Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Hub network design problem in the presence of disruptions

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

The main issue in p-hub median problem is locating hub facilities and allocating spokes to those hubs in order to minimize the total transportation cost. However hub facilities may fail occasionally due to some disruptions which could lead to excessive costs. One of the most effective ways to hedge against disruptions especially intentional disruptions is designing more reliable hub networks. In this paper, we formulate the multiple allocation p-hub median problem under intentional disruptions by a bi-level model with two objective functions at the upper level and a single objective at the lower level. In this model, the leader aims at identifying the location of hubs so that minimize normal and worst-case transportation costs. Worst-case scenario is modeled in the lower level where the follower’s objective is to identify the hubs that if lost, it would mostly increase the transportation cost. We develop two multi-objective metaheuristics based on simulated annealing and tabu search to solve the problem. Computational results indicate the viability and effectiveness of the proposed algorithms for exploring the non-dominated solutions.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • Abo-Sinna, M. A., & Baky, I. A. (2007). Interactive balance space approach for solving multi-level multi-objective programming problems. Information Sciences, 177, 3397–3410.

    Article  Google Scholar 

  • Alumur, S. A., & Kara, B. Y. (2008). Network hub location problems: The state of the art. European Journal of Operational Research, 190, 1–21.

    Article  Google Scholar 

  • Azad, N., Saharidis, G. K. D., Davoudpour, H., Malekly, H., Yektamaram, S. A. (2012). Strategies for protecting supply chain networks against facility and transportation disruptions: An improved Benders decomposition approach. Annals of operations research, doi:10.1007/s10479-012-1146-x (Article in press).

  • Baky, I. A. (2009). Fuzzy goal programming algorithm for solving decentralized bi-level multi-objective programming problems. Fuzzy sets and systems, 160, 2701–2713.

    Article  Google Scholar 

  • Bandyopadhyay, S., Saha, S., Maulik, U., & Deb, K. (2008). A simulated annealing-based multiobjective optimization algorithm: AMOSA. IEEE Transactions on Evolutionary Computation, 12, 269–283.

    Article  Google Scholar 

  • Bard, J. F. (1998). Practical bilevel optimization: Algorithms and applications. Dordrecht: Kluwer.

    Book  Google Scholar 

  • Baykasoğlu, A. (2006). Multi-rule multi-objective simulated annealing algorithm for straight and U type assembly line balancing problems. Journal of Intelligent Manufacturing, 17, 217–232.

    Google Scholar 

  • Baykasoğlu, A., & özbakir, L., Sönmez A. I. (2004). Using multiple objective tabu search and grammars to model and solve multi-objective flexible job shop scheduling problems. Journal of Intelligent Manufacturing, 15, 777–785.

    Google Scholar 

  • Beasley, J. E. (1990). OR-Library, Obtaining test problems via Internet, Downloadable from website http://people.brunel.ac.uk/~mastjjb/jeb/info.html.

  • Ben-Ayed, O., Boyce, D. E., & Blair, C. E. (1988). A general bilevel programming formulation of the network design problem. Transportation Research part B, 22, 311–318.

    Article  Google Scholar 

  • Berman, O., Drezner, T., Drezner, Z., & Wesolowsky, G. O. (2009a) A defensive maximal covering problem on a network. International Transaction in Operational Research, 16, 69–86.

    Google Scholar 

  • Berman, O., Drezner, Z., & Wesolowsky, G. O. (2003). Locating service facilities whose reliability is distance dependent. Computers and Operations Research, 30, 1683–1695.

    Article  Google Scholar 

  • Berman, O., Ianovsky, E., & Krass, D. (2011). Optimal search path for service in the presence of disruptions. Computers and Operations Research, 38, 1562–1571.

    Article  Google Scholar 

  • Berman, O., Krass, D., & Menezes, M. B. C. (2007). Facility reliability issues in network p-median problems: Strategic centralization and co-location effects. Operations Research, 55, 332–350.

    Article  Google Scholar 

  • Berman, O., Krass, D., & Menezes, M. B. C. (2009b). Locating facilities in the presence of disruptions and incomplete information. Decision Sciences, 40, 845–868.

    Google Scholar 

  • Campbell, J. F. (1992). Location and allocation for distribution systems with transshipments and transportation economies of scale. Annals of Operations Research, 40, 77–99.

    Article  Google Scholar 

  • Campbell, J. F. (1994). Integer programming formulations of discrete hub location problems. European Journal of Operational Research, 72, 387–405.

    Article  Google Scholar 

  • Chen, Q., Li, X., & Ouyang, Y. (2011). Joint inventory-location problem under the risk of probabilistic facility disruptions. Transportation Research Part B, 45, 991–1003.

