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Adaptive memory programming for the many-to-many vehicle routing problem with cross-docking

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Abstract

This paper presents a new generalized vehicle routing problem with a cross-dock. Basic features of the examined problem are the many-to-many relationship between the suppliers and customers, and the use of different vehicle fleets for performing the inbound and outbound routes. An adaptive memory programming method has been developed coupled with a Tabu Search algorithm. For generating new provisional solutions, elite subroutes with varying lengths are identified from the reference solutions and are used as building blocks, while multiple strategies are applied to maintain an effective interplay between diversification and intensification. Various computational experiments are conducted on existing as well as on new data sets with diverse features, regarding the geographic distribution of the nodes and the density of supplier-customer links. Overall, the proposed method performed very well and new best solutions have been found. Lastly, new insights regarding the impact of split options are reported.

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Acknowledgements

This work was supported by the ΙΚΥ fellowships of excellence for postgraduate studies in Greece—Siemens program, as well as by the Research Center of the Athens University of Economics and Business (ΕΡ-2349-01). Support from the National Science Foundation under Award Number 1434432 is also gratefully acknowledged.

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Authors and Affiliations

  1. Department of Management Science and Technology, School of Business, Athens University of Economics and Business, Athens, Greece

    Amalia I. Nikolopoulou, Christos D. Tarantilis & Emmanouil E. Zachariadis

  2. Department of Marketing and Communication, School of Business, Athens University of Economics and Business, Athens, Greece

    Panagiotis P. Repoussis

  3. School of Business, Stevens Institute of Technology, Hoboken, 07030, NJ, USA

    Panagiotis P. Repoussis

Authors
  1. Amalia I. Nikolopoulou
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  2. Panagiotis P. Repoussis
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  3. Christos D. Tarantilis
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  4. Emmanouil E. Zachariadis
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Corresponding author

Correspondence to Christos D. Tarantilis.

Appendix

Appendix

Detailed solutions for all problem instances of the new benchmark data sets are reported in Tables 7, 8, 9 and 10. In particular, Tables 7 and 8 present the solutions obtained for the many-to-many VRPCD with no split options with up to 100 and 200 nodes, respectively. Note that the average results for these problem instances are reported in Table 4. Tables 9 and 10 present the solution obtained for the transformed VRPCD with collocated nodes and split options, with up to 100 and 200 nodes, respectively. In both cases the problem instances are grouped according to the geographic distribution class, supplier-customer density (ε) class, and supplier participation ratio (σ).

Table 7 Detailed solution scores for the many-to-many VRPCD data set
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Table 8 Detailed solutions for the many-to-many VRPCD data set
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Table 9 Detailed solutions for the transformed VRPCD data set with collocated nodes and split options (Data Set I)
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Table 10 Detailed solutions for the transformed VRPCD data set with collocated nodes and split options (Data Set II)
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Nikolopoulou, A.I., Repoussis, P.P., Tarantilis, C.D. et al. Adaptive memory programming for the many-to-many vehicle routing problem with cross-docking. Oper Res Int J 19, 1–38 (2019). https://doi.org/10.1007/s12351-016-0278-1

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  • DOI: https://doi.org/10.1007/s12351-016-0278-1

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