Mathematical model, heuristics and exact method for order picking in narrow aisles

Abstract

Order picking is one of the most challenging operations in distribution centre management and one of the most important sources of costs. One way to reduce the lead time and associated costs is to minimise the total amount of work for collecting all orders. This paper is motivated by a collaboration with an industrial partner who delivers furniture and electronic equipment. We have modelled their narrow aisles order picking problem as a vehicle routing problem through a series of distance transformations between all pairs of locations. Security issues arising when working on narrow aisles impose an extra layer of difficulty when determining the routes. We show that these security measures and the operator equipment allow us to decompose the problem per aisle. In other words, if one has to pick orders from three aisles in the warehouse, it is possible to decompose the problem and create three different instances of the picking problem. Our approach yields an exact representation of all possible picking sequences. We also show that neglecting 2D aspects and solving the problem over a 1D warehouse yields significant difference in the solutions, which are then suboptimal for the real 2D case. We have solved a large set of instances reproducing realistic configurations using a combination of heuristics and an exact algorithm, minimising the total distance travelled for picking all items. Through extensive computational experiments, we identify which of our methods are better suited for each aisle configuration. We also compare our solutions with those obtained by the company order picking procedures, showing that improvements can be achieved by using our approach.

Keywords:

• Order-picking
• narrow-aisle
• vehicle routing problem

Acknowledgements

We thank the contact persons from our industrial partner. We also thank the editor and two anonymous referees for their valuable comments on a earlier version of this paper.

Notes

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research was partly supported by the Canadian Natural Sciences and Engineering Research Council (NSERC) [grant number RGPIN-2014-05764], [grant number 01726-33], [grant number RGPIN-2015-04893]. This support is gratefully acknowledged.