Abstract
We address the freight consolidation problem under the context of road transportation with outsourced fleet, motivated by the real-life situation faced by a major manufacturer of school supplies located in Brazil. Given a set of shipments scheduled for the next few days, and a set of available vehicles at the carriers, the company has to determine how to best assign shipments to vehicles in a way to minimize the total transportation cost. This is a challenging case of vehicle consolidation, in which each carrier has a complex pricing table that follows particular rules, rates and taxes. Prices are defined according to the vehicle type (heterogeneous fleet), number of deliveries (visits to redispatching points) and the individual price and weight of items in the shipments consolidated in the truck. These components cause a piecewise linear behavior of the cost function, which makes the consolidation even more difficult. To aid this decision-making process, we propose a mixed-integer linear programming (MIP) model that fully represents the problem. We are not aware of any other model or solution strategy that includes all the features observed in the addressed situation. The results of computational experiments using real-life instances provided by the manufacturer show the benefits of using the proposed model in practice, as we observed reductions of more than 44% in comparison to the freight consolidation policy of the company.
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
Ahuja, R., Magnanti, T.L., Orlin, J.B.: Network Flows: Theory, Algorithms and Applications. Rentice-Hall, New Jersey (1993)
Croxton, K.L., Gendron, B., Magnanti, T.L.: A comparison of mixed-integer programming models for nonconvex piecewise linear cost minimization problems. Management Science 49(9), 1268–1273 (2003a)
Croxton, K.L., Gendron, B., Magnanti, T.L.: Models and methods for merge-in-transit operations. Transportation Science 37(1), 1–22 (2003b)
Cruz, C.A., Munari, P., Morabito, R.: A branch-and-price method for the vehicle allocation problem. Comput. Ind. Eng. 149, 106745 (2020)
Cunha, C.B., Silva, M.R.: A genetic algorithm for the problem of configuring a hub-and-spoke network for a ltl trucking company in brazil. Eur. J. Oper. Res. 179(3), 747–758 (2007)
Dror, M., Hartman, B.C.: Shipment consolidation: Who pays for it and how much? Management Science 53(1), 78–87 (2007)
Dyckhoff, H., Finke, U.: Cutting and Packing in Production and Distribution: A Typology and Bibliography. Springer Science & Business Media (2012)
Friesen, D.K., Langston, M.A.: Variable sized bin packing. SIAM J. Comput. 15(1), 222–230 (1986)
Ghiani, G., Laporte, G., Musmanno, R.: Introduction to Logistics Systems Planning and Control. Wiley (2004)
Hall, R.W.: Consolidation strategy: inventory, vehicles and terminals. J. Bus. Logist. 8(2), 57 (1987)
Higginson, J., Bookbinder, J.H.: Policy recommendations for a shipment-consolidation program. J. Bus. Logist. 15(1), (1994)
Martín, J.C., Román, C.: Analyzing competition for hub location in intercontinental aviation markets. Transport. Res. E Logist. Transport. Rev. 40(2), 135–150 (2004)
Nguyen, C., Dessouky, M., Toriello, A.: Consolidation strategies for the delivery of perishable products. Transport. Res. E Logist. Transport. Rev. 69, 108–121 (2014)
Popken, D.A.: An algorithm for the multiattribute, multicommodity flow problem with freight consolidation and inventory costs. Operations Research 42(2), 274–286 (1994)
Qin, H., Zhang, Z., Qi, Z., Lim, A.: The freight consolidation and containerization problem. Eur. J. Oper. Res. 234(1), 37–48 (2014)
Song, H., Hsu, V.N., Cheung, R.K.: Distribution coordination between suppliers and customers with a consolidation center. Operations Research 56(5), 1264–1277 (2008)
Tyan, J.C., Wang, F.K., Du, T.C.: An evaluation of freight consolidation policies in global third party logistics. Omega 31(1), 55–62 (2003)
Ülkü, M.A.: Dare to care: Shipment consolidation reduces not only costs, but also environmental damage. Int. J. Prod. Econ. 139(2), 438–446 (2012)
Vielma, J.P., Ahmed, S., Nemhauser, G.: Mixed-integer models for nonseparable piecewise-linear optimization: Unifying framework and extensions. Operations Research 58(2), 303–315 (2010)
Acknowledgements
The authors are thankful to the company involved in this study and for the financial support provided by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES) [Finance code 001], the National Council for Scientific and Technological Development (CNPq) [grant number 313220/2020-4], and the São Paulo Research Foundation (FAPESP) [grant numbers 20/11602-5, 19/23596-2 and 16/01860-1].
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Vieira, T., Munari, P. (2021). A MIP Model for Freight Consolidation in Road Transportation Considering Outsourced Fleet. In: Masone, A., Dal Sasso, V., Morandi, V. (eds) Optimization and Data Science: Trends and Applications. AIRO Springer Series, vol 6. Springer, Cham. https://doi.org/10.1007/978-3-030-86286-2_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-86286-2_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-86285-5
Online ISBN: 978-3-030-86286-2
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)