Skip to main content
Log in

Heuristic algorithms for the single allocation p-hub center problem with routing considerations

  • Regular Article
  • Published:
OR Spectrum Aims and scope Submit manuscript

Abstract

Given a network with n nodes, the p-hub center problem locates p hubs and allocates the remaining non-hub nodes to the hubs in such a way that the maximum distance (or time) between all pairs of nodes is minimized. Commonly, it is assumed that a vehicle is available to operate between each demand center and hub. Thus traditional p-hub center models assume that vehicles do not visit more than one non-hub node. However, in many-to-many distribution systems, there are some cases where nodes do not have enough demand to justify direct connections between the non-hub nodes and the hubs. This results in unnecessarily increasing the total number of vehicles on the network. Therefore, the optimal hub network design ought to include location-allocation and routing decisions simultaneously to form the routes among the nodes allocated to the same hubs. In this paper, through the observations from real-life hub networks, we introduce the p-hub center and routing network design problem (pHCVRP) and propose a mixed integer programming (MIP) formulation to model this problem formally. The aim is to locate p hubs, allocate demand centers to the hubs and determine the routes of vehicles for each hub such that the maximum travel time between all origin-destination pairs is minimized. We prove that pHCVRP is NP-hard and therefore only very small instances can be solved to optimality using a MIP solver. Hence, we develop two heuristics based on ant colony system (ACS) and discrete particle swarm optimization (DPSO) to obtain solutions for realistic instance sizes. Our design of the DPSO is quite different to the standard DPSO methods. In our DPSO, we combine concepts from simulated annealing (SA) and ACS to update the particles. We also use iterated local search (ILS) as a baseline algorithm to observe the improvements from a pure local search through more complex algorithms. We test the performance of the heuristics that we develop on the Turkish network and Australia Post data set and compare the performance of these methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Ai TJ, Kachitvichyanukul V (2009) A particle swarm optimization for the vehicle routing problem with simultaneous pickup and delivery. Comput Oper Res 36(5):1693–1702

    Article  Google Scholar 

  • Alumur S, Kara BY (2008) Network hub location problems: the state of the art. Eur J Oper Res 190(1):1–21

    Article  Google Scholar 

  • Alumur S, Kara BY (2009) A hub covering network design problem for cargo applications in Turkey. J Oper Res Soc 60(10):1349–1359

    Article  Google Scholar 

  • Alumur SA, Kara BY, Karasan OE (2009) The design of single allocation incomplete hub networks. Transp Res Part B Methodol 43(10):936–951

    Article  Google Scholar 

  • Alumur SA, Kara BY, Karasan OE (2012a) Multimodal hub location and hub network design. Omega 40(6):927–939

    Article  Google Scholar 

  • Alumur SA, Yaman H, Kara BY (2012b) Hierarchical multimodal hub location problem with time-definite deliveries. Transp Res Part E Logist Transp Rev 48(6):1107–1120

    Article  Google Scholar 

  • Aykin T (1995) The hub location and routing problem. Eur J Oper Res 83(1):200–219

    Article  Google Scholar 

  • Bashiri M, Mirzaei M, Randall M (2013) Modeling fuzzy capacitated p-hub center problem and a genetic algorithm solution. Appl Math Model 37(5):3513–3525

    Article  Google Scholar 

  • Brimberg J, Mladenovic N, Todosijevic R, Dragan U (2015) A basic variable neighborhood search heuristic for the uncapacitated multiple allocation \(p\)-hub center problem. Optim Lett 11:313–327

    Article  Google Scholar 

  • Bruns A, Klose A, Stahly P (2000) Restructuring of swiss parcel delivery services. OR-Spektrum 22(2):285–302

    Article  Google Scholar 

  • Bullnheimer B, Hartl RF, Strauß C (1997) A new rank based version of the ant system—a computational study. Cent Eur J Oper Res Econ 7:25–38

    Google Scholar 

  • Camargo RSD, Miranda GD, Lokketangen A (2013) A new formulation and an exact approach for the many-to-many hub location-routing problem. Appl Math Model 37(1213):7465–7480

    Article  Google Scholar 

  • Campbell JF (1994) Integer programming formulations of discrete hub location problems. Eur J Oper Res 72(2):387–405

    Article  Google Scholar 

  • Campbell JF, O’Kelly ME (2012) Twenty-five years of hub location research. Transp Sci 46(2):153–169

    Article  Google Scholar 

  • Campbell JF, Ernst AT, Krishnamoorthy M (2002) Hub location problems. In: Hamacher H, Drezner Z (eds) Facility location: applications and theory. Springer, Berlin, pp 373–408

    Chapter  Google Scholar 

  • Campbell JF, Stiehr G, Ernst AT, Krishnamoorthy M (2003) Solving hub arc location problems on a cluster of workstations. Parallel Comput 29:555–574

    Article  Google Scholar 

  • Campbell JF, Ernst AT, Krishnamoorthy M (2005a) Hub arc location problems: part I—introduction and results. Manag Sci 51:1540–1555

