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Scheduling movements in the network of an express service provider

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Abstract

Express service providers manage shipments from senders to receivers under strict service level agreements. Such shipments are usually not sufficient to justify a single transportation, so it is preferred to maximize consolidation of these shipments to reduce cost. The consolidation is organized via depots and hubs: depots are local sorting centers that take care of the collection and delivery of the parcels at the customers, and hubs are used to consolidate the transportation between the depots. A single transportation between two locations, carried out by a certain vehicle at a specific time, is defined as a movement. In this paper, we address the problem of scheduling all movements in an express network at minimum cost. Our approach allows to impose restrictions on the number of arriving/departing movements at the hubs so that sufficient handling capacity is ensured. As the movement scheduling problem is complex, it is divided into two parts: one part concerns the movements between depots and hubs; the other part considers the movements between the hubs. We use a column generation approach and a local search algorithm to solve these two subproblems, respectively. Computational experiments show that by using this approach the total transportation costs are decreased.

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References

  • Aarts EHL, Lenstra JK (2003) Local search in combinatorial optimization. Princeton University Press, Princeton

    MATH  Google Scholar 

  • Armacost AP, Barnhart C, Ware KA (2002) Composite variable formulations for express shipment service network design. Transp Sci 36(1):1–20

    Article  MATH  Google Scholar 

  • Armacost AP, Barnhart C, Ware KA, Wilson AM (2004) UPS optimizes its air network. Interfaces 34(1):15–25

    Article  Google Scholar 

  • Baltz A, Dubhashi D, Srivastav A, Tansini L, Werth S (2007) Probabilistic analysis for a multiple depot vehicle routing problem. Random Struct Algorithms 30(1–2):206–225

    Article  MATH  MathSciNet  Google Scholar 

  • Barnhart C, Johnson EL, Nemhauser GL, Savelsbergh MWP, Vance PH (1998) Branch-and-price: column generation for solving huge integer programs. Oper Res 46(3):316–329

    Article  MATH  MathSciNet  Google Scholar 

  • Barnhart C, Schneur RR (1996) Air network design for express shipment service. Oper Res 44(6):852–863

    Article  MATH  Google Scholar 

  • Cordeau JF, Gendreau M, Laporte G (1997) A tabu search heuristic for periodic and multi-depot vehicle routing problems. Networks 30(2):105–119

    Article  MATH  Google Scholar 

  • Cormen TH, Leiserson CE, Rivest RL, Stein C (2001) Introduction to algorithms. The MIT University Press, Cambridge

    MATH  Google Scholar 

  • Crainic TG (2000) Service network design in freight transportation. Eur J Oper Res 122(2):272–288

    Article  MATH  Google Scholar 

  • Dejax PJ, Crainic TG (1987) A review of empty flows and fleet management models in freight transportation. Transp Sci 21(4):227–248

    Article  Google Scholar 

  • Farvolden JM, Powell WB (1994) Subgradient methods for the service network design problem. Transp Sci 28(3):256–272

    Article  MATH  MathSciNet  Google Scholar 

  • Holmberg K, Hellstrand J (1998) Solving the uncapacitated network design problem by a lagrangean heuristic and branch-and-bound. Oper Res 46(2):247–259

    Article  MATH  MathSciNet  Google Scholar 

  • Leung JMY, Magnanti TL, Singhal V (1990) Routing in point-to-point delivery systems: Formulations and solution heuristics. Transp Sci 24(4):245–260

    Article  MATH  Google Scholar 

  • Lübbecke ME, Desrosiers J (2005) Selected topics in column generation. Oper Res 53(6):1007–1023

    Article  MATH  MathSciNet  Google Scholar 

  • Magnanti TL, Mirchandani P (1993) Shortest paths, single origin-destination network design, and associated polyhedra. Networks 23(2):103–121

    Article  MATH  MathSciNet  Google Scholar 

  • Magnanti TL, Wong RT (1984) Network design and transportation planning: Models and algorithms. Transp Sci 18(1):1–55

    Article  Google Scholar 

  • Meuffels I, Fleuren H, Poppelaars J, Hoornenborg H, De Rooij F (2010) The design of express networks in a nutshell—playing the global optimisation game (GO-Game). OR News 39:6–8

    Google Scholar 

  • Mitrović-Minić S, Laporte G (2006) The pickup and delivery problem with time windows and transshipment. INFOR 44(3):217–228

    MathSciNet  Google Scholar 

  • Pedersen MB, Crainic TG, Madsen OBG (2009) Models and tabu search metaheuristics for service network design with asset-balance requirements. Transp Sci 43(2):158–177

    Article  Google Scholar 

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Correspondence to Ilse Louwerse.

Appendix

Appendix

See Fig. 4

Fig. 4
figure 4

Graphical representation of the GO Network. The squares represent hub locations and the circles represent depot locations

See Table 7

Table 7 Transportation cost, simulated cases of network 1

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Louwerse, I., Mijnarends, J., Meuffels, I. et al. Scheduling movements in the network of an express service provider. Flex Serv Manuf J 26, 565–584 (2014). https://doi.org/10.1007/s10696-013-9171-x

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  • DOI: https://doi.org/10.1007/s10696-013-9171-x

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