Abstract
Distribution related problems in logistics have many decision variables and constraints that have to be considered simultaneously. Most often, these are the problems in the discrete optimization branch, modeled and optimized using operational research, in particular mathematical programming (MP) models and methods, such as mixed integer linear programming (MILP), integer programming (IP) and integer linear programming (ILP). These methods become ineffective very quickly in the case of larger size problems. Also, the number of decision variables and constraints may exceed the capacity of available MP solvers. To improve their efficiency and reduce the effective size of the solution space of a given problem, hybrid approaches are being developed, integrating MP with other environments. This article discusses the results of comparative analysis of several MP solvers (LINGO, SCIP, GUROBI) in the context of their use for optimization of selected distribution problems.
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
Schrijver, A.: Theory of Linear and Integer Programming. Wiley (1998). ISBN 0- 471-98232-6
Apt, K., Wallace, M.: Constraint Logic Programming using Eclipse. Cambridge University Press, Cambridge (2006)
Rossi, F., Van Beek, P., Walsh, T.: Handbook of Constraint Programming (Foundations of Artificial Intelligence). Elsevier Science Inc., New York (2006)
Sitek, P., Wikarek, J.: A hybrid programming framework for modeling and solving constraint satisfaction and optimization problems. In: Scientific Programming, vol. 2016(2016), Article ID 5102616, 13 p. (2016). http://dx.doi.org/10.1155/2016/5102616
Sitek, P.: A hybrid approach to the two-echelon capacitated vehicle routing problem (2E-CVRP). In: Szewczyk, R., Zieliński, C., Kaliczyńska, M. (eds.) Recent Advances in Automation, Robotics and Measuring Techniques. AISC, vol. 267, pp. 251–263. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-05353-0_25
Sitek, P., Bzdyra K., Wikarek, J.: A hybrid method for modeling and solving supply chain optimization problems with soft and logical constraints. In: Mathematical Problems in Engineering, vol. 2016(2016), Article ID 1532420, 16 p. (2016). http://dx.doi.org/10.1155/2016/1532420
Lindo. http://www.lindo.com/, Accessed 20 Oct 2016
Codd, E.: A relational model of data for large shared data banks. Commun. ACM 13(6), 377–387 (1970). https://doi.org/10.1145/362384.362685
Toth, P., Vigo, D.: Vehicle routing: problems, methods, and applications. In: SIAM, Philadelphia (2014). https://doi.org/10.1137/1.9781611973594
Perboli, G., Tadei, R., Vigo, D.: The two-echelon capacitated vehicle routing problem: models and math-based heuristics. Transp. Sci. 45(3), 364–380 (2012). https://doi.org/10.1287/trsc.1110.0368
ORO Group Web-page. http://www.orgroup.polito.it/. Accessed 20 Oct 2016
Mula, J., Peidro, D., Diaz-Madronero, M., Vicens, E.: Mathematical programming models for supply chain production and transport planning. Eur. J. Oper. Res. 204(3), 377–390 (2010). https://doi.org/10.1016/j.ejor.2009.09.008
Park, Y.: An integrated approach for production and distribution planning in supply chain management. Int. J. Prod. Res. 43(6), 1205–1224 (2005). https://doi.org/10.1080/00207540412331327718
Nielsen, P., Michna, Z., Do, N.A.D.: An empirical investigation of lead time distributions. In: Grabot, B., Vallespir, B., Gomes, S., Bouras, A., Kiritsis, D. (eds.) APMS 2014. IAICT, vol. 438, pp. 435–442. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-44739-0_53
Nielsen, P., Jiang, L., Rytter, N.G.M., Chen, G.: An investigation of forecast horizon and observation fit’s influence on an econometric rate forecast model in the liner shipping industry. Marit. Pol. Manag. 41(7), 667–682 (2014). https://doi.org/10.1080/03088839.2014.960499
Bocewicz, G., Nielsen, I., Banaszak, Z.: Production flows scheduling subject to fuzzy processing time constraints. Int. J. Comput. Integr. Manuf. 29(10), 1105–1127 (2016). https://doi.org/10.1080/0951192x.2016.1145739
Relich, M.: Identifying project alternatives with the use of constraint programming. In: Borzemski, L., Grzech, A., Świątek, J., Wilimowska, Z. (eds.) Information Systems Architecture and Technology: Proceedings of 37th International Conference on Information Systems Architecture and Technology—ISAT 2016—Part I. AISC, vol. 521, pp. 3–13. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-46583-8_1
Grzybowska, K., Gajšek, B.: Supply chain logistics platform as a supply chain coordination support. In: Bajo, J., Escalona, M.J., Giroux, S., Hoffa-Dąbrowska, P., Julián, V., Novais, P., Sánchez-Pi, N., Unland, R., Azambuja-Silveira, R. (eds.) PAAMS 2016. CCIS, vol. 616, pp. 61–72. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-39387-2_6
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Wikarek, J., Sitek, P., Stefański, T. (2018). Comparative Analysis of MP-Based Solvers to Optimize Distribution Problems in Logistics. In: Szewczyk, R., Zieliński, C., Kaliczyńska, M. (eds) Automation 2018. AUTOMATION 2018. Advances in Intelligent Systems and Computing, vol 743. Springer, Cham. https://doi.org/10.1007/978-3-319-77179-3_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-77179-3_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-77178-6
Online ISBN: 978-3-319-77179-3
eBook Packages: EngineeringEngineering (R0)