    Article  Google Scholar 

  • Campbell JF, Ernst AT, Krishnamoorthy M (2005b) Hub arc location problems: part II—formulations and optimal algorithms. Manag Sci 51:1556–1571

    Article  Google Scholar 

  • Cetiner S, Sepil C, Sural H (2010) Hubbing and routing in postal delivery systems. Ann Oper Res 181(1):109–124

    Article  Google Scholar 

  • Cheng CY, Chen YY, Chen TL, Yoo JJW (2015) Using a hybrid approach based on the particle swarm optimization and ant colony optimization to solve a joint order batching and picker routing problem. Int J Prod Econ 170(Part C):805–814

    Article  Google Scholar 

  • Dorigo M, Gambardella LM (1997a) Ant colonies for the travelling salesman problem. Biosystems 43(2):73–81

    Article  Google Scholar 

  • Dorigo M, Gambardella LM (1997b) Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans Evolut Comput 1(1):53–66

    Article  Google Scholar 

  • Dorigo M, Stutzle T (2010) Ant colony optimization: overview and recent advances. In: Gendreau M, Potvin JY (eds) Handbook of metaheuristics. Springer, US, Berlin, pp 227–263

    Chapter  Google Scholar 

  • Dorigo M, Maniezzo V, Colorni A (1996) Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern Part B 26(1):29–41

    Article  Google Scholar 

  • Ernst AT, Krishnamoorthy M (1996) Efficient algorithms for the uncapacitated single allocation p-hub median problem. Locat Sci 4(3):139–154

    Article  Google Scholar 

  • Ernst AT, Hamacher H, Jiang H, Krishnamoorthy M, Woeginger G (2009) Uncapacitated single and multiple allocation \(p\)-hub center problems. Comput Oper Res 36(7):2230–2241

    Article  Google Scholar 

  • Gelareh S, Monemi RN, Nickel S (2015) Multi-period hub location problems in transportation. Transp Res Part E Logist Transp Rev 75:67–94

    Article  Google Scholar 

  • Goksal FP, Karaoglan I, Altiparmak F (2013) A hybrid discrete particle swarm optimization for vehicle routing problem with simultaneous pickup and delivery. Comput Ind Eng 65(1):39–53

    Article  Google Scholar 

  • Kara BY, Tansel B (2000) On the single-assignment p-hub center problem. Eur J Oper Res 125(3):648–655

    Article  Google Scholar 

  • Kara BY, Tansel B (2001) The latest arrival hub location problem. Manag Sci 47(10):1408–1420

    Article  Google Scholar 

  • Karimi H, Setak M (2014) Proprietor and customer costs in the incomplete hub location-routing network topology. Appl Math Model 38(3):1011–1023

    Article  Google Scholar 

  • Kartal Z, Ernst AT (2015) Integer programming formulations for the uncapacitated vehicle routing p-hub center problem. In: Weber T, McPhee M, Anderssen R (eds) 21st International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, Gold Coast, pp 1724–1730

    Google Scholar 

  • Kartal Z, Hasgul S, Ernst AT (2017) Single allocation p-hub median location and routing problem with simultaneous pick-up and delivery. Transp Res Part E Logist Transp Rev 108:141–159

    Article  Google Scholar 

  • Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, vol 4, pp 1942–1948

  • Kratica J, Stanimirovic Z (2006) Solving the uncapacitated multiple allocation \(p\)-hub center problem by genetic algorithm. Asia Pac J Oper Res 23(04):425–437

    Article  Google Scholar 

  • Krause J, Cordeiro J, Parpinelli RS, Lopes HS (2013) A survey of swarm algorithms applied to discrete optimization problems. In: Yang XS, Cui Z, Xiao R, Gandomi AH, Karamanoglu M (eds) Swarm intelligence and bio-inspired computation. Elsevier, Oxford, pp 169–191

    Chapter  Google Scholar 

  • Li Z, Wang W, Yan Y, Li Z (2015) PSABC: a hybrid algorithm based on particle swarm and artificial bee colony for high-dimensional optimization problems. Expert Syst Appl 42(22):8881–8895

    Article  Google Scholar 

  • Lopes MC, de Andrade CE, de Queiroz TA, Resende MGC, Miyazawa FK (2016) Heuristics for a hub location-routing problem. Networks 68(1):54–90

    Article  Google Scholar 

  • Lourenco H, Martin O, Stutzle T (2003) Iterated local search. In: Glover F, Kochenberger G (eds) Handbook of metaheuristics. Springer, US, Berlin, pp 320–353

    Chapter  Google Scholar 

  • Lourenco H, Martin O, Stutzle T (2010) Iterated local search. In: Gendreau M, Potvin JY (eds) Handbook of metaheuristics. Springer, US, Berlin, pp 363–397

    Chapter  Google Scholar 

  • Marini F, Walczak B (2015) Particle swarm optimization (PSO). A tutorial. Chemom Intell Lab Syst 149(Part B):153–165

    Article  Google Scholar 

  • Nagy G, Salhi S (1998) The many-to-many location-routing problem. Top 6(2):261–275

    Article  Google Scholar 

  • Norouzi N, Sadegh-Amalnick M, Alinaghiyan M (2015) Evaluating of the particle swarm optimization in a periodic vehicle routing problem. Measurement 62:162–169

