On the Concept of Opportunity Cost in Integrated Demand Management and Vehicle Routing

Published Online:https://doi.org/10.1287/trsc.2024.0644

References

  • Abdollahi M, Yang X, Nasri MI, Fairbank M (2023) Demand management in time-slotted last-mile delivery via dynamic routing with forecast orders. Eur. J. Oper. Res. 309(2):704–718.CrossrefGoogle Scholar
  • Adelman D (2007) Dynamic bid prices in revenue management. Oper. Res. 55(4):647–661.LinkGoogle Scholar
  • Agatz N, Campbell AM, Fleischmann M, Van Nunen J, Savelsbergh M (2013) Revenue management opportunities for Internet retailers. J. Revenue Pricing Management 12(2):128–138.CrossrefGoogle Scholar
  • Akkerman F, Mes M, Lalla-Ruiz E (2022) Dynamic time slot pricing using delivery costs approximations. de Armas J, Ramalhinho H, Voß S, eds. Computational Logistics, Lecture Notes in Computer Science, vol. 13557 (Springer, Cham, Switzerland), 214–230.CrossrefGoogle Scholar
  • Al-Kanj L, Nascimento J, Powell WB (2020) Approximate dynamic programming for planning a ride-hailing system using autonomous fleets of electric vehicles. Eur. J. Oper. Res. 284(3):1088–1106.CrossrefGoogle Scholar
  • Arian E, Bai X, Chen X (2022) Joint pricing and routing for a ride-sharing platform in low-density rural areas. Working paper, University of Illinois at Urbana-Champaign, Urbana.Google Scholar
  • Asdemir K, Jacob VS, Krishnan R (2009) Dynamic pricing of multiple home delivery options. Eur. J. Oper. Res. 196(1):246–257.CrossrefGoogle Scholar
  • Atasoy B, Ikeda T, Song X, Ben-Akiva ME (2015) The concept and impact analysis of a flexible mobility on demand system. Transportation Res. Part C Emerging Tech. 56:373–392.CrossrefGoogle Scholar
  • Avraham E, Raviv T (2021) The steady-state mobile personnel booking problem. Transportation Res. Part B Methodological 154:266–288.CrossrefGoogle Scholar
  • Azi N, Gendreau M, Potvin JY (2012) A dynamic vehicle routing problem with multiple delivery routes. Ann. Oper. Res. 199(1):103–112.CrossrefGoogle Scholar
  • Bertsimas D, Jaillet P, Martin S (2019) Online vehicle routing: The edge of optimization in large-scale applications. Oper. Res. 67(1):143–162.LinkGoogle Scholar
  • Campbell AM, Savelsbergh M (2005) Decision support for consumer direct grocery initiatives. Transportation Sci. 39(3):313–327.LinkGoogle Scholar
  • Campbell AM, Savelsbergh M (2006) Incentive schemes for attended home delivery services. Transportation Sci. 40(3):327–341.LinkGoogle Scholar
  • Fleckenstein D, Klein R, Steinhardt C (2023) Recent advances in integrating demand management and vehicle routing: A methodological review. Eur. J. Oper. Res. 306(2):499–518.CrossrefGoogle Scholar
  • Gallego G, Topaloglu H (2019) Revenue Management and Pricing Analytics (Springer, New York).CrossrefGoogle Scholar
  • Hosni H, Naoum-Sawaya J, Artail H (2014) The shared-taxi problem: Formulation and solution methods. Transportation Res. Part B Methodological 70:303–318.CrossrefGoogle Scholar
  • Klein V, Steinhardt C (2023) Dynamic demand management and online tour planning for same-day delivery. Eur. J. Oper. Res. 307(2):860–886.CrossrefGoogle Scholar
  • Klein R, Koch S, Steinhardt C, Strauss AK (2020) A review of revenue management: Recent generalizations and advances in industry applications. Eur. J. Oper. Res. 284(2):397–412.CrossrefGoogle Scholar
  • Klein R, Mackert J, Neugebauer M, Steinhardt C (2018) A model-based approximation of opportunity cost for dynamic pricing in attended home delivery. OR Spectrum 40(4):969–996.CrossrefGoogle Scholar
  • Koch S (2017) Least squares approximate policy iteration for learning bid prices in choice-based revenue management. Comput. Oper. Res. 77:240–253.CrossrefGoogle Scholar
  • Koch S, Klein R (2020) Route-based approximate dynamic programming for dynamic pricing in attended home delivery. Eur. J. Oper. Res. 287(2):633–652.CrossrefGoogle Scholar
  • Kullman ND, Cousineau M, Goodson JC, Mendoza JE (2022) Dynamic ride-hailing with electric vehicles. Transportation Sci. 56(3):775–794.LinkGoogle Scholar
  • Lang MA, Cleophas C, Ehmke JF (2021) Anticipative dynamic slotting for attended home deliveries. Oper. Res. Forum 2(4):1–39.CrossrefGoogle Scholar
  • Laud AD (2004) Theory and application of reward shaping in reinforcement learning. PhD thesis, University of Illinois at Urbana-Champaign, Urbana.Google Scholar
  • Lebedev D, Goulart P, Margellos K (2021) A dynamic programming framework for optimal delivery time slot pricing. Eur. J. Oper. Res. 292(2):456–468.CrossrefGoogle Scholar
  • Lebedev D, Margellos K, Goulart P 2020 Approximate dynamic programming for delivery time slot pricing: A sensitivity analysis. Working paper, University of Oxford, Oxford, UK.Google Scholar
  • Lee TC, Hersh M (1993) A model for dynamic airline seat inventory control with multiple seat bookings. Transportation Sci. 27(3):252–265.LinkGoogle Scholar
  • Li Y, Archetti C, Ljubic I (2024) Emerging optimization problems for distribution in same-day delivery. Working paper, ESSEC Business School, Cergy-Pontoise, France.Google Scholar
  • Mackert J (2019) Choice-based dynamic time slot management in attended home delivery. Comput. Industry Engrg. 129:333–345.CrossrefGoogle Scholar
  • Maddah B, Moussawi-Haidar L, El-Taha M, Rida H (2010) Dynamic cruise ship revenue management. Eur. J. Oper. Res. 207(1):445–455.CrossrefGoogle Scholar
  • Powell WB (2011) Approximate Dynamic Programming: Solving the Curses of Dimensionality (John Wiley & Sons, Hoboken, NJ).CrossrefGoogle Scholar
  • Powell WB (2022) Reinforcement Learning and Stochastic Optimization: A Unified Framework for Sequential Decisions (John Wiley & Sons, Hoboken, NJ).CrossrefGoogle Scholar
  • Powell WB, Simao HP, Bouzaiene-Ayari B (2012) Approximate dynamic programming in transportation and logistics: A unified framework. EURO J. Transportation Logist. 1(3):237–284.CrossrefGoogle Scholar
  • Prokhorchuk A, Dauwels J, Jaillet P (2019) Stochastic dynamic pricing for same-day delivery routing. Working paper, Nanyang Technological University, Singapore.Google Scholar
  • Quante R, Fleischmann M, Meyr H (2009) A stochastic dynamic programming approach to revenue management in a make-to-stock production system. Working paper, Vienna University of Economics and Business, Vienna.Google Scholar
  • Russell SJ, Zimdars A (2003) Q-decomposition for reinforcement learning agents. Fawcett T, Mishra N, eds. Proc. 20th Internat. Conf. Machine Learn. (AAAI Press, Washington, DC), 656–663.Google Scholar
  • Snoeck A, Merchán D, Winkenbach M (2020) Revenue management in last-mile delivery: State-of-the-art and future research directions. Transportation Res. Proc. 46:109–116.CrossrefGoogle Scholar
  • Soeffker N, Ulmer MW, Mattfeld DC (2022) Stochastic dynamic vehicle routing in the light of prescriptive analytics: A review. Eur. J. Oper. Res. 298(3):801–820.CrossrefGoogle Scholar
  • Strauss AK, Gülpinar N, Zheng Y (2021) Dynamic pricing of flexible time slots for attended home delivery. Eur. J. Oper. Res. 294(3):1022–1041.CrossrefGoogle Scholar
  • Strauss AK, Klein R, Steinhardt C (2018) A review of choice-based revenue management: Theory and methods. Eur. J. Oper. Res. 271(2):375–387.CrossrefGoogle Scholar
  • Subramanian J, Stidham S Jr, Lautenbacher CJ (1999) Airline yield management with overbooking, cancellations, and no-shows. Transportation Sci. 33(2):147–167.LinkGoogle Scholar
  • Talluri K, Van Ryzin G (2004) The Theory and Practice of Revenue Management (Springer, Boston).CrossrefGoogle Scholar
  • Ulmer MW (2020) Dynamic pricing and routing for same-day delivery. Transportation Sci. 54(4):1016–1033.LinkGoogle Scholar
  • Ulmer MW, Goodson JC, Mattfeld DC, Hennig M (2019) Offline–online approximate dynamic programming for dynamic vehicle routing with stochastic requests. Transportation Sci. 53(1):185–202.LinkGoogle Scholar
  • Ulmer MW, Goodson JC, Mattfeld DC, Thomas BW (2020) On modeling stochastic dynamic vehicle routing problems. EURO J. Transportation Logist. 9(2):100008.CrossrefGoogle Scholar
  • Van Seijen H, Fatemi M, Romoff J, Laroche R, Barnes T, Tsang J (2017) Hybrid reward architecture for reinforcement learning. Guyon I, Von Luxburg U, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R, eds. Advance Neural Information Processing Systems (Neural Information Processing Systems Foundation, Inc. (NeurIPS), San Diego, CA), 5392–5402.Google Scholar
  • Vinsensius A, Wang Y, Chew EP, Lee LH (2020) Dynamic incentive mechanism for delivery slot management in e-commerce attended home delivery. Transportation Sci. 54(3):567–587.LinkGoogle Scholar
  • Waßmuth K, Köhler C, Agatz N, Fleischmann M (2023) Demand management for attended home delivery: A literature review. Eur. J. Oper. Res. 311(3):801–815.CrossrefGoogle Scholar
  • Weatherford LR, Bodily SE (1992) A taxonomy and research overview of perishable-asset revenue management: Yield management, overbooking, and pricing. Oper. Res. 40(5):831–844.LinkGoogle Scholar
  • Xu L, Wang Q, Huang S (2015) Dynamic order acceptance and scheduling problem with sequence-dependent setup time. Internat. J. Production Res. 53(19):5797–5808.CrossrefGoogle Scholar
  • Yang X, Strauss AK (2017) An approximate dynamic programming approach to attended home delivery management. Eur. J. Oper. Res. 263(3):935–945.CrossrefGoogle Scholar
  • Yang X, Strauss AK, Currie CS, Eglese R (2016) Choice-based demand management and vehicle routing in e-fulfillment. Transportation Sci. 50(2):473–488.LinkGoogle Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.