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Adaptive GVNS Heuristics for Solving the Pollution Location Inventory Routing Problem

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Learning and Intelligent Optimization (LION 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11968))

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Abstract

This work proposes Adaptive General Variable Neighborhood Search metaheuristic algorithms for the efficient solution of Pollution Location Inventory Routing Problems (PLIRPs). A comparative computational study, between the proposed methods and their corresponding classic General Variable Neighborhood Search versions, illustrates the effectiveness of the intelligent mechanism used for automating the re-ordering of the local search operators in the improvement step of each optimization method. Results on 20 PLIRP benchmark instances show the efficiency of the proposed metaheuristics.

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References

  1. Antoniadis, N., Sifaleras, A.: A hybrid CPU-GPU parallelization scheme of variable neighborhood search for inventory optimization problems. Electron. Notes Discrete Math. 58, 47–54 (2017)

    Article  MathSciNet  Google Scholar 

  2. Cheng, C., Yang, P., Qi, M., Rousseau, L.M.: Modeling a green inventory routing problem with a heterogeneous fleet. Transp. Res. Part E 97, 97–112 (2017)

    Article  Google Scholar 

  3. Glover, F., Gutin, G., Yeo, A., Zverovich, A.: Construction heuristics for the asymmetric TSP. Eur. J. Oper. Res. 129, 555–568 (2001)

    Article  MathSciNet  Google Scholar 

  4. Hansen, P., Mladenovic, N.: Variable neighborhood search. In: Burke, E., Kendall, G. (eds.) Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques, chap. 12, pp. 313–337. Springer, New York (2014). https://doi.org/10.1007/978-1-4614-6940-7

    Google Scholar 

  5. Hansen, P., Mladenovic, N., Todosijevic, R., Hanafi, S.: Variable neighborhood search: basics and variants. EURO J. Comput. Optim. 5, 423–454 (2017)

    Article  MathSciNet  Google Scholar 

  6. Huber, S., Geiger, M.: Order matters - a variable neighborhood search for the swap-body vehicle routing problem. Eur. J. Oper. Res. 263, 419–445 (2017)

    Article  MathSciNet  Google Scholar 

  7. Karakostas, P., Sifaleras, A., Georgiadis, M.C.: Basic VNS algorithms for solving the pollution location inventory routing problem. In: Sifaleras, A., Salhi, S., Brimberg, J. (eds.) ICVNS 2018. LNCS, vol. 11328, pp. 64–76. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-15843-9_6

    Chapter  Google Scholar 

  8. Karakostas, P., Sifaleras, A., Georgiadis, C.: A general variable neighborhood search-based solution approach for the location-inventory-routing problem with distribution outsourcing. Comput. Chem. Eng. 126, 263–279 (2019)

    Article  Google Scholar 

  9. Li, K., Tian, H.: A two-level self-adaptive variable neighborhood search algorithm for the prize-collecting vehicle routing problem. Appl. Soft Comput. 43, 469–479 (2016)

    Article  Google Scholar 

  10. Sifaleras, A., Konstantaras, I.: General variable neighborhood search for the multi-product dynamic lot sizing problem in closed-loop supply chain. Electron. Notes Discrete Math. 47, 69–76 (2015)

    Article  MathSciNet  Google Scholar 

  11. Skouri, K., Sifaleras, A., Konstantaras, I.: Open problems in green supply chain modeling and optimization with carbon emission targets. In: Pardalos, P.M., Migdalas, A. (eds.) Open Problems in Optimization and Data Analysis. SOIA, vol. 141, pp. 83–90. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-99142-9_6

    Chapter  MATH  Google Scholar 

  12. Todosijevic, R., Mladenovic, M., Hanafi, S., Mladenovic, N., Crevits, I.: Adaptive general variable neighborhood search heuristics for solving the unit commitment problem. Electr. Power Energy Syst. 78, 873–883 (2016)

    Article  Google Scholar 

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Acknowledgement

The second author has been funded by the University of Macedonia Research Committee as part of the “Principal Research 2019” funding scheme (ID 81307).

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Authors and Affiliations

  1. Department of Chemical Engineering, Aristotle University of Thessaloniki, University Campus, 54124, Thessaloniki, Greece

    Panagiotis Karakostas & Michael C. Georgiadis

  2. Department of Applied Informatics, School of Information Sciences, University of Macedonia, 156 Egnatia Str., 54636, Thessaloniki, Greece

    Angelo Sifaleras

Authors
  1. Panagiotis Karakostas
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  2. Angelo Sifaleras
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  3. Michael C. Georgiadis
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Corresponding author

Correspondence to Michael C. Georgiadis .

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Editors and Affiliations

  1. Technical University of Crete, Chania, Greece

    Nikolaos F. Matsatsinis

  2. Technical University of Crete, Chania, Greece

    Yannis Marinakis

  3. Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA

    Panos Pardalos

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Karakostas, P., Sifaleras, A., Georgiadis, M.C. (2020). Adaptive GVNS Heuristics for Solving the Pollution Location Inventory Routing Problem. In: Matsatsinis, N., Marinakis, Y., Pardalos, P. (eds) Learning and Intelligent Optimization. LION 2019. Lecture Notes in Computer Science(), vol 11968. Springer, Cham. https://doi.org/10.1007/978-3-030-38629-0_13

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