Abstract
This paper introduces process planning, scheduling and optimization in warehouse environment. The leading companies of the logistics warehouse industry still do not use planning and scheduling by automatic computer methods. Processes are planned and scheduled by an operational manager with detailed knowledge of the problem, processed tasks and commodities, warehouse layout, performance of employees, parameters of equipment etc. This is a quantum of information to be handled by a human and it can be very time-consuming to plan every process and schedule the timetable. The manager is usually also influenced by stress conditions, especially by the time of holidays when everyone is making supplies and the performance of the whole warehouse management goes down. The main contribution of this work is (a) to introduce the novel automatic method for optimization based on the evolutionary method called genetic programming, (b) to give a description of a tested warehouse, and (c) to show the metrics for performance measurement and to give a results which states the baseline for further research.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
de Koster, R., Le-Duc, T., Roodbergen, K.J.: Design and control of warehouse order picking: a literature review. Eur. J. Oper. Res. 182(2), 481–501 (2007)
Geraldes, C.A.S., Sameiro, M., Carvalho, F., Pereira, G.A.B.: A warehouse design decision model case study. In: IEEE International Engineering Management Conference, IEMC Europe, pp. 397–401 (2008)
B\({\bar{\text{u}}}\)lb\({\bar{\text{u}}}\)l, K., Kaminsky, P.: A linear programming-based method for job shop scheduling. J. Sched. 16(2), 161–183 (2013)
van Laarhoven, P.J.M., Aarts, E.H.L., Lenstra, J.K.: Job shop scheduling by simulated annealing. Oper. Res. 40(1), 113–125 (1992)
Tasgetiren, M.F., Liang, Y-Ch., Sevkli, M., Gencyilmaz, G.: A particle swarm optimization algorithm for makespan and total flowtime minimization in the permutation flowshop sequencing problem. Eur. J. Oper. Res. 177(3), 1930–1947 (2007)
Nowicki, E., Smutnicki, C.: An advanced tabu search algorithm for the job shop problem. J. Sched. 8(2), 145–159 (2005)
K\({\bar{\text{o}}}\)skolan, M., Keha, A.B.: Using genetic algorithm for single-machine bicriteria scheduling problems. Eur. J. Oper. Res. 145(3), 543–556 (2003)
Benes, R., Karasek, J., Burget, R., Riha, K.: Automatically designed machine vision system for the localization of CCA transverse section in ultrasound images. Comput. Methods Programs Biomed. 109(1), 92–103 (2013)
Burget, R., Karasek, J., Smekal, Z.: Recognition of emotions in Czech newspaper headlines. Radioengineering 20(1), 39–47 (2011)
Karasek, J., Burget, R., Morsky, O.: Towards an automatic design of non-cryptographic hash function. In: 34th International Conference on Telecommunications and Signal Processing, pp. 19–23 (2011)
Acknowledgments
This research work is funded by projects SIX CZ.1.05/2.1.00/03.0072, MPO FR-TI1/444, and project FEKT-S-11-17.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Karasek, J., Burget, R., Povoda, L. (2014). Logistic Warehouse Process Optimization Through Genetic Programming Algorithm. In: Silhavy, R., Senkerik, R., Oplatkova, Z., Silhavy, P., Prokopova, Z. (eds) Modern Trends and Techniques in Computer Science. Advances in Intelligent Systems and Computing, vol 285. Springer, Cham. https://doi.org/10.1007/978-3-319-06740-7_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-06740-7_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-06739-1
Online ISBN: 978-3-319-06740-7
eBook Packages: EngineeringEngineering (R0)