An investigation of the effects of storage assignment and picker routing on the occurrence of picker blocking in manual picker-to-parts warehouses

• Date: 14 August 2017
• DOI: https://doi.org/10.1108/IJLM-04-2016-0095
• Pages: 841-863
• Published date: 14 August 2017
• Author: Torsten Franzke,Eric H. Grosse,Christoph H. Glock,Ralf Elbert
• Subject Matter: Management science & operations,Logistics

An investigation of the effects of

storage assignment and picker

routing on the occurrence of

picker blocking in manual

picker-to-parts warehouses

Torsten Franzke

Chair of Management and Logistics,

Technische Universität Darmstadt, Darmstadt, Germany

Eric H. Grosse and Christoph H. Glock

Chair of Production and Supply Chain Management,

Technische Universität Darmstadt, Darmstadt, Germany, and

Ralf Elbert

Chair of Management and Logistics,

Technische Universität Darmstadt, Darmstadt, Germany

Abstract

Purpose –Order picking is one of the most costly logistics processes in warehouses. As a result, the

optimization of order picking processes has received an increased attention in recent years. One potential

source for improving order picking is the reduction of picker blocking. The purpose of this paper is to

investigate picker blocking under different storage assignment and order picker-route combinations and

evaluate its effects on the performance of manual order picking processes.

Design/methodology/approach –This study develops an agent-based simulation model (ABS) for order

picking in a rectangular warehouse. By employing an ABS, we are able to study the behaviour of individual

order pickers and their interactions with the environment.

Findings –The simulation model determines shortest mean throughput times when the same routing policy

is assigned to all order pickers. In addition, it evaluates the efficiency of alternative routing policies–storage

assignment combinations.

Research limitations/implications –The paper implies that ABS is well-suited for further investigations

in the field of picker blocking, for example, with respect to the individual behaviour of agents.

Practical implications –Based on the resultsof this paper, warehouse managerscan choose an appropriate

routing policy thatbest matches their storage assignmentpolicy and the number of order pickers employed.

Originality/value –This paper is the first to comprehensively study the effects of different combinations of

order picker routing and storage assignment policies on the occurrence of picker blocking.

Keywords Congestion, Agent-based simulation, Manual order picking, Picker blocking, Routing policies,

Storage assignment

Paper type Research paper

1. Introduction

Order picking is one of the most labour- and time-consuming processes in warehouses

(Frazelle, 2000; Tompkins et al., 2010). It can be defined as the process of retrieving stock

keeping units (SKUs) from storage locations to fulfil customer orders (De Koster et al., 2007),

and it is a critical process within supply chains with a direct influence on customer

satisfaction. If order picking is not organized adequately, the picking of wrong or damaged The International Journal of

Logistics Management

Vol. 28 No. 3, 2017

pp. 841-863

© Emerald PublishingLimited

0957-4093

DOI 10.1108/IJLM-04-2016-0095

Received 7 November 2015

Revised 8 April 2016

11 April 2016

12 April 2016

15 July 2016

Accepted 27 July 2016

The current issue and full text archive of this journal is available on Emerald Insight at:

www.emeraldinsight.com/0957-4093.htm

The authors wish to thank the guest editor Peter McCullen and two anonymous reviewers for their

valuable comments and suggestions that helped to improve an earlier version of this paper.

841

Picker-to-parts

warehouses

SKUs or long order picking process times can have a negative influence on customer

satisfaction (Gue et al., 2006; Parikh and Meller, 2008). Thus, efficient order picking

processes are a prerequisite for a high-performing supply chain (Chen et al., 2013).

Due to the fact that order picking is performed manually in many cases, researchers have

estimated that it accounts for about 50 per cent of the operating costs of warehouses

(Frazelle, 2000; Tompkins et al., 2010; Grosse et al., 2015). To reduce the cost of order picking,

researchers and practitioners have tried to minimize travel times for order pickers in

picker-to-parts systems to increase the throughput of the warehouse (Chen et al., 2014;

De Koster et al., 2007). Travelling, which can account for up to 55 per cent of the total work time

of the order picker (Tompkins et al., 2010), can be influenced by assigning SKUs to storage

locations, by grouping or splitting up incoming orders, or by defining routes for the orderpicker.

Another aspect that is of high importance in warehousing is space utilization. Especially

in situations where it is not possible or too expensive to make additional storage space

available, warehouse managers have to store as many products as possible in the existing

storage facility. To increase space utilization for order picking warehouses, so-called

narrow-aisle warehouses are commonly employed (Chen et al., 2013; Chen et al., 2014;

Gue et al., 2006). Narrow-aisle warehouses have one major disadvantage, however, as aisles

in such warehouses are usually too small to permit order pickers to pass each other.

The consequence is that in narrow-aisle warehouses, order pickers working in the same zone

may block each other (Davarzani and Norrman, 2015), which can put the operational

advantages of narrow-aisle warehouses at stake (Hong, 2014). “Picker blocking”or “picker

congestion”, in this context, refers to a situation where one order picker is disturbed by

another order picker, such that he/she is unable to pass the other order picker or reach a pick

column (e.g. Parikh and Meller, 2009). Picker blocking can result in longer travel times due to

idle or waiting times, and hence may reduce order picking efficiency (Chen et al., 2013, 2014;

Mowrey and Parikh, 2014; Parikh and Meller, 2008). Due to the complex and stochastic

nature of picker blocking, its influence on the performance of order picking is difficult to

quantify (Heath et al., 2013).

Although picker blocking has received some attention in the literature, there are still

several research gaps that need to be addressed. A closer look at the literature shows that

most studies on picker blocking focussed mainly on the s-shape routing policy and on

random storage assignment (e.g. Mowrey and Parikh, 2014). Existing studies thus neglected

the effects of the combination of storage assignment and routing policies on the occurrence

of picker blocking (e.g. Chen et al., 2013; Kłodawski and Żak, 2013; Pan and Shih, 2008).

However, as we hypothesize, different routing policies can positively or negatively influence

additional throughput times resulting from picker blocking, depending on how they are

combined with storage assignment rules. Thus, it is necessary to quantify the effects of

picker blocking on mean order throughput times for different routing policies and storage

assignment rules. Another important aspect is that most studies on routing policies and

storage assignment rules analyzed the performance of alternative picker routing-storage

assignment combinations in single picker environments only. Multi-picker investigations

that study the effects of route combinations for multiple order pickers on picker blocking,

and the resulting order picking efficiency, are still lacking.

The paper at hand aims to close these research gaps by studying the effects of routing

and storage assignment on the occurrence and the performance impact of picker blocking.

The results of the paper support practitioners in selecting suitable combinations of picker

routing and storage assignment policies that reduce picker blocking and guarantee shorter

order throughput times. In our simulation analysis, we consider varying the number of

order pickers and picks per order as well as several combinations of routing policies and

storage assignment rules. To analyze the system’s performance in terms of mean order

throughput time, an agent-based simulation (ABS) model is developed.

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