bornet, O., Do, T.-M.-T., Dousse, O., … Miettinen, M. (2012). The mobile data challenge: Big data for mobile computing research. In Proceedings of the Workshop on the Nokia Mobile Data Challenge, in Conjunction with the 10th International Conference on Pervasive Computing, pp. 1–8.
   Google Scholar
• Lin, C., Choy, K. L., Ho, G. T. S., Chung, S. H., & Lam, H. Y. (2014). Survey of green vehicle routing problem: Past and future trends. Expert Systems with Applications, 41, 1118–1138. doi: 10.1016/j.eswa.2013.07.107
   Web of Science ®Google Scholar
• Maden, W., Eglese, R., & Black, D. (2010). Vehicle routing and scheduling with time-varying data: A case study. Journal of the Operational Research Society, 61(3), 515–522. doi: 10.1057/jors.2009.116
   Web of Science ®Google Scholar
• Malandraki, C. (1989). Time dependent vehicle routing problems: Formulations, solution algorithms and computational experiments. Thesis (Ph.D.). Northwestern University.
   Google Scholar
• Malandraki, C., & Daskin, M. S. (1992). Time dependent vehicle routing problems: Formulations, properties and heuristic algorithms. Transportation Science, 26(3), 185–200. doi: 10.1287/trsc.26.3.185
   Web of Science ®Google Scholar
• Marfia, G., Roccetti, M., & Amoroso, A. (2013). A new traffic congestion prediction model for advanced traveler information and management systems. Wireless Communications and Mobile Computing, 13(3), 266–276. doi: 10.1002/wcm.2200
   Web of Science ®Google Scholar
• Montemanni, R., Gambardella, L. M., Rizzoli, A. E., & Donati, A. V. (2005). Ant colony system for a dynamic vehicle routing problem. Journal of Combinatorial Optimization, 10(4), 327–343. doi: 10.1007/s10878-005-4922-6
   Web of Science ®Google Scholar
• OR/MS-Today. (2010). Vehicle routing software survey. Retrieved from http://www.lionhrtpub.com/orms/surveys/Vehicle_Routing/vrss.html
   Google Scholar
• OR/MS-Today. (2014). Vehicle routing software survey. Retrieved from http://www.orms-today.org/surveys/Vehicle_Routing/vrss.html
   Google Scholar
• OR/MS-Today. (2016). Vehicle routing survey. Retrieved from http://www.paragonrouting.com/uk/enewsletter/paragon-tachomaster-link
   Google Scholar
• Paragon. (2009). Paragon/tachomaster link. Retrieved from http://www.paragonrouting.com/uk/enewsletter/paragon-tachomaster-link
   Google Scholar
• Parragh, S. N., Doerner, K. F., & Hartl, R. F. (2008). A survey on pickup and delivery problems. Journal für Betriebswirtschaft, 58(1), 21–51. doi: 10.1007/s11301-008-0033-7
   Google Scholar
• Pillac, V., Gendreau, M., Guéret, C., & Medaglia, A. (2013). A review of dynamic vehicle routing problems. European Journal of Operational Research, 225, 1–11. doi: 10.1016/j.ejor.2012.08.015
   Web of Science ®Google Scholar
• Pisinger, D., & Ropke, S. (2007). A general heuristic for vehicle routing problems. Computers & Operations Research, 34(8), 2403–2435. doi: 10.1016/j.cor.2005.09.012
   Web of Science ®Google Scholar
• Ploskas, N., Ioannis, A., Jason, P., & Nikolaos, S. (2015). A collaborative spatial decision support system for the capacitated vehicle routing problem on a tabletop display. In I. Linden, S. Liu, F. E. Dargam, & J. Hernández (Eds.), Decision support systems IV-information and knowledge management in decision processes (pp. 26–36). Cham: Springer International.
   Google Scholar
• Prescott-Gagnon, E., Desaulniers, G., Drexl, M., & Rousseau, L.-M. (2010). European driver rules in vehicle routing with time windows. Transportation Science, 44(4), 455–473. doi: 10.1287/trsc.1100.0328
   Web of Science ®Google Scholar
• Rincon-Garcia, N., Velazquez, A., Waterson, B., & Cherrett, T. (2015). Improved algorithms for routing urban delivery vehicles. Paper presented at the logistics research network, Derby.
   Google Scholar
• Rincon-Garcia, N., Waterson, B., & Cherrett, T. (2016). A hybrid metaheuristic for the time-dependent vehicle routing problem with hard time windows. International Journal of Industrial Engineering Computations [ Online preview]. Retrieved from http://www.growingscience.com/ijiec/IJIEC_2016_18.pdf
   Web of Science ®Google Scholar
• Rochat, Y., & Semet, F. (1994). A Tabu search approach for delivering pet food and flour in Switzerland. Journal of the Operational Research Society, 45, 1233–1246. doi: 10.1057/jors.1994.197
   Web of Science ®Google Scholar
• Rowell, M. (2012). Improving statewide freight routing capabilities for sub-national commodity flows. Washington DC: University of Washington.
   Google Scholar
• Sadeghi-Niaraki, A., Varshosaz, M., Kim, K., & Jung, J. J. (2011). Real world representation of a road network for route planning in GIS. Expert Systems with Applications, 38(10), 11999–12008. doi: 10.1016/j.eswa.2010.12.123
   Web of Science ®Google Scholar
• Shim, J. P., Warkentin, M., Courtney, J. F., Power, D. J., Sharda, R., & Carlsson, C. (2002). Past, present, and future of decision support technology. Decision Support Systems, 33(2), 111–126. doi: 10.1016/S0167-9236(01)00139-7
   Web of Science ®Google Scholar
• Shrestha, B. P., Hounsell, N. B., McDonald, M., Cre, I., McDonald, R., & Beasley, S. (2014). Transferability and implementation issues of open data for application-based traveller information. 10th ITS European Congress, Helsinki, FI, 16–19 June, p. 9.
   Google Scholar
• Skabardonis, A., Varaiya, P., & Petty, K. F. (2003). Measuring recurrent and nonrecurrent traffic congestion. Transportation Research Record: Journal of the Transportation Research Board, 1856(1), 118–124. doi: 10.3141/1856-12
   Web of Science ®Google Scholar
• Solomon, M. M. (1987). Algorithms for the vehicle routing and scheduling problems with time window constraints. Operations Research, 35(2), 254–265. doi: 10.1287/opre.35.2.254
   Web of Science ®Google Scholar
• Sörensen, K. (2015). Metaheuristics-the metaphor exposed. International Transactions in Operational Research, 22, 3–18. doi: 10.1111/itor.12001
   Web of Science ®Google Scholar
• Sörensen, K., Sevaux, M., & Schittekat, P. (2008). Multiple neighbourhood. Search in commercial VRP packages: Evolving towards self-adaptive methods. In Adaptive and multilevel metaheuristics (pp. 239–253). Heidelberg: Springer.
   Google Scholar
• Soriguera, F., & Robusté, F. (2011). Requiem for freeway travel time estimation methods based on blind speed interpolations between point measurements. IEEE Transactions on Intelligent Transportation Systems, 12(1), 291–297. doi: 10.1109/TITS.2010.2095007
   Web of Science ®Google Scholar
• Taillard, ÉD. (1999). A heuristic column generation method for the heterogeneous fleet VRP. RAIRO – Operations Research, 33(01), 1–14. doi: 10.1051/ro:1999101
   Web of Science ®Google Scholar
• Tarantilis, C. D., & Kiranoudis, C. T. (2002). Using a spatial decision support system for solving the vehicle routing problem. Information & Management, 39(5), 359–375. doi: 10.1016/S0378-7206(01)00103-3
   Web of Science ®Google Scholar
• Toth, P., & Vigo, D. (2001). The vehicle routing problem. Philadelphia: Siam.
   Google Scholar
• UPS. (2015). ORION Backgrounder. Retrieved from https://www.pressroom.ups.com/pressroom/ContentDetailsViewer.page?ConceptType=Factsheets&id=1426321616277-282
   Google Scholar
• US-Department-Of-Transport. (2003). Final report – Traffic congestion and reliability: Linking solutions to problems. Retrieved from http://www.ops.fhwa.dot.gov/congestion_report_04/chapter3.htm
   Google Scholar
• Vidal, T., Crainic, T. G., Gendreau, M., & Prins, C. (2013). A hybrid genetic algorithm with adaptive diversity management for a large class of vehicle routing problems with time-windows. Computers & Operations Research, 40(1), 475–489. doi: 10.1016/j.cor.2012.07.018
   Web of Science ®Google Scholar
• Vidal, T., Crainic, T. G., Gendreau, M., & Prins, C. (2014). A unified solution framework for multi-attribute vehicle routing problems. European Journal of Operational Research, 234(3), 658–673. doi: 10.1016/j.ejor.2013.09.045
   Web of Science ®Google Scholar
• Visser, J., Nemoto, T., & Browne, M. (2014). Home delivery and the impacts on urban freight transport: A review. Procedia – Social and Behavioral Sciences, 125, 15–27. doi: 10.1016/j.sbspro.2014.01.1452
   Google Scholar
• Weigel, D., & Cao, B. (1999). Applying GIS and OR techniques to solve sears technician-dispatching and home delivery problems. Interfaces, 29(1), 112–130. doi: 10.1287/inte.29.1.112
   Web of Science ®Google Scholar
• Yu, B., & Yang, Z. Z. (2011). An ant colony optimization model: The period vehicle routing problem with time windows. Transportation Research Part E: Logistics and Transportation Review, 47(2), 166–181. doi: 10.1016/j.tre.2010.09.010
   Web of Science ®Google Scholar