    Article  Google Scholar 

  • Church, R. L., Scaparra, M. P., & Middleton, R. S. (2004). Identifying critical infrastructure: The median and covering facility interdiction problems. Annals of the Association of American Geographers, 94, 491–502.

    Article  Google Scholar 

  • Cui, T., Ouyang, Y., & Shen, Z. J. (2010). Reliable facility location design under the risk of disruptions. Operations Research, 58, 998–1011.

    Article  Google Scholar 

  • Czyzzak, P., & Jaszkiewicz, A. (1998). Pareto simulated annealing—a metaheuristic technique for multiple-objective combinatorial optimization. Journal of Multi-Criteria Decision Analysis, 7, 34–37.

    Google Scholar 

  • Drezner, Z. (1987). Heuristic solution methods for two location problems with unreliable facilities. Journal of the Operational Research Society, 38, 509–514.

    Article  Google Scholar 

  • Drezner, Z., & Hamacher, H. W. (2004). Facility location:Applications and theory. Berlin: Springer.

    Google Scholar 

  • Elhedhli, S., & Hu, F. X. (2005). Hub-and-spoke network design with congestion. Computers and Operations Research, 32, 1615–1632.

    Article  Google Scholar 

  • Erkut, E., & Gzara, F. (2008). Solving the hazmat transport network design problem. Computers and Operations Research, 35, 2234–2247.

    Article  Google Scholar 

  • Ernst, A. T., & Krishnamoorthy, M. (1998). An exact solution approach based on shortest-paths for p-hub median problems. Informs Journal on Computing, 10, 149–162.

    Article  Google Scholar 

  • Fonseca, C. M., & Fleming, P. J. (1995). An overview of evolutionary algorithms in multiobjective optimization. Evolutionary Computation, 3, 1–16.

    Article  Google Scholar 

  • Gholami, M., & Zandieh, M. (2009). Integrating simulation and genetic algorithm to schedule a dynamic flexible job shop. Journal of Intelligent Manufacturing, 20, 481–498.

    Article  Google Scholar 

  • Glover, F. (1986). Future paths for integer programming and links to artificial intelligence. Computers and Operational Research, 13, 533–549.

    Google Scholar 

  • Hamacher, H. W., Labbé, M., Nickel, S., & Sonneborn, T. (2004). Adapting polyhedral properties from facility to hub location Problems. Discrete Applied Mathematics, 145(1), 104–116.

    Article  Google Scholar 

  • Hecheng, L., & Yuping, W. (2008). Exponential distribution-based genetic algorithm for solving mixed-integer bilevel programming problems. Journal of Systems Engineering and Electronics, 19, 1157–1164.

    Article  Google Scholar 

  • Hejazi, S. R., Memarian, A. L., Jahanshahloo, G., & Sepehri, M. M. (2002). Linear bi-level programming solution by genetic algorithm. Computers & Operations Research, 29, 1913–1925.

    Article  Google Scholar 

  • Hong, J., Xie, Y., & Jeong, K. (2012). Development and evaluation of an integrated emergency response facility location model. Journal of Industrial Engineering and Management, 5, 4–21.

    Article  Google Scholar 

  • Ishibuchi, H., Yoshida, T., & Murata, T. (2003). Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling. IEEE Transactions on Evolutionary Computation, 7, 204–223.

    Article  Google Scholar 

  • Jabbarzadeh, A., Jalali Naini, S. G., Davoudpour, H., Azad, N. (2012). Designing a supply Chain network under the risk of disruptions. Mathematical Problems in Engineering, 2012, art. no. 234324, 1–23.

  • Jaeggi, D. M., Parks, G. T., Kipouros, T., & Clarkson, P. J. (2008). The development of a multi-objective tabu search algorithm for continuous optimisation problems. European Journal of Operational Research, 185, 1192–1212.

    Article  Google Scholar 

  • Kim, H., & O’Kelly, M. E. (2009). Reliable p-hub location problems in telecommunication networks. Geographical Analysis, 41, 283–306.

    Article  Google Scholar 

  • Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220, 671–680.

    Article  Google Scholar 

  • Knowles, J. (2006). ParEGO: A hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems. IEEE Transactions on Evolutionary Computation, 10, 50–66.

    Google Scholar 

  • Kristianto, Y., Helo, P., Jiao, R. J. (2012). Mass customization design of engineer-to-order products using Benders’ decomposition and bi-level stochastic programming. Journal of Intelligent Manufacturing, doi: 10.1007/s10845-012-0692-z (Article in press).

