The Vehicle Routing Problem with Availability Profiles

References

• Agatz N, Campbell A, Fleischmann M, Savelsbergh M (2011) Time slot management in attended home delivery. Transportation Sci. 45(3):435–449.Link, Google Scholar
• Belhaiza S, Hansen P, Laporte G (2014) A hybrid variable neighborhood tabu search heuristic for the vehicle routing problem with multiple time windows. Comput. Oper. Res. 52(Part B, December):269–281.Crossref, Google Scholar
• Belhaiza S, M’Hallah R, Ben Brahim G (2017) A new hybrid genetic variable neighborhood search heuristic for the vehicle routing problem with multiple time windows. 2017 IEEE Congress Evolutionary Comput. (CEC), 1319–1326.Google Scholar
• Belhaiza S, M’Hallah R, Brahim GB, Laporte G (2019) Three multi-start data-driven evolutionary heuristics for the vehicle routing problem with multiple time windows. J. Heuristics 25(3):485–515.Crossref, Google Scholar
• Ben-Ameur W (2004) Computing the initial temperature of simulated annealing. Comput. Optim. Appl. 29(3):369–385.Crossref, Google Scholar
• Cardenas I, Beckers J, Vanelslander T, Verhetsel A, Dewulf W (2016) Spatial characteristics of failed and successful e-commerce deliveries in Belgian cities. Proc. 6th Internat. Conf. on Inform. Systems Logist. Supply Chain (INFORMS, Catonsville, MD).Google Scholar
• de Jong C, Kant G, van Vlient A (1996) On finding minimal route duration in the vehicle routing problem with multiple time windows. Working paper, Department of Computer Science, Utrecht University, Utrecht, Netherlands.Google Scholar
• Edwards J, McKinnon A, Cherrett T, McLeod F, Song L (2009) The impact of failed home deliveries on carbon emissions: Are collection/delivery points environmentally-friendly alternatives. Proc. 14th Annual Logist. Res. Network Conf.Google Scholar
• Favaretto D, Moretti E, Pellegrini P (2007) Ant colony system for a VRP with multiple time windows and multiple visits. J. Interdisciplinary Math. 10(2):263–284.Crossref, Google Scholar
• Figliozzi MA (2010) An iterative route construction and improvement algorithm for the vehicle routing problem with soft time windows. Transportation Res. Part C Emerging Tech. 18(5):668–679.Crossref, Google Scholar
• Florio AM, Feillet D, Hartl RF (2018) The delivery problem: Optimizing hit rates in e-commerce deliveries. Transportation Res. Part B Methodological 117(Part A, November):455–472.Crossref, Google Scholar
• Fu Z, Eglese R, Li LYO (2008) A unified tabu search algorithm for vehicle routing problems with soft time windows. J. Oper. Res. Soc. 59(5):663–673.Crossref, Google Scholar
• Gershuny J, Sullivan O (2017) United Kingdom Time Use Survey, 2014-2015. Data collection, Centre for Time Use Research, IOE, University College London, London.Google Scholar
• Hashimoto H, Ibaraki T, Imahori S, Yagiura M (2006) The vehicle routing problem with flexible time windows and traveling times. Discrete Appl. Math. 154(16):2271–2290.Crossref, Google Scholar
• Hermes (2020) Allgemeine Geschäftsbedingungen für den Versand von Hermes Päckchen. Hermes Germany GmbH. Accessed October 22, 2022, https://www.myhermes.de/content/pdf/agb_verpackungsrichtlinien.pdf.Google Scholar
• Hoogeboom M, Dullaert W, Lai D, Vigo D (2020) Efficient neighborhood evaluations for the vehicle routing problem with multiple time windows. Transportation Sci. 54(2):400–416.Link, Google Scholar
• Hübner A, Kuhn H, Wollenburg J (2016) Last mile fulfilment and distribution in omni-channel grocery retailing. Internat. J. Retail Distribution Management 44(3):228–247.Crossref, Google Scholar
• Ibaraki T, Imahori S, Kubo M, Masuda T, Uno T, Yagiura M (2005) Effective local search algorithms for routing and scheduling problems with general time-window constraints. Transportation Sci. 39(2):206–232.Link, Google Scholar
• Ibaraki T, Imahori S, Nonobe K, Sobue K, Uno T, Yagiura M (2008) An iterated local search algorithm for the vehicle routing problem with convex time penalty functions. Discrete Appl. Math. 156(11):2050–2069.Crossref, Google Scholar
• Johnson DS, Aragon CR, McGeoch LA, Schevon C (1989) Optimization by simulated annealing: An experimental evaluation. Part I. Graph partitioning. Oper. Res. 37(6):865–892.Link, Google Scholar
• Koskosidis YA, Powell WB, Solomon MM (1992) An optimization-based heuristic for vehicle routing and scheduling with soft time window constraints. Transportation Sci. 26(2):69–85.Link, Google Scholar
• Kritzinger S, Tricoire F, Doerner KF, Hartl RF, Stützle T (2017) A unified framework for routing problems with a fixed fleet size. Internat. J. Metaheuristics 6(3):160–209.Crossref, Google Scholar
• Larsen R, Pacino D (2019) Fast delta evaluation for the vehicle routing problem with multiple time windows. Preprint, submitted May 10, https://arxiv.org/abs/1905.04114.Google Scholar
• Mouthuy S, Massen F, Deville Y, Van Hentenryck P (2015) A multistage very large-scale neighborhood search for the vehicle routing problem with soft time windows. Transportation Sci. 49(2):223–238.Link, Google Scholar
• Mühlbauer F, Fontaine P (2021) A parallelised large neighbourhood search heuristic for the asymmetric two-echelon vehicle routing problem with swap containers for cargo-bicycles. Eur. J. Oper. Res. 289(2):742–757.Crossref, Google Scholar
• Nagata Y (1997) Edge assembly crossover: A high-power genetic algorithm for the traveling salesman problem. Back T, ed. Proc. 7th Internat. Conf. Genetic Algorithms (ACM, New York).Google Scholar
• Nagata Y, Bräysy O, Dullaert W (2010) A penalty-based edge assembly memetic algorithm for the vehicle routing problem with time windows. Comput. Oper. Res. 37(4):724–737.Crossref, Google Scholar
• Okholm HB, Thelle MH, Möller A, Basalisco B, Rolmer S (2013) E-commerce and delivery: A study of the state of play of EU parcel markets with particular emphasis on e-commerce. European Commission. Accessed October 22, 2022, https://op.europa.eu/en/publication-detail/-/publication/fe614075-8eb3-4178-b753-f0123ce90ba1.Google Scholar
• Özarik SS, Veelenturf LP, Van Woensel T, Laporte G (2021) Optimizing e-commerce last-mile vehicle routing and scheduling under uncertain customer presence. Transportation Res. Part E Logist. Transportation Rev. 148(April):102263.Google Scholar
• Pan S, Giannikas V, Han Y, Grover-Silva E, Qiao B (2017) Using customer-related data to enhance e-grocery home delivery. Indust. Management Data Systems 117(9):1917–1933.Crossref, Google Scholar
• Pisinger D, Ropke S (2007) A general heuristic for vehicle routing problems. Comput. Oper. Res. 34(8):2403–2435.Crossref, Google Scholar
• Pitney Bowes (2021) Pitney Bowes shipping index 2021. Accessed October 22, 2022, https://www.pitneybowes.com/content/dam/pitneybowes/us/en/shipping-index/parcel_shipping_index_ebook_final.pdf.Google Scholar
• Praet S, Martens D (2019) Efficient parcel delivery by predicting customers’ locations. Decision Sci. 51(5):1202–1231.Crossref, Google Scholar
• Punakivi M, Saranen J (2001) Identifying the success factors in e-grocery home delivery. Internat. J. Retail Distribution Management 29(4):156–163.Crossref, Google Scholar
• Punakivi M, Yrjölä H, Holmström J (2001) Solving the last mile issue: Reception box or delivery box? Internat. J. Physical Distribution Logist. Management 31(6):427–439.Crossref, Google Scholar
• Ropke S, Pisinger D (2006) An adaptive large neighborhood search heuristic for the pickup and delivery problem with time windows. Transportation Sci. 40(4):455–472.Link, Google Scholar
• Schaap H, Schiffer M, Schneider M, Walther G (2022) A large neighborhood search for the vehicle routing problem with multiple time windows. Transportation Sci., ePub ahead of print April 7, https://doi.org/10.1287/trsc.2021.1120.Google Scholar
• Schwerdfeger S, Boysen N (2020) Optimizing the changing locations of mobile parcel lockers in last-mile distribution. Eur. J. Oper. Res. 285(3):1077–1094.Crossref, Google Scholar
• Solomon MM (1987) Algorithms for the vehicle routing and scheduling problems with time window constraints. Oper. Res. 35(2):254–265.Link, Google Scholar
• Taillard É, Badeau P, Gendreau M, Guertin F, Potvin JY (1997) A tabu search heuristic for the vehicle routing problem with soft time windows. Transportation Sci. 31(2):170–186.Link, Google Scholar
• van Duin JHR, de Goffau W, Wiegmans B, Tavasszy LA, Saes M (2016) Improving home delivery efficiency by using principles of address intelligence for B2C deliveries. Transportation Res. Procedia 12:14–25.Crossref, Google Scholar
• Vidal T, Crainic TG, Gendreau M, Prins C (2013a) A hybrid genetic algorithm with adaptive diversity management for a large class of vehicle routing problems with time-windows. Comput. Oper. Res. 40(1):475–489.Crossref, Google Scholar
• Vidal T, Crainic TG, Gendreau M, Prins C (2013b) Heuristics for multi-attribute vehicle routing problems: A survey and synthesis. Eur. J. Oper. Res. 231(1):1–21.Crossref, Google Scholar
• Vidal T, Crainic TG, Gendreau M, Prins C (2014) A unified solution framework for multi-attribute vehicle routing problems. Eur. J. Oper. Res. 234(3):658–673.Crossref, Google Scholar
• Vidal T, Crainic TG, Gendreau M, Prins C (2015) Timing problems and algorithms: Time decisions for sequences of activities. Networks 65(2):102–128.Crossref, Google Scholar
• Vidal T, Crainic TG, Gendreau M, Lahrichi N, Rei W (2012) A hybrid genetic algorithm for multidepot and periodic vehicle routing problems. Oper. Res. 60(3):611–624.Link, Google Scholar
• Voigt S, Kuhn H (2022) Crowdsourced logistics: The pickup and delivery problem with transshipments and occasional drivers. Networks 79(3):403–426.Crossref, Google Scholar
• Voigt S, Frank M, Fontaine P, Kuhn H (2022) Hybrid adaptive large neighborhood search for vehicle routing problems with depot location decisions. Comput. Oper. Res. 146(October):105856.Crossref, Google Scholar
• von Abrams K (2021) Global ecommerce forecast 2021. eMarketer (July 7, 2021), accessed October 22, 2022, https://www.emarketer.com/content/global-ecommerce-forecast-2021.Google Scholar
• Wong RT (2008) Vehicle Routing for Small Package Delivery and Pickup Services (Springer, Boston), 475–485.Crossref, Google Scholar