A literature review on robust and real-time models for cross-docking

Abstract

Cross-docking is a logistics procedure implemented in a warehouse to achieve a competitive advantage by consolidating and transferring goods directly from an inbound supplier to an outbound customer on short notice and with no or limited storage. Today, one of the challenges related to cross-docking for both practitioners and researchers is handling the uncertainty. Robust cross-docking solutions bring a part of the answer to this challenge. This paper proposes an overview of robust and real-time models for cross-dock problems with a focus on scheduling problems, notably in the road-to-road cross-dock environment. To this end, the conducted systematic literature review addresses the collection, identification, screening, eligibility, and inclusion steps to extract the most relevant literature. The gaps in the literature are identified, and some perspectives to support future studies are proposed.

Keywords:

• Cross-docking
• scheduling
• robust
• real-time
• literature review

Acknowledgments

We thank anonymous reviewers for their valuable comments and constructive remarks.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work has been partially supported by the MIAI Multidisciplinary AI Institute at the Univ. Grenoble Alpes: (MIAI@Grenoble Alpes - ANR-19-P3IA-0003).

Notes on contributors

Bilge Torbali

Bilge Torbali is a Ph.D. student at the Grenoble Institute of Technology, University Grenoble Alpes, France. Currently, she is studying real-time planning models for cross-docking in the context of industry 4.0. Prior to this, she worked as a Teaching and Research Assistant in the Industrial Engineering Department at Istanbul Kultur University, Turkey. She earned her Master of Science degree in Industrial Engineering at Galatasaray University, Turkey and a Bachelor of Science degree in Industrial Engineering at Istanbul Kultur University, Turkey.

Gülgün Alpan

Gülgün Alpan is Professor of Industrial Engineering at Grenoble Institute of Technology, University Grenoble Alpes, France. Her research interests are in modelling, analysis and optimisation of production and service systems. Currently, she works on hybrid methods combining artificial intelligence with optimisation and simulation techniques, and she holds an Industry 4.0 Chair within the Multidisciplinary Institute on Artificial Intelligence in Grenoble. Gülgün Alpan is graduated from Rutgers University, New Jersey, USA, with M.Sc. and Ph.D. degrees in Industrial and Systems Engineering, in 1994 and 1997, respectively.