Proceedings of ESCAPE 36ISSN: 2818-4734
Volume: 5 (2026)
Table of Contents
LAPSE:2026.0497
Published Article
LAPSE:2026.0497
A novel decomposition-based approach to solve heterogeneous capacitated vehicle routing problems
Vakil Vamsi Krishna, Mangesh Kapadi, Pankaj Verma, Shamik Misra
June 12, 2026
Abstract
The Heterogeneous Capacitated Vehicle Routing Problem (HCVRP) is a fundamental extension of the classical Vehicle Routing Problem in which customer demands must be satisfied using a fleet of vehicles with varying capacities and costs. In this paper, a novel and intuitive decomposition-based formulation for HCVRP is presented that decomposes the problem into two tractable subproblems: (i) a route generation and an optimal customer sequencing problem and (ii) a vehicle route assignment problem. In the first stage, all feasible customer combinations are constructed as routes, and for each route an optimization problem is solved to identify the optimal customer sequence that results in the minimum distance travelled. In the second stage, the optimal routes are selected, and vehicles are assigned using a mixed integer linear programming (MILP) formulation that minimizes the fixed cost of vehicle utilisation and total transportation costs, ensuring demand satisfaction for all customers while meeting vehicle capacity limitations. Computational results demonstrate that the decomposition strategy preserves optimality while significantly reducing solution time.
Keywords
Decomposition, Mixed integer linear programming, Optimization, Vehicle Routing
Suggested Citation
Krishna VV, Kapadi M, Verma P, Misra S. A novel decomposition-based approach to solve heterogeneous capacitated vehicle routing problems. Systems and Control Transactions 5:2355-2361 (2026) https://doi.org/10.69997/sct.157285
Author Affiliations
Krishna VV: Department of Chemical engineering, Indian Institute of Technology Tirupati, Andhra Pradesh, India - 517609 [ORCID]
Kapadi M: Linde South Asia Services Limited, Pune, Maharashtra, India - 411045
Verma P: Linde South Asia Services Limited, Pune, Maharashtra, India - 411045
Misra S: Department of Chemical engineering, Indian Institute of Technology Tirupati, Andhra Pradesh, India - 517609 [ORCID]
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Journal Name
Systems and Control Transactions
Volume
5
First Page
2355
Last Page
2361
Year
2026
Publication Date
2026-06-12
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Original Submission
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PII: 2355-2361-657-SCT-5-2026, Publication Type: Journal Article
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References Cited
  1. Dantzig GB, Ramser JH. The truck dispatching problem. Management Science 6:80-91 (1959) https://doi.org/10.1287/mnsc.6.1.80
  2. Baldacci R, Toth P, Vigo D. Exact algorithms for routing problems under vehicle capacity constraints. Ann Oper Res 175:213-245 (2009) https://doi.org/10.1007/s10479-009-0650-0
  3. Fitzpatrick J, Ajwani D, Carroll P. A scalable learning approach for the capacitated vehicle routing problem. Computers & Operations Research 171:106787 (2024) https://doi.org/10.1016/j.cor.2024.106787
  4. Koç Ç, Bekta? T, Jabali O, Laporte G. Thirty years of heterogeneous vehicle routing. European Journal of Operational Research 249:1-21 (2016) https://doi.org/10.1016/j.ejor.2015.07.020
  5. Golden B, Assad A, Levy L, Gheysens F. The fleet size and mix vehicle routing problem. Computers & Operations Research 11:49-66 (1984) https://doi.org/10.1016/0305-0548(84)90007-8
  6. Baldacci R, Toth P, Vigo D. Exact algorithms for routing problems under vehicle capacity constraints. Ann Oper Res 175:213-245 (2009) https://doi.org/10.1007/s10479-009-0650-0
  7. Koç Ç, Bekta? T, Jabali O, Laporte G. Thirty years of heterogeneous vehicle routing. European Journal of Operational Research 249:1-21 (2016) https://doi.org/10.1016/j.ejor.2015.07.020
  8. Irnich S, Schneider M, Vigo D. Chapter 9: four variants of the vehicle routing problem. Vehicle Routing :241-271 (2014) https://doi.org/10.1137/1.9781611973594.ch9
  9. Hoff A, Andersson H, Christiansen M, Hasle G, Løkketangen A. Industrial aspects and literature survey: fleet composition and routing. Computers & Operations Research 37:2041-2061 (2010) https://doi.org/10.1016/j.cor.2010.03.015
