cobot order picking sci 2021 public

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RSM - a force for positive change

Warehouse coboticsRecent Research Developments

Supply Chain Innovation, Antwerp, 7 Oct 2021

René de Koster

[email protected]

Program

1. Fully robotic warehouses?

2. Cobot-AMRs in order picking

3. Performance analysis

4. Role of humans

5. Ride on AMRs

6. Sorting with AMRs?

R. de Koster (c), Warehouse cobotics 211.10.2021

1.Fully robotic warehouses?

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Fully Robotic Warehouses - stacking

R. de Koster (c), Warehouse cobotics 4

Source: Azadeh, De Koster, Roy, Transportation Science, 2019

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Fully Robotic warehouses

Implications

+ Particularly for (store-based) retail

+ Number increases rapidly

- Long pay-back time (if ever)

- Not affordable for most operations


Automated storage/picking systems

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2.Cobot-AMRs in order picking


Autonomous mobile robot systems

Collaborative Picking Systems

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• High throughput performance• Requires many robots• Requires special racking structure• Could become very expensive

Automated Systems Manual Systems

• Lower throughput performance• More operational flexibility• No special racking structure

Collaborative Systems

11.10.2021 R. de Koster (c), Warehouse cobotics

Variants


Cobot AMRs (autonomous mobile robots)


Human leading:

• Fetch Robotics

• Still iGo neo

Cobot leading:

• Locus Robotics

• Toyota

Hybrid: cobot with multiple persons (Zone picking):

• Vecna Robotics

• Righthand Robotics

• 6 River Systems (‘chuck’)

• Oceaneering (FROG)

Ride-on cobot

• Pick-by-glove (Crown QuickPick Remote)

Variants


Picking Operation in Collaborative Systems


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Picking Operation


Picking Operation


Picking Operation


Picking Operation


Picking Operation


Picking Operation


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Picking Operation


Picking Operation


Picking Operation


Picking Operation


Picking Operation


Picking Operation


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Picking Operation


Picking Operation


Picking Operation


Picking Operation


Picking Operation


Picking Operation


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Picking Operation


Picking Operation


Picking Operation


Collaborative Picking Systems

Benefits:

• Increase picker productivity (reduce walking)

• Easy implementation: in running operations, no rack or organizational changes needed.

• Low risk: invest with growing volume; easy fallback

• Safe: (relatively) low risk


3.Performance analysis: effect of zoning


Autonomous mobile robot systems

Flexibility in zoning


No Zoning (NZ)

Progressive Zoning (PZ) (Two Picking Zones)

Performance trade-offs:

• Shorter Robot Waiting Time• Longer Picker Travel Time

• Shorter Picker Travel Time• Longer Robot Waiting Time

Research Question:Can the pick performance be increased by dynamic switching between zone picking and No zoning (parallel picking)?

Azadeh, Roy, De Koster (2020), working paper

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2-Step Research Framework


Markov Decision ProcessQueueing Network Modeling

Throughput capestimation of the system

Optimal switching strategy

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Step 1. Realistic Movement of Resources


Picking Items

Return to Depot: Robot Transporting Picked Items to the Depot

We model the Parallel Movement of the picker and the robot using a two‐phase server

Parallel Movement: Picker and robot

simultaneously travel to the first item location

Picker Idle Location

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Queueing Network – With One Picker

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Closed Queuing Network Models for Different Picking Strategies


Pooled Pickers (NZ) Dedicated Pickers (with 2 zones, PZ)

Solution method to obtain throughput: Continuous time Markov chain

Solution method to obtain throughput: Aggregation disaggregation (ADA) based solution

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Queuing Network Model with K zones


Step 2. Dynamic Switching (DS) Policy


External waiting queue

Orders arrive in the system

Successfully processed orders leave the system

Collaborative Human‐Robot Picking System

Costs: • 𝐶 = Waiting Cost Per Time Unit to Fulfill the Order• 𝐶 = Postponement Cost Per Time Unit (no capacity to fulfill so will be

postponed to another day)

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Dynamic Switching (DS) Policy

• Objective: find a stationary policy 𝑓 which minimizes the average cost per

time period

• Solution method: value iteration

R. de Koster (c), Warehouse cobotics 4311.10.2021

Numerical Results DS Policy

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Description Value

Number of aisle 20

Number of storage columns per block

20

Number of blocks 2

Number of pickers 6

Picker speed 0.75 m/s

Robot Speed 1 m/s

Depot Time 10 sec

Capacity of System 20 orders

Arrival Rate 200 orders/h

Postponement Cost 20 euro

Waiting Cost 0.833 euro/order/ h

Switching Cost 0.001 euro

Blo

ck

Warehouse Layout and Parameters


Current picking operation: NZ Current picking operation: PZ

# robots: 8Small-sized order probability: 0.55


Numerical Results DS PolicyAverage cost comparison between Dynamic Switching (DS) and Static - NZ/PZ

• We simulate the network to

calculate the average cost

per unit time under DS and

NZ and PZ picking policy by

changing:

• Number of robots from 8-11

• probability of arrival order to

be small: from 0.05 to 0.95

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Simulation Framework


Average cost comparison between DS and NZ/PZ

Insights:

• Region A (E-commerce): PZ

• Region C (Store-chain whse): NZ

• Region B (Omnichannel whse) : DS

The performance gain is larger

when the number of robots is small

(Region B)

With a small number of robots, start

with dynamic policy

As the number of robots increases,

choose either PZ or NZ depending

on the order composition

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8 robots 9 robots

10 robots 11 robots

Average cost comparison between DS and NZ/PZ

Insights:

• Region A (E-commerce): PZ

• Region C (Store-chain whse): NZ

• Region B (Omnichannel whse) : DS

The performance gain is larger

when the number of robots is small

(Region B)

With a small number of robots, start

with dynamic policy

As the number of robots increases,

choose either PZ or NZ depending

on the order composition

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8 robots 9 robots

10 robots 11 robots

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Conclusions and Next Steps

• Cobot order-picking systems improve productivity (ergonomics) and also

costs

• Dynamic policy can improve static policy (depending on mix of small/large

orders), with up to 8%

• But: Behavioral issues need to be analyzed

• Robot leading or lagging? Picker control or robot control?

• Future of work?


4. Role of humans


Research questions & aims

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1. What is the best collaboration strategy?

Comparison of Human leading vs Human supporting

2. How will people with different behavioral traits perform in such a collaboration?

Based on Prevention regulatory focus

3. How will human robot collaboration affect the worker’s psychosynthesis?

Technology acceptance and robot design may affect job satisfaction, self-esteem, robot-efficacy


Work with: Alexandros Pasparakis, Jelle de Vries, (2021), working paper

Research questions & aims

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1. Collaboration Dynamics:

Human leading vs Human supporting

System performance:

i. Total productivity

ii. Picking accuracy

2. Behavioral Mechanism: Prevention regulatory focus

Human performance:

iii. Intention to be productive

3. Behavioral Implications:

Technology acceptance, robot design

Human psychosynthesis:

iv. Task satisfaction

v. Self-esteem

vi. Robot efficacy


Human leading Human supporting

Human leading vs human supporting

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Vs


Human leading Human supporting

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Vs

Human leading vs human supporting


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Human characteristics

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a. Competence: base speed level

of manual order picking

b. Order picking experience

American New Home Group11.10.2021 R. de Koster (c), Warehouse cobotics

Human characteristics

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a. Competence: base speed level

of manual order picking

b. Order picking experience

c. Prevention regulatory focus(Regulatory Focus Theory, Higgins et al., 1998)

• State dependent work-rate adjustment (Powell & Schultz, 2004)

• Productivity cap (cooperation incentive)

• Imminent job loss threat

American New Home Group


Warehouse Behavioral Lab

2.5 aisles

300 picking locations

Task:

Pick as many correct order-lines as possible within time limits

Incentives:

• Fixed monetary compensation

• Productivity bonus, prize for top pickers

• Certificate of training

Participants’ profile:

60 Students training to become logistics professionals

(2.5 hours each)

Experiment: The warehouse


Experiment description


Results 1 - TOTAL PRODUCTIVITY

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Table 1. Linear mixed-effects models predicting total productivity in human-robot collaborative order picking.

Dependent variable: Total productivity (correct total order lines picked in 20 minutes)

Scenarios model (M1a) Behavioral model (M1b) Interaction model (M1c)

Independent variable estimate Std. error estimate Std. error estimate Std. error

Constant 60.213 1.048 54.201 4.199 50.895 4.763

Base competence: Medium 4.472 1.105 4.828 1.120 4.841 1.119

Base competence: High 7.935 1.085 8.052 1.077 8.066 1.076

Order picking experience -0.117 0.086 -0.126 0.085 -0.125 0.085

Order set #2 -2.301 0.668 -2.229 0.669 -2.080 0.671

Order set #3 -3.766 0.654 -3.660 0.657 -3.620 0.652

Scenario sequence 1.598 0.905 1.444 0.902 1.441 0.901

Scenario: Human leading 5.074 0.490 5.074 0.490 11.453 4.381

Prevention regulatory focus 1.388 0.939 2.150 1.073

(Scenario: Human leading) * (Prevention

regulatory focus)

-1.504 1.026

Restricted maximum likelihood criterion 634.0 630.2 622.8

Number of subjects 60 60 60

Number of rounds 120 120 120

Bold type = significant at p < 0.05.


Results 1 - TOTAL PRODUCTIVITY

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Table 1. Linear mixed-effects models predicting total productivity in human-robot collaborative order picking.

Dependent variable: Total productivity (correct total order lines picked in 20 minutes)



Constant 60.213 1.048 54.201 4.199 50.895 4.763

Base competence: Medium 4.472 1.105 4.828 1.120 4.841 1.119



Order set #2 -2.301 0.668 -2.229 0.669 -2.080 0.671

Order set #3 -3.766 0.654 -3.660 0.657 -3.620 0.652

Scenario sequence 1.598 0.905 1.444 0.902 1.441 0.901

Scenario: Human leading 5.074 0.490 5.074 0.490 11.453 4.381

Prevention regulatory focus 1.388 0.939 2.150 1.073


regulatory focus)

-1.504 1.026





Human leading: superior in total order picking

productivity


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Results 2 - Accuracy

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Table 2. Negative binomial mixed-effects models predicting inaccuracy in human-robot collaborative order picking.

Dependent variable: Inaccuracy (total errors per 20 minutes)



Constant 4.088 1.714 4.206 1.901 5.161 2.112

Base competence: Medium -0.386 0.331 -0.403 0.352 -0.414 0.353



Order set #2 0.210 0.311 0.210 0.311 0.182 0.314

Order set #3 0.540 0.300 0.537 0.301 0.549 0.301

Scenario sequence -0.289 0.249 -0.288 0.249 -0.287 0.249

Correct total order lines picked in 20 minutes -0.075 0.029 -0.074 0.029 -0.071 0.029

Scenario: Human leading 0.662 0.263 0.657 0.264 -1.341 1.960

Prevention regulatory focus -0.038 0.268 -0.305 0.374


regulatory focus)

0.472 0.459

Log-likelihood -131.09 -131.08 -130.55



Bold type = significant at p < 0.05. ALEXANDROS PASPARAKIS | SCM Seminar | Virtual | Nov 23, 2020


Results 2 - Accuracy

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Table 2. Negative binomial mixed-effects models predicting inaccuracy in human-robot collaborative order picking.

Dependent variable: Inaccuracy (total errors per 20 minutes)



Constant 4.088 1.714 4.206 1.901 5.161 2.112




Order set #2 0.210 0.311 0.210 0.311 0.182 0.314

Order set #3 0.540 0.300 0.537 0.301 0.549 0.301


Correct total order lines picked in 20 minutes -0.075 0.029 -0.074 0.029 -0.071 0.029

Scenario: Human leading 0.662 0.263 0.657 0.264 -1.341 1.960



regulatory focus)

0.472 0.459

Log-likelihood -131.09 -131.08 -130.55




Human supporting: greater order picking accuracy


Table 3. Linear mixed-effects models predicting pick time of products located in same aisle (different stopping node).

` Dependent variable: Pick Time in same aisle (Tukey transformation, 𝜆 0.325)



Constant 1.497 0.049 1.806 0.152 1.976 0.170


Base competence: High -0.269 0.040 -0.274 0.039 -0.274 0.039

Order picking experience 0.005 0.003 0.005 0.003 0.005 0.003


Progress (square root of pick timestamp) -0.006 0.001 -0.006 0.001 -0.006 0.001

Scenario: Human leading -