Dynamic Human-Robot Collaborative Picking Strategies

51 Pages Posted: 22 May 2020

See all articles by Kaveh Azadeh

Kaveh Azadeh

Erasmus University Rotterdam (EUR) - Rotterdam School of Management (RSM)

Debjit Roy

Indian Institute of Management (IIM), Ahmedabad

M. B. M. de Koster

Erasmus University Rotterdam (EUR) - Department of Technology and Operations Management

Date Written: April 25, 2020

Abstract

In the last decades, many retailers have started to combine traditional store deliveries with fulfilment of online sales to consumers, from omnichannel warehouses, which are increasingly automated. One popular way of warehouse automation is with Autonomous Mobile Robots (AMRs), that collaborate with human pickers to efficiently pick the orders by reducing the pickers' unproductive walking time. Picker travel time can be reduced even more by zoning the storage system, where robots take care of the travel between these zones. However, the optimal zoning strategy for these robotic systems is not clear: few zones are particularly good for the large store orders, while many zones are particularly good for the small online orders. We therefore study the effect of dynamic zoning strategies, i.e. dynamic switching between a No Zoning (NZ) strategy and a Progressive Zoning (PZ) strategy. We solve the problem in two stages. First, we develop queuing network models to obtain load-dependent pick throughput rates corresponding to a given number of AMRs and a picking strategy with a fixed number of zones. Then, we develop a Markov-decision model to investigate how higher pick performance can be achieved by dynamically switching between these pick strategies. Using data from an omnichannel warehouse that processes various order sizes, we show that a Dynamic Switching (DS) policy can lower operational cost by up to 7 percent. However, these cost savings decrease as the number of robots per picker increases.

Keywords: collaborative robots, order picking, queuing network model, Markov decision process, throughput analysis

JEL Classification: M11

Suggested Citation

Azadeh, Kaveh and Roy, Debjit and de Koster, M.B.M. René, Dynamic Human-Robot Collaborative Picking Strategies (April 25, 2020). Available at SSRN: https://ssrn.com/abstract=3585396 or http://dx.doi.org/10.2139/ssrn.3585396

Kaveh Azadeh

Erasmus University Rotterdam (EUR) - Rotterdam School of Management (RSM) ( email )

P.O. Box 1738
Room T08-21
3000 DR Rotterdam, 3000 DR
Netherlands

Debjit Roy

Indian Institute of Management (IIM), Ahmedabad ( email )

Vastrapur
Ahmedabad, Gujarat 380 015
India

M.B.M. René De Koster (Contact Author)

Erasmus University Rotterdam (EUR) - Department of Technology and Operations Management ( email )

RSM Erasmus University
PO Box 1738
3000 DR Rotterdam
Netherlands
+31 10 408 1719 (Phone)
+31 10 408 9014 (Fax)

HOME PAGE: http://www.rsm.nl/rdekoster

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