  • Kulturel-Konak, S., Smith, A. E., & Norman, B. A. (2006). Multi-objective tabu search using a multinomial probability mass function. European Journal of Operational Research, 169, 918–931.

    Article  Google Scholar 

  • Kuo, R. J., & Huang, C. C. (2009). Application of particle swarm optimization algorithm for solving bi-level linear programming problem. Computers & Mathematics with Applications, 58, 678–685.

    Article  Google Scholar 

  • Lan, K. M., Wen, U. P., Shih, H. S., & Lee, E. S. (2007). A hybrid neural network approach to bilevel programming problems. Applied Mathematics Letters, 20, 880–884.

    Article  Google Scholar 

  • Lei, T. L., Tong, D. (2012). Hedging against service disruptions: an expected median location problem with site-dependent failure probabilities. Journal of Geographical Systems, doi:10.1007/s10109-012-0175-y (Article in press).

  • Li, J., Pan, Q., & Liang, Y. (2010). An effective hybrid tabu search algorithm for multi-objective flexible job-shop scheduling problems. Computers and Industrial Engineering, 59, 647–662.

    Article  Google Scholar 

  • Li, X., & Ouyang, Y. (2010). A continuum approximation approach to reliable facility location design under correlated probabilistic disruptions. Transportation Research Part B, 44, 535–548.

    Article  Google Scholar 

  • Liberatore, F., & Scaparra, M. P. (2011). Optimizing protection strategies for supply chains: Comparing classic decision-making criteria in an uncertain environment. Annals of the Association of American Geographers, 101, 1241–1258.

    Article  Google Scholar 

  • Liberatore, F., Scaparra, M. P., & Daskin, M. S. (2011). Analysis of facility protection strategies against an uncertain number of attacks: The stochastic R-interdiction median problem with fortification. Computers and Operations Research, 38, 357–366.

    Article  Google Scholar 

  • Liberatore, F., Scaparra, M. P., & Daskin, M. S. (2012). Hedging against disruptions with ripple effects in location analysis. Omega, 40, 21–30.

    Article  Google Scholar 

  • Lim, M., Daskin, M. S., Bassamboo, A., & Chopra, S. (2010). A facility reliability problem: Formulation, properties, and algorithm. Naval Research Logistics, 57, 58–70.

    Google Scholar 

  • Lin, C. K. Y., & Kwok, R. C. W. (2006). Multi-objective metaheuristics for a location-routing problem with multiple use of vehicles on real data and simulated data. European Journal of Operational Research, 175, 1833–1849.

    Article  Google Scholar 

  • Liu, Q., & Xu, J. (2011). A study on facility location-allocation problem in mixed environment of randomness and fuzziness. Journal of Intelligent Manufacturing, 22, 389–398.

    Article  Google Scholar 

  • Losada, C., Scaparra, M. P., Church, R.L., Daskin, M. S. (2012). The stochastic interdiction median problem with disruption intensity levels. Annals of Operations Research, doi:10.1007/s10479-012-1170-x (Article in press).

  • Losada, C., Scaparra, M. P., & O’Hanley, J. R. (2012). Optimizing system resilience: A facility protection model with recovery time. European Journal of Operational Research, 217, 519– 530.

    Article  Google Scholar 

  • Marjani, M. R., Moattar Husseini, S. M., & Karimi, B. (2011). Bi-objective heuristics for multi-item freights distribution planning problem in crossdocking networks. International Journal of Advanced Manufacturing, 58, 1201–1216.

    Article  Google Scholar 

  • Mersha, A. G., & Dempe, S. (2006). Linear bilevel programming with upper level constraints depending on the lower level solution. Applied Mathematics and Computation, 180, 247–254.

    Article  Google Scholar 

  • Metropolis, N., Rosenbluth, A., Rosenbluth, M., Teller, A., & Teller, E. (1953). Equation of state calculations by fast computing machines. Journal of Chemical Physics, 21, 1087–1092.

    Article  Google Scholar 

  • O’Hanley, J. R., & Church, R. L. (2011). Designing robust coverage networks to hedge against worst-case facility losses. European Journal of Operational Research, 209, 23–36.

    Article  Google Scholar 

  • Pasandideh, S. H. R., Niaki, S. T. A., Hajipour, V. (2011). A multi-objective facility location model with batch arrivals: two parameter-tuned meta-heuristic algorithms. Journal of Intelligent Manufacturing, doi:10.1007/s10845-011-0592-7 (Article in press).

  • Peng, P., Snyder, L. V., Lim, A., & Liu, Z. (2011). Reliable logistics networks design with facility disruptions. Transportation Research (B), 45, 1190–1211.

