Performance optimisation of pick and transport robot in a picker to parts order picking system: a human-centric approach

Figures & data

Figure 1. Grab™ by SOLWR.

Picture of ‘Grab’ robot picking a box from the shelf.

Table 1. Summary of literature on pick and transport robots in order picking system.

Authors	Methodology	Type of robot	HF aspects	Contribution	Article type
Zhang, Winkelhaus, and Grosse (2021)	Simulation model	Toru by Magazino	Physical	hybrid order picking system with specific assignment criteria for picking tasks can minimise both the system's operational costs and human workload.	Journal
Kauke, Sailer, and Fottner (2022)	Simulation model	Mobile picking robots	–	The influence of human-robot interaction on different aisle widths and layout variants.	Conference
Winkelhaus et al. (2022)	Simulation model	Toru by Magazino	–	Introduces a simulation model that considers the numerous system characteristics and parameters of a hybrid order picking system.	Journal
Zhang, Winkelhaus, and Grosse (2021)	Simulation model	Toru by Magazino	Physical	To thoroughly examine the hybrid order picking system, assess management consequences, and identify research possibilities for further work.	Conference
Fager, Sgarbossa, and Calzavara (2021)	Mathematical model	Cobots	–	Modelling technique to examine and evaluate the economic characteristics of a cobot sorting mode in a picking system against a manual sorting mode.	Journal
D’Souza, Costa, and Pires (2020)	Experimental study	KR 810 by Kassow Robotics	–	Introduces a basic model of a robotic OP system.	Journal
Tian et al. (2020)	Mathematical modelling	Robot shelf picking system	–	Introduces an optimal path planning approach by generating the order picking sequence with the shortest path.	Conference
Sgarbossa et al. (2020b)	Mathematical modelling	Grab^TM by SOLWR	Physical	Assigning products to two warehouse zones: one for robot and one for order pickers.	Conference
Lee and Murray (2017)	Mathematical modelling	‘Fetch’ and ‘Freight’ robots by Fetch Robotics	–	Designs a routing policy to route these mobile robots in order to reduce the time it takes to fetch a collection of items from a warehouse.	Journal
Kimura et al. (2015)	Experimental study	Mobile dual-arm robot	–	Develops a system that can automate whole order picking operations in warehouses.	Conference
Bonini, Urru, and Echelmeyer (2016)	Case study	EK-X by Still GmbH	–	Demonstrates how the robot-to-goods concept, as implemented by a rapidly deployable autonomous commissioning system, may result in cost reductions for small and medium-sized logistics enterprises.	Conference
This paper	Mathematical modelling	Grab^TM by SOLWR	Physical	The paper introduces a mathematical model to optimise the performance outcome of an order picking system with PTRs and order pickers	Journal

Figure 2. Warehouse layout of the case company.

Schematic representation of the case company’s warehouse layout.

Table 2. Operational data about the order picker and current order-picking process at the case warehouse.

Current order-picking process
Average walking speed of the order picker	1.3 m/s
Average order picker's pick speed	20 s/item
Average order picker's search time	1s
Work hours per day	7.5 hrs.
Workdays per week	5 days
Error rate	0.26%

Table 3. Operational data about shelf layout and warehouse sizes.

Current layout
Number of items	2423
Pick locations per aisle	60
Length of an aisle	28.5 m
Width of an aisle	5.5 m
Width of opening between aisles	3 m

Table 4. Operational data about the robot.

The robot
Average driving speed of the robot	0.9 m/s
Average robot pick speed	40 s/item
Average robot travel time between locations	10 s
Robot search time	0 s
Work hours per day	7.5 hrs.
Workdays per week	5 days
Error rate	0.1%

Figure 3. Performance outcomes in terms of daily cost, energy expenditure and error rate.

Figure 4. (a) Quantities (Qop, Qr), (b) lines (NLop, NLr) (c) orders (Zop, Zr) picked per worker (Nop, Nr).

A line graph illustrating the quantities, lines and orders picked per worker for the prime scenario.

Figure 5. Performance outcomes for WH15 and LAY.

A bar graph illustrating the performance outcomes in terms of daily cost, energy expenditure and error rate for the WH15 and LAY.

Figure 6. (a) Quantities (Qop, Qr), (b) lines (NLop, NLr) and (c) orders (Zop, Zr) picked per worker (Nop, Nr) for WH15 and LAY.

A line graph illustrating the quantities, lines and orders picked per worker for the WH15 and LAY.

Table 5. Performance outcomes of the prime scenario.

Number of robots
	nRa	Daily cost of operation (€)	Order pickers' energy expenditure (kcal/min)	Error rate of OP operations (%)
1	2	2663	2.197	0.25
	3	2665	2.090	0.25
	4	2666	2.088	0.25
2	2	2494	2.118	0.24
	3	2495	2.070	0.24
	4	2496	2.068	0.24
3	2	2325	2.103	0.23
	3	2332	2.055	0.23
	4	2336	2.047	0.23
4	2	2155	2.029	0.22
	3	2168	1.968	0.22
	4	2174	1.955	0.22
5	2	1983	2.012	0.21
	3	2002	1.857	0.21
	4	2005	1.799	0.21

Table 6. Performance outcomes for WH15 and LAY.

WH15				LAY
	Number of robots
	nRa	Daily cost of operation (€)	Order picker's energy expenditure (kcal/min)	Error rate of OP operations (%)	Daily cost of operation (€)	Order picker's energy expenditure (kcal/min)	Error rate of OP operations (%)
1	2	2662	2.188	0.24	2673	2.128	0.25
	3	2670	2.075	0.24	2675	2.127	0.25
	4	2681	2.069	0.24	2676	2.125	0.25
2	2	2502	2.236	0.22	2504	2.057	0.24
	3	2506	2.052	0.22	2504	2.052	0.24
	4	2509	2.046	0.22	2506	2.050	0.24
3	2	2335	2.298	0.21	2335	1.971	0.23
	3	2341	2.042	0.21	2342	1.966	0.23
	4	2343	2.025	0.20	2346	1.964	0.23
4	2	2168	2.218	0.20	2164	1.871	0.23
	3	2170	1.955	0.19	2177	1.866	0.23
	4	2177	1.953	0.19	2184	1.865	0.23
5	2	1992	2.133	0.18	1993	1.754	0.22
	3	2005	1.838	0.18	2012	1.749	0.22
	4	2010	1.778	0.18	2014	1.747	0.22

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Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.