  10. Golden B, Assad A, Levy L, Gheysens F. The fleet size and mix vehicle routing problem. Computers & Operations Research 11:49-66 (1984) https://doi.org/10.1016/0305-0548(84)90007-8
  11. Laporte G. Fifty years of vehicle routing. Transportation Science 43:408-416 (2009) https://doi.org/10.1287/trsc.1090.0301
  12. Golden B, Wang X, Wasil E. The evolution of the vehicle routing problem-a survey of VRP research and practice from 2005 to 2022. Synthesis Lectures on Operations Research and Applications :1-64 (2023) https://doi.org/10.1007/978-3-031-18716-2_1
  13. Archetti C, Coelho LC, Speranza MG, Vansteenwegen P. Beyond fifty years of vehicle routing: insights into the history and the future. European Journal of Operational Research 330:355-372 (2026) https://doi.org/10.1016/j.ejor.2025.06.014
  14. El-Sherbeny NA. Vehicle routing with time windows: an overview of exact, heuristic and metaheuristic methods. Journal of King Saud University - Science 22:123-131 (2010) https://doi.org/10.1016/j.jksus.2010.03.002
  15. Hildebrandt FD, Thomas BW, Ulmer MW. Opportunities for reinforcement learning in stochastic dynamic vehicle routing. Computers & Operations Research 150:106071 (2023) https://doi.org/10.1016/j.cor.2022.106071
  16. Morabit M, Desaulniers G, Lodi A. Learning to repeatedly solve routing problems. Networks 83:503-526 (2023) https://doi.org/10.1002/net.22200
  17. Guan Q, Cao H, Tan J, Ou B, Wang Y, Yan X, Chen B. Dynamic-aware deep reinforcement learning for heterogeneous fleet-based capacitated electric vehicle routing problems. Applied Energy 413:127757 (2026) https://doi.org/10.1016/j.apenergy.2026.127757
  18. Yaman H. Formulations and valid inequalities for the heterogeneous vehicle routing problem. Math. Program. 106:365-390 (2005) https://doi.org/10.1007/s10107-005-0611-6
  19. Miller CE, Tucker AW, Zemlin RA. Integer programming formulation of traveling salesman problems. J. ACM 7:326-329 (1960) https://doi.org/10.1145/321043.321046
  20. Baldacci R, Battarra M, Vigo D. Valid inequalities for the fleet size and mix vehicle routing problem with fixed costs. Networks 54:178-189 (2009) https://doi.org/10.1002/net.20331
  21. Pessoa A, Uchoa E, Poggi de Aragão M. A robust branch?cut?and?price algorithm for the heterogeneous fleet vehicle routing problem. Networks 54:167-177 (2009) https://doi.org/10.1002/net.20330
  22. Fukasawa R, Longo H, Lysgaard J, Aragão MP, Reis M, Uchoa E, Werneck RF. Robust branch-and-cut-and-price for the capacitated vehicle routing problem. Math. Program. 106:491-511 (2005) https://doi.org/10.1007/s10107-005-0644-x
  23. Pessoa A, Sadykov R, Uchoa E, Vanderbeck F. A generic exact solver for vehicle routing and related problems. Math. Program. 183:483-523 (2020) https://doi.org/10.1007/s10107-020-01523-z
  24. Pessoa A, Sadykov R, Uchoa E. Enhanced branch-cut-and-price algorithm for heterogeneous fleet vehicle routing problems. European Journal of Operational Research 270:530-543 (2018) https://doi.org/10.1016/j.ejor.2018.04.009
  25. Pecin D, Pessoa A, Poggi M, Uchoa E. Improved branch-cut-and-price for capacitated vehicle routing. Math. Prog. Comp. 9:61-100 (2016) https://doi.org/10.1007/s12532-016-0108-8
  26. N. Errami, E. Queiroga, R. Sadykov, and E. Uchoa, "VRPSolverEasy: A Python Library for the Exact Solution of a Rich Vehicle Routing Problem, " INFORMS J. Comput., vol. 36, no. 4, 2024, doi: 10.1287/ijoc.2023.0103.
  27. Fitzpatrick J, Ajwani D, Carroll P. A scalable learning approach for the capacitated vehicle routing problem. Computers & Operations Research 171:106787 (2024) https://doi.org/10.1016/j.cor.2024.106787
  28. Balinski ML, Quandt RE. On an integer program for a delivery problem. Operations Research 12:300-304 (1964) https://doi.org/10.1287/opre.12.2.300
  29. Uchoa E, Pecin D, Pessoa A, Poggi M, Vidal T, Subramanian A. New benchmark instances for the capacitated vehicle routing problem. European Journal of Operational Research 257:845-858 (2017) https://doi.org/10.1016/j.ejor.2016.08.012
  30. C. Guéret, C. Prins, M. Sevaux, and S. Heipcke, Applications of Optimization with Xpress-MP. Dash Optimization Limited, 2002. [Online]. Available: https://books.google.co.in/books?id=2hDrPQAACAAJ
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