    Article  Google Scholar 

  • Roghanian, E., Sadjadi, S. J., & Aryanezhad, M. B. (2007). A probabilistic bi-level linear multi-objective programming problem to supply chain planning. Applied Mathematics and computation, 188, 786–800.

    Article  Google Scholar 

  • Safaei, N., Banjevic, D., & Jardine, A. K. S. (2012). Multi-threaded simulated annealing for a bi-objective maintenance scheduling problem. International Journal of Production Research, 50, 63–80.

    Article  Google Scholar 

  • Saharidis, G. K., & Ierapetritou, M. G. (2009). Resolution method for mixed integer bi-level linear problems based on decomposition technique. Journal of Global Optimization, 44, 29–51.

    Article  Google Scholar 

  • Scaparra, M. P., & Church, R. L. (2008). A bilevel mixed-integer program for critical infrastructure protection planning. Computers and Operations Research, 35, 1905–1923.

    Article  Google Scholar 

  • Shi, X., & Xia, H. (2001). Model and interactive algorithm of bi-level multi-objective decision-making with multiple interconnected decision makers. Journal of Multi-Criteria Decision Analysis, 10, 27–34.

    Google Scholar 

  • Snyder, L. V., & Daskin, M. S. (2005). Reliability models for facility location: The expected failure cost case. Transportation Science, 39, 400–416.

    Article  Google Scholar 

  • Snyder, L. V., & Daskin, M. S. (2006). Stochastic p-robust location problems. IIE Transactions, 38, 971–985.

    Article  Google Scholar 

  • Snyder, L. V., Scaparra, M. P., Daskin, M. S., Church, R. L. (2006). Planning for disruptions in supply chain networks, In Tutorials in Operations Research INFORMS, 234–257.

  • Sohn, J., & Park, S. (1998). Efficient Solution procedure and reduced size formulations for p-hub location problems. European Journal of Operational Research, 108, 118–126.

    Article  Google Scholar 

  • Suman, B., & Kumar, P. (2006). A survey of simulated annealing as a tool for single and multiobjective optimization. Journal of the Operational Research Society, 57, 1143–1160.

    Article  Google Scholar 

  • Suppapitnarm, A., Seffen, K. A., Parks, G. T., & Clarkson, P. J. (2000). A simulated annealing algorithm for multiobjective optimization. Engineering Optimization, 33, 59–85.

    Article  Google Scholar 

  • Viana, A., & Pinho de Sousa, J. (2000). Using metaheuristics in multiobjective resource constrained project scheduling. European Journal of Operational Research, 120, 359–374.

    Article  Google Scholar 

  • Wagner, S. M., & Neshat, N. (2010). Assessing the vulnerability of supply chains using graph theory. International Journal of Production Economics, 126, 121–129.

    Article  Google Scholar 

  • Wu, T., Blackhurst, J., & O’grady, P. (2007). Methodology for supply chain disruption analysis. International Journal of Production Research, 45, 1665–1682.

    Article  Google Scholar 

  • Yin, Y. (2002). Multiobjective bilevel optimization for transportation planning and management problems. Journal of Advanced Transportation, 36, 93–105.

    Google Scholar 

  • Zanjirani Farahani, R., & Hekmatfar, M. (2009). Facility location: Concepts, models, algorithms and case studies. Berlin: Springer.

  • Zhang, G., Lu, J., & Dillon, T. (2007). Decentralized multi-objective bilevel decision making with fuzzy demands. Knowledge-Based Systems, 20, 495–507.

    Google Scholar 

  • Zitzler, E., & Thiele, L. (1999). Multiobjective evolutionary algorithms: A comparative case study and the strength pareto approach. IEEE Transactions on Evolutionary Computation, 3, 257–271.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Industrial Engineering and Management Systems, Amirkabir University of Technology, 424 Hafez Avenue, Tehran, Iran

    F. Parvaresh, S.M. Moattar Husseini & B. Karimi

  2. Department of Computer Engineering and Information Technology, Amirkabir University of Technology, 424 Hafez Avenue, Tehran, Iran

    S.A. Hashemi Golpayegany

Authors
  1. F. Parvaresh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S.M. Moattar Husseini
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. S.A. Hashemi Golpayegany
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. B. Karimi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S.M. Moattar Husseini.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Parvaresh, F., Husseini, S.M., Golpayegany, S.H. et al. Hub network design problem in the presence of disruptions. J Intell Manuf 25, 755–774 (2014). https://doi.org/10.1007/s10845-012-0717-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-012-0717-7

Keywords

Search

Navigation