Using an agent-based neural-network computational model to improve product routing in a logistics facility

Till Becker, Christoph Illigen, Bill McKelvey, Michael Hülsmann and Katja Windt

International Journal of Production Economics, 2016, vol. 174, issue C, 156-167

Abstract: This study tests whether a simplified neural-network computational model can make routing decisions in a logistics facility more efficiently than five ׳intelligent׳ routing heuristics from the logistics literature. The experiment uses a real-world simulation scenario based on the Hamburg Harbor Car Terminal, a logistic site faced with managing approximately 46,500 car-routing decisions on a yearly basis. The simulation environment has been built based on a data set provided by the Terminal operator to reflect a real-world case. The simulation results show that the percent-improvement of the neural-net model׳s performance is 48% better than that of the best routing heuristic tested in previous studies. To test the applicability of the method with more complex logistic scenarios, we relaxed the sequence constraints for routing in a subsequent simulations study. If logistic complexity in terms of more freedom in decision-making is increased, the neural net model׳s percent-improvement performance of routing decisions is around three times better than the best-performing heuristic.

Keywords: Logistics; Ship terminal; Routing heuristics; Agent-based computational model; Neural network model; Complexity management (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0925527316000049
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:proeco:v:174:y:2016:i:c:p:156-167

DOI: 10.1016/j.ijpe.2016.01.003

Access Statistics for this article

International Journal of Production Economics is currently edited by Stefan Minner

More articles in International Journal of Production Economics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().