    Article  Google Scholar 

  • Osman IH (1993) Metastrategy simulated annealing and tabu search algorithms for the vehicle routing problem. Ann Oper Res 41(4):421–451

    Article  Google Scholar 

  • Pamuk F, Sepil C (2001) A solution to the hub center problem via a single-relocation algorithm with tabu search. IIE Trans 33(5):399–411

    Google Scholar 

  • Pan QK, Tasgetiren MF, Liang YC (2008) A discrete particle swarm optimization algorithm for the no-wait flowshop scheduling problem. Comput Oper Res 35(9):2807–2839

    Article  Google Scholar 

  • Randall M (2008) Solution approaches for the capacitated single allocation hub location problem using ant colony optimisation. Comput Optim Appl 39(2):239–261

    Article  Google Scholar 

  • Reimann M, Doerner K, Hartl RF (2004) D-ants: savings based ants divide and conquer the vehicle routing problem. Comput Oper Res 31(4):563–591

    Article  Google Scholar 

  • Rieck J, Ehrenberg C, Zimmermann J (2014) Many-to-many location-routing with inter-hub transport and multi-commodity pickup-and-delivery. Eur J Oper Res 236(3):863–878

    Article  Google Scholar 

  • Rodriguez-Martin I, Salazar-Gonzalez JJ, Yaman H (2014) A branch-and-cut algorithm for the hub location and routing problem. Comput Oper Res 50:161–174

    Article  Google Scholar 

  • Sasaki M, Campbell JF, Krishnamoorthy M, Ernst AT (2014) A stackelberg hub arc location model for a competitive environment. Comput Oper Res 47:27–41

    Article  Google Scholar 

  • Serper EZ, Alumur SA (2016) The design of capacitated intermodal hub networks with different vehicle types. Transp Res Part B Methodol 86:51–65

    Article  Google Scholar 

  • Shieh HL, Kuo CC, Chiang CM (2011) Modified particle swarm optimization algorithm with simulated annealing behavior and its numerical verification. Appl Math Comput 218(8):4365–4383

    Google Scholar 

  • Stutzle T (2006) Iterated local search for the quadratic assignment problem. Eur J Oper Res 174(3):1519–1539

    Article  Google Scholar 

  • Stutzle T, Hoos HH (2000) Max–min ant system. Future Gener Comput Syst 16(9):889–914

    Article  Google Scholar 

  • Sun JU (2013) An integrated hub location and multi-depot vehicle routing problem. Appl Mech Mater 409–410:1188–1192

    Google Scholar 

  • Sun Z, Zheng J (2016) Finding potential hub locations for liner shipping. Transp Res Part B Methodol 93(Part B):750–761

    Article  Google Scholar 

  • Tan PZ, Kara BY (2007) A hub covering model for cargo delivery systems. Networks 49(1):28–39

    Article  Google Scholar 

  • Tasgetiren MF, Liang YC, Sevkli M, Gencyilmaz G (2007) A particle swarm optimization algorithm for makespan and total flowtime minimization in the permutation flowshop sequencing problem. Eur J Oper Res 177(3):1930–1947

    Article  Google Scholar 

  • Tchomte SK, Gourgand M (2009) Particle swarm optimization: a study of particle displacement for solving continuous and combinatorial optimization problems. Int J Prod Econ 121(1):57–67

    Article  Google Scholar 

  • Ting CJ, Chen CH (2013) A multiple ant colony optimization algorithm for the capacitated location routing problem. Int J Prod Econ 141(1):34–44

    Article  Google Scholar 

  • Wasner M, Zapfel G (2004) An integrated multi-depot hub-location vehicle routing model for network planning of parcel service. Int J Prod Econ 90(3):403–419

    Article  Google Scholar 

  • Yaman H (2009) The hierarchical hub median problem with single assignment. Transp Res Part B Methodol 43(6):643–658

    Article  Google Scholar 

  • Yaman H, Kara BY, Tansel B (2007) The latest arrival hub location problem for cargo delivery systems with stopovers. Transp Res Part B 41(8):906–919

    Article  Google Scholar 

  • Yang K, Liu Y, Yang G (2013a) An improved hybrid particle swarm optimization algorithm for fuzzy \(p\)-hub center problem. Comput Ind Eng 64(1):133–142

    Article  Google Scholar 

  • Yang K, Liu YK, Yang GQ (2013b) Solving fuzzy \(p\)-hub center problem by genetic algorithm incorporating local search. Appl Soft Comput 13(5):2624–2632

    Article  Google Scholar 

Download references

Acknowledgements

The authors thank two anonymous referees and area editor, whose feedbacks helped us in improving the paper. The first author is supported from the Scientific and Technological Research Council of Turkey (TÜBİTAK).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zühal Kartal.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kartal, Z., Krishnamoorthy, M. & Ernst, A.T. Heuristic algorithms for the single allocation p-hub center problem with routing considerations. OR Spectrum 41, 99–145 (2019). https://doi.org/10.1007/s00291-018-0526-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00291-018-0526-2

Keywords

Navigation