Strategies for Handling Temporal Uncertainty in Pickup and Delivery Problems with Time Windows

Published Online:https://doi.org/10.1287/trsc.2015.0658

References

  • Bandyk M (2012) Designated drivers—a nascent industry. America’s Future Foundation (November 7), http://americasfuture.org/doublethink/2012/11/designated-drivers-a-nascent-industry/.Google Scholar
  • Bent RW, Van Hentenryck P (2004) Scenario-based planning for partially dynamic vehicle routing with stochastic customers. Oper. Res. 52(6):977–987.LinkGoogle Scholar
  • Berbeglia G, Cordeau J-F, Laporte G (2010) Dynamic pickup and delivery problems. Eur. J. Oper. Res. 202(1):8–15.CrossrefGoogle Scholar
  • Cortés CE, Sáez D, Nuñez A, Muñoz-Carpintero D (2009) Hybrid adaptive predictive control for a dynamic pickup and delivery problem. Transportation Sci. 43(1):27–42.LinkGoogle Scholar
  • Desrosiers J, Dumas Y, Solomon MM, Soumis F (1995) Time constrained routing and scheduling. Ball MO, Magnanti TL, Monma CL, Nemhauser GL, eds. Network Routing, Handbooks Oper. Res. Management Sci., Vol. 8 (North-Holland, Amsterdam), 35–139.CrossrefGoogle Scholar
  • Dror M, Laporte G, Trudeau P (1989) Vehicle routing with stochastic demands: Properties and solution frameworks. Transportation Sci. 23(3):166–176.LinkGoogle Scholar
  • Ghiani G, Manni E, Thomas BW (2012) A comparison of anticipatory algorithms for the dynamic and stochastic traveling salesman problem. Transportation Sci. 46(3):374–387.LinkGoogle Scholar
  • Goodson JC, Ohlmann JW, Thomas BW (2013) Rollout policies for dynamic solutions to the multivehicle routing problem with stochastic demand and duration limits. Oper. Res. 61(1):138–154.LinkGoogle Scholar
  • Gurobi Optimization, Inc. (2012) Gurobi optimizer reference manual. http://www.gurobi.com.Google Scholar
  • Hvattum LM, Løkketangen A, Laporte G (2006) Solving a dynamic and stochastic vehicle routing problem with a sample scenario hedging heuristic. Transportation Sci. 40(4):421–438.LinkGoogle Scholar
  • Hyytiä E, Penttinen A, Sulonen R (2012) Non-myopic vehicle and route selection in dynamic darp with travel time and workload objectives. Comput. Oper. Res. 39(12):3021–3030.CrossrefGoogle Scholar
  • Ichoua S, Gendreau M, Potvin J-Y (2006) Exploiting knowledge about future demands for real-time vehicle dispatching. Transportation Sci. 40(2):211–225.LinkGoogle Scholar
  • Jaillet P, Lu X (2011) Online traveling salesman problems with service flexibility. Networks 58(2):137–146.Google Scholar
  • Jaillet P, Wagner MR (2006) Online routing problems: Value of advanced information as improved competitive ratios. Transportation Sci. 40(2):200–210.LinkGoogle Scholar
  • Kim Y, Mahmassani HS, Jaillet P (2004) Dynamic truckload routing, scheduling, and load acceptance for large fleet operation with priority demands. Transportation Res. Record: J. Transportation Res. Board 1882(1):120–128.CrossrefGoogle Scholar
  • Larsen A, Madsen OBG, Solomon MM (2004) The a priori dynamic traveling salesman problem with time windows. Transportation Sci. 38(4):459–472.LinkGoogle Scholar
  • Mes M, van der Heijden M, Schuur P (2010) Look-ahead strategies for dynamic pickup and delivery problems. OR Spectrum 32(2): 395–421.CrossrefGoogle Scholar
  • Mitrović-Minić S, Krishnamurti R, Laporte G (2004) Double-horizon based heuristics for the dynamic pickup and delivery problem with time windows. Transportation Res. Part B: Methodological 38(8):669–685.CrossrefGoogle Scholar
  • Parragh SN, Doerner KF, Hartl RF (2008) A survey on pickup and delivery problems. J. für Betriebswirtschaft 58(2):81–117.CrossrefGoogle Scholar
  • Pillac V, Guéret C, Medaglia A (2012b) An event-driven optimization framework for dynamic vehicle routing. Decision Support Systems 54(1):414–423.CrossrefGoogle Scholar
  • Pillac V, Gendreau M, Guéret C, Medaglia AL (2012a) A review of dynamic vehicle routing problems. Eur. J. Oper. Res. 225(1):1–11.CrossrefGoogle Scholar
  • Powell WB, Sheffi Y, Nickerson KS, Butterbaugh K, Atherton S (1988) Maximizing profits for North American van lines’ truckload division: A new framework for pricing and operations. Interfaces 18(1):21–41.LinkGoogle Scholar
  • Savelsbergh MWP, Sol M (1995) The general pickup and delivery problem. Transportation Sci. 29(1):17–29.LinkGoogle Scholar
  • Srour J, Zuidwijk R (2008) How much is location information worth? A competitive analysis of the online traveling salesman problem with two disclosure dates. Technical report ERS-2008-075-LIS, Erasmus Research Institute of Management, Erasmus University Rotterdam, Netherlands. http://hdl.handle.net/1765/13837.Google Scholar
  • Stefik M (1981) Planning with constraints (MOLGEN: Part 1). Artificial Intelligence 16(2):111–139.CrossrefGoogle Scholar
  • Thomas BW (2007) Waiting strategies for anticipating service requests from known customer locations. Transportation Sci. 41(3):319–331.LinkGoogle Scholar
  • Thomas BW, White CC (2004) Anticipatory route selection. Transportation Sci. 38(4):473–487.LinkGoogle Scholar
  • Williams WH, Goodman ML (1971) A simple method for the construction of empirical confidence limits for economic forecasts. J. Amer. Statist. Assoc. 66(336):752–754.CrossrefGoogle Scholar
  • Yang J, Jaillet P, Mahmassani H (1999) On-line algorithms for truck fleet assignment and scheduling under real-time information. Transportation Res. Record: J. Transportation Res. Board 1667:107–113.CrossrefGoogle Scholar
  • Yang J, Jaillet P, Mahmassani H (2004) Real-time multivehicle truckload pickup and delivery problems. Transportation Sci. 38(2):135–148.LinkGoogle Scholar
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