Abstract
We study an interactive-approach to the Inventory Routing Problem (IRP) with the goal of supporting the decision maker (DM). Combining the supply chain management aspects ‘inventory management’ and ‘transportation’ into a simultaneous model can lead to beneficial cost reductions for both the supplier and the customer. A preference model, namely the reference point, is introduced to elicit individual preference information of the experts. Then, a subsequent interactive-approach is developed to solve the dynamic IRP. The comparison of the interactive-approach with an a posteriori-approach shows the applicability and the achieved speedup of the focused search. We also consider an extended interactive-approach for the benchmark test instances that is meaningful in terms of including a reservation point as a ‘natural’ convergence criterion.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Barthélemy T, Geiger MJ, Sevaux M (2012) An extended solution representation for the biobjective inventory routing problem. In: Renatus F, Kunze R, Karschin I, Geldermann J, Fichtner W (eds) Entscheidungsunterstützung durch Operations Research im Energie- und Umweltbereich, Volume of the series operations research, Shaker Verlag, Aachen, pp 7–20
Bertazzi L, Speranza MG (2013) Inventory routing problems with multiple customers. EUR J Transp Logistics 2(3):255–275
Clarke G, Wright JW (1964) Scheduling of vehicles from a central depot to a number of delivery points. Oper Res 12(4):568–581
Coelho LC, Laporte G (2013) A branch-and-cut algorithm for the multi-product multi-vehicle inventory-routing problem. Int J Prod Res 51(23–24):1–14. url:http://dx.doi.org/10.1080/00207543.2012.757668
Coelho LC, Cordeau J-F, Laporte G (2014) Thirty years of inventory-routing.Transport Sci 48(1):1–19
Deb K, Kumar A (2007) Light beam search based multi-objective optimization using evolutionary algorithms. IEEE congress on evolutionary computation, CEC 2007. pp 2125–2132
Eppe S, López-Ibáñez M, Stützle T, de Smet Y (2011) An experimental study of preference model integration into multi-objective optimization heuristics. IEEE congress on evolutionary computation, CEC 2011, New Orleans, pp 2751–2758
Figueira JR, Liefooghe A, Talbi E-G, Wierzbicki AP (2010) A parallel multiple reference point approach for multi-objective optimization. Eur J Oper Res 205(2):390–400
Geiger MJ, Sevaux S (2011) The biobjective inventory routing problem—problem solution and decision support. In: Pahl J, Reiners T, Voß S (eds) Network optimization, Lecture notes in computer science, vol 6701. Springer, Berlin, pp 365–378
Gendreau M, Potvin J-Y, Bräysy O, Hasle G, LØkketangen A (2008) Metaheuristics for the vehicle routing problem and its extensions: a categorized bibliography. In: Golden B, Raghavan S, Wasil E (eds) The vehicle routing problem: latest advances and new challenges, operations research/computer science interfaces, vol 43. Springer, Berlin, pp 143–169
Hwang C-L, Masud ASMd. (1979) Multiple objective decision making—methods and applications. Lecture notes in economics and mathematical systems, vol 164, Springer, Berlin
Kohl N, Desrosiers J, Madsen OBG, Solomon MM, Soumis F (2011) 2-Path cuts for the vehicle routing problem with time windows. Transp Sci 33(1):101–116
Li F, Golden B, Wasil E (2007) A record-to-record algorithm for solving the heterogeneous fleet vehicle routing problem. Comput Oper Res 34(9):2734–2742
Nikulin Y, Miettinen K, Mäkelä MM (2012) A new achievement scalarizing function based on parameterization in multiobjective optimization. OR Spectrum 34(1):69–87
Popović D, Vidović M, Radivojević G (2012) Variable neighborhood search heuristic for the inventory routing problem in fuel delivery. Expert Syst Appl 39(18):13390–13398
Range TM (2007) Exact solution of resource constrained routing problems using branch-price-and-cut. University Press of Southern Denmark, Denmark
Wierzbicki AP (1980) The use of reference objectives in multiobjective optimization. In: Fandel G, Gal T (eds) Multiple criteria decision making, theory and Application, Lecture notes in economics and mathematical systems 177. Springer, Berlin, pp 468–486
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Huber, S., Geiger, M.J., Sevaux, M. (2015). Interactive Approach to the Inventory Routing Problem: Computational Speedup Through Focused Search. In: Dethloff, J., Haasis, HD., Kopfer, H., Kotzab, H., Schönberger, J. (eds) Logistics Management. Lecture Notes in Logistics. Springer, Cham. https://doi.org/10.1007/978-3-319-13177-1_27
Download citation
DOI: https://doi.org/10.1007/978-3-319-13177-1_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13176-4
Online ISBN: 978-3-319-13177-1
eBook Packages: EngineeringEngineering (R0)