An evaluation of routing and volume-based storage policies in an order picking operation

Decision Sciences, Spring 1999 by Petersen, Charles G II, Schmenner, Roger W

What do these results have to say about the management of a typical warehouse, whose pattern of demand is not so concentrated as the Pareto 80-20 rule, but for which 20% of the items account for, say, 60% of the volume, and for which the pick list sizes can vary dramatically? Here is what the research suggests:

1. Organize the warehouse storage so that the high volume items are concentrated in a few aisles (within-aisle storage).

2. Position the pick-up/drop-off (PID) point in the middle of the front aisle, and not in the corner.

3. Use a routing heuristic for your pickers so as to form more consistent routes, making it less confusing, and minimizing picking errors. A good heuristic is the composite, which combines the transversal and return strategies.

CONCLUSIONS

No study from the literature has (1) evaluated various routing heuristics in a volume-based storage environment, (2) proposed several methods of implementing volume-based storage, and (3) examined the interaction of the routing and storage policies under different operating conditions.

Heuristics are commonly used in practice because they are easy for warehouse workers to understand, they form consistent routes that risk fewer missed picks, they offer good solutions, and they have a fast solution time. The biggest drawback to heuristic routing solutions is that the gap between these solutions and the optimal can be significant. This experiment showed an average solution gap of around 10% for the composite, largest gap, and midpoint strategies, and around 30% for the return and transversal strategies. However, the overall solution gap between the best heuristic solution for a given scenario and the optimal was only 3%. Note that these solution gaps are expressed in travel distance, the gap between heuristics and optimal routing changes depending on travel speed and picking time per SKU.

The performance of the routing policies, heuristics and optimal, was highly dependent on the storage policy in use. The size of the pick list and to a lesser extent the degree of demand skewness also affect the routing heuristics. Therefore, the solution gap between optimal routing and the routing heuristics can vary widely depending on the storage policy, pick list size, and demand skewness. In addition, the advanced heuristics often offer solutions that are as complex as the optimal. Using an optimal routine such as Ratliff and Rosenthal (1983) offers a manager a fast solution time and the shortest distance route. However, optimal routes are often confusing in nature and may not work within the confines of an order picking operation. Managers must analyze the trade-off between the efficiency of optimal solutions and the ease of implementation and use of heuristic procedures.

This research confirms and extends the results of Jarvis and McDowell (1991) that within-aisle storage is the best overall volume-based storage policy regardless of aisle travel restrictions. In addition, within-aisle storage also works well for all levels of pick list size and demand skewness. The performance of diagonal storage was also impressive. The perimeter storage policy does not work well with the composite, return, and transversal routing policies. However, it does result in a small solution gap between the optimal and the largest gap and midpoint strategies. The across-aisle storage policy was not effective. The managerial implications of these results are that a significant cost savings can result from volumebased storage and that the method of storing items in a warehouse can yield varying results.