The paper aims to optimize the final part of a firm’s value chain with regard to attended last-mile deliveries. It is assumed that to be profitable, e-commerce businesses need to maximize the overall value of fulfilled orders (rather than their number), while also limiting costs of delivery. To do so, it is essential to decide which delivery requests to accept and which time windows to offer to which consumers. This is especially relevant for attended deliveries, as delivery fees usually cannot fully compensate costs of delivery given tight delivery time windows. The literature review shows that existing order acceptance techniques often ignore either the order value or the expected costs of delivery. The paper presents an iterative solution approach: after calculating an approximate transport capacity based on forecasted expected delivery requests and a cost-minimizing routing, actual delivery requests are accepted or rejected aiming to maximize the overall value of orders given the computed transport capacity. With the final set of accepted requests, the routing solution is updated to minimize costs of delivery. The presented solution approach combines well-known methods from revenue management and time-dependent vehicle routing. In a computational study for a German metropolitan area, the potential and the limits of value-based demand fulfillment as well as its sensitivity regarding forecast accuracy and demand composition are investigated.
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Agatz N, Campbell AM, Fleischmann M, Savelsbergh M (2011) Time slot management in attended home delivery. Transportation Science 45(3):435–449
Allen J, Thorne G, Browne M (2007) BESTUFS good practice guide of urban freight transport. Manual. http://www.bestufs.net/download/BESTUFS_II/good_practice/English_BESTUFS_Guide.pdf. Accessed 2013-06-30
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
Belobaba PP (1987) Air travel demand and airline seat inventory management. Dissertation, Flight Transportation Laboratory, Massachusetts Institute of Technology, Cambridge
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
Bräysy O, Gendreau M (2005b) Vehicle routing problem with time windows. Part II. Metaheuristics. Transportation Science 39(1):119–139
Campbell AM, Savelsbergh M (2005) Decision support for consumer direct grocery initiatives. Transportation Science 39(3):313–327
Cleophas C, Frank M, Kliewer N (2009) Recent developments in demand forecasting for airline revenue management. International Journal of Revenue Management 6(3):252–269
Donati AV, Montemanni R, Casagrande N, Rizzoli AE, Gambardella LM (2008) Time dependent vehicle routing problem with a multi ant colony system. European Journal of Operational Research 185(3):1174–1191
Ehmke JF, Steinert A, Mattfeld DC (2012a) Advanced routing for city logistics service providers based on time-dependent travel times. International Journal of Computational Science 3(4):193–205
Ehmke JF, Meisel S, Mattfeld DC (2012b) Floating car based travel times for city logistics. Transportation Research Part C: Emerging Technologies 21(1):338–352
Ehmke JF, Campbell AM (2014) Customer acceptance mechanisms for attended home deliveries in metropolitan areas. European Journal of Operational Research 233(1):193–207
Fleischmann B, Gietz M, Gnutzmann S (2004) Time-varying travel times in vehicle routing. Transportation Science 38(2):160–173
Figliozzi MA (2009) A route improvement algorithm for the vehicle routing problem with time dependent travel times. In: Proceedings of the 88th transportation research board annual meeting, Washington, DC
Gevaers R, van de Voorde E, Vanelslander T (2010) Characteristics and typology of last-mile logistics from an innovation perspective in an urban context. In: Proc of WCTR 2010, Lisbon, Portugal
Haghani A, Jung S (2005) A dynamic vehicle routing problem with time-dependent travel times. Computers & Operations Research 32(11):2959–2986
Hahn GJ, Kuhn H (2012) Designing decision support systems for value-based management: a survey and an architecture. Decision Support Systems 53:591–598
Hashimoto H, Yagiura M, Ibaraki T (2008) An iterated local search algorithm for the time-dependent vehicle routing problem with time windows. Discrete Optimization 5(2):434–456
Ichoua S, Gendreau M, Potvin J-Y (2003) Vehicle dispatching with time-dependent travel times. European Journal of Operational Research 144(2):379–396
Kelton WD, Law A (2000) Simulation modeling and analysis. McGraw Hill, Boston
Kok AL, Hans EW, Schutten JMJ (2012) Vehicle routing under time-dependent travel times: the impact of congestion avoidance. Computers & Operations Research 39(5):910–918
Landeshauptstadt Stuttgart (2012) Kleinräumige Einkommensverteilung in Stuttgart 2009. http://www.stuttgart.de/item/show/305805/1/publ/20908. Accessed 2013-06-30
Littlewood K (1972) Forecasting and control of passenger booking. In: AGIFORS symposium proceedings
Maden W, Eglese R, Black D (2010) Vehicle routing and scheduling with time-varying data: a case study. Journal of Operations Research Society 61:515–522
Potvin JY, Rousseau JM (1993) A parallel route building algorithm for the vehicle routing and scheduling problem with time windows. European Journal of Operational Research 66(3):331–340
Punakivi M, Saranen J (2001) Identifying the success factors in e-grocery home delivery. Journal of Retail and Distribution Management 29(4):156–163
Quante R, Meyr H, Fleischmann M (2009) Revenue management and demand fulfillment: matching applications, models, and software. OR Spectrum 31:31–62
Stadtler H (2005) Supply chain management and advanced planning – basics, overview and challenges. European Journal of Operational Research 163(3):575–588
Talluri KT, Van Ryzin GJ (2004) The theory and practice of revenue management, vol 68. Springer, Heidelberg
US Census Bureau (2012). Quarterly retail e-commerce sales 2nd quarter 2012. News release. http://www.census.gov/retail/mrts/www/data/pdf/ec_current.pdf. Accessed 2012-09-30
Wirthman L (2013) Amazon, Ebay, Walmart same-day deliver, but should you? Forbes BrandVoice. http://www.forbes.com/sites/ups/2013/04/04/amazon-ebay-walmart-same-day-deliver-but-should-you/. Accessed 2013-06-30
Yang X, Strauss AK, Currie C, Eglese R (2012) Choice-based demand management and vehicle routing in e-fulfilment. Working paper, University of Warwick. http://www2.warwick.ac.uk/fac/soc/wbs/subjects/orms/about/people/strauss/publications/jointdemandmanagementfinalplainr1.pdf. Accessed 2013-10-15
Vinod B (2006) Advances in inventory control. Journal of Revenue and Pricing Management 4(4):367–381
Zeni R (2001) Improved forecast accuracy in airline revenue management by unconstraining demand estimates from censored data. Dissertation. State University of New Jersey
Accepted after three revisions by the editors of the special focus.
This article is also available in German in print and via http://www.wirtschaftsinformatik.de: Cleophas C, Ehmke JF (2014) Wann sind Lieferaufträge profitabel? Berücksichtigung des Auftragswertes und der Transportkapazität in der Tourenplanung für die „letzte Meile“ in Ballungsräumen. WIRTSCHAFTSINFORMATIK. doi: 10.1007/s11576-014-0412-8.
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Cleophas, C., Ehmke, J.F. When Are Deliveries Profitable?. Bus Inf Syst Eng 6, 153–163 (2014). https://doi.org/10.1007/s12599-014-0321-9
- Demand fulfillment
- Vehicle routing
- Revenue management
- City logistics