Abstract
Army fielding is the process by which new equipment is distributed to soldiers either at dispersed homeland or theatre of operations units. Fielding is followed by backhauling of used material to be replaced. Minimization of the use of transport media and time resourses is of utmost importance for improving the supply chain management system and equipment fielding operations. The present paper, addresses Vehicle Routing Problems with Backhauls and Time Windows (VRPBTW) with line-haul and backhaul military units in the context of military operations. The primary objective is the minimization of the required number of vehicles and the secondary objective is the minimization of the total cost of the routes. First a mixed integer programming formulation of the problem is given. Since the VRPBTW is NP-Hard, a metaheuristic algorithm is proposed for the solution. Initial solutions are produced through a tour construction heuristic scheme and evolve through a variation of the Threshold Accepting method, which is based on a special destruction-reconstruction scheme. The method has been tested on numerous problem instances with favorable results.
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
Brigantic, R.T., Mahan, J.M. (eds.): Defense Transportation—Algorithms Models and Applications for the 21st Century. Elsevier, Amsterdasss2004)
Carier, J.D.: The Army rapid fielding initiative. Syst. Inf. Eng. Des. Symp. IEEE. 1–4 (2007)
Cheung, R.K., Hang, D.D.: Multi-attribute label matching algorithms for vehicle routing problems with time windows and backhauls. IIE Trans. 35(3), 191–205 (2003)
Clark, S.J., Barnhart, C., Kolitz, S.E.: Large-scale optimization planning methods for the distribution of United States army munitions. Math & Comp Mod 39, 697–714 (2004)
Crino, J.A., Moore, J.T., Barnes, J.W., et al.: Solving the theater distribution vehicle routing and scheduling problem using group theoretic tabu search. Math & Comp Mod 39:599–416 (2004)
Dueck, G., Scheuer, T.: Threshold accepting: a general purpose optimization algorithm appearing superior to simulated annealing. J. Comput. Phys. 90, 161–175 (1990)
Duhamel, C., Potvin, J.Y., Rousseau, J.M.: A tabu search heuristic for the vehicle routing problem with backhauls and time windows. Transport Sci. 31, 49–59 (1997)
Gélinas, S., Desrochers, M., Desrosiers, J. Solomon, M.M.: A new branching strategy for time constrained routing problems with application to backhauling. Ann. Oper. Res. 61, 91–109 (1995)
Hasama, T., Kokubugata, H., Kawashima, H.: A heuristic approach based on the string model to solve vehicle routing problem with various conditions, Preprint for World Congress on Intelligent Transport Systems, No.3027, Toronto, Canada (1999)
Kaminski P.G.: The revolution in defense logistics, In Keynote Address of the USAD for Acquisition and Technology, Alexandria, VA (1995)
Kontoravdis, G., Bard, J.F.: Improved heuristics for the vehicle routing problem with time windows. Technical Report, Operations Research Group, Department of Mechanical Engineering, The University of Texas, Austin, USA (1992)
Mathworks: MatLab version (7.8.0347) R2009a, February 12, 2009, License number: 54126 (2009)
Nikolakopoulos, A., Sarimveis, H.: A threshold accepting heuristic with intense local search for the solution of special instances of the traveling salesman problem. Eur. J. Oper. Res. 177, 1911–1929 (2007)
O’Rourke, K.P.: Dynamic unmanned aerial vehicle (UAV) routing with a Java encoded reactive tabu search metaheuristic. Master’s Thesis, Air Force Institute of Technology, USA (1999)
Osman, I.H.: Metastrategy simulated annealing and tabu search algorithms for the vehicle routing problem. Ann. Oper. Res. 41, 421–451 (1993)
Potvin, J., Duhamel, C., Guertin, F.: A genetic algorithm for vehicle routing with backhauling. Appl. Intell. 6, 345–355 (1996)
Reimann M., Doerner, K., Hartl, R.F.: Insertion based ants for vehicle routing problems with backhauls and time windows. In: Dorigo, M., Caro G.Di., Sampels, M. (eds.) Ant Algorithms, pp. 135–147. Springer, Berlin (2002)
Ropke, S., Pisinger, D.: A unified heuristic for a large class of vehicle routing problems with Backhauls. Eur. J. Oper. Res. 171, 750–775 (2006)
Sarimveis, H., Nikolakopoulos, A.: A line up evolutionary algorithm for solving nonlinear constrained optimization problems. Comp. Oper. Res. 32, 1499–1514 (2005)
Solomon, M.: Algorithms for the vehicle routing and scheduling problem with time window constraints. Oper. Res. 35, 254–265 (1987)
Thangiah, S.R., Potvin, J.Y., Sun, T.: Heuristic approaches to vehicle routing with backhauls and time windows. Comp. Oper. Res. 23, (11) 1043–1057 (1996)
Wilkins, D.E., Smith, S.F., Kramer, L.A., et al.: Airlift mission monitoring and dynamic rescheduling. Math & Comp Mod 39, 141–155 (2004)
Zhong, Y., Cole, M.H.: A vehicle routing problem with backhauls and time windows: a guided local search solution. Transport. Res. E-Log. 41, 131–144 (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Nikolakopoulos, A. (2015). A Metaheuristic Reconstruction Algorithm for Solving Bi-level Vehicle Routing Problems with Backhauls for Army Rapid Fielding. In: Zeimpekis, V., Kaimakamis, G., Daras, N. (eds) Military Logistics. Operations Research/Computer Science Interfaces Series, vol 56. Springer, Cham. https://doi.org/10.1007/978-3-319-12075-1_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-12075-1_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12074-4
Online ISBN: 978-3-319-12075-1
eBook Packages: Business and EconomicsBusiness and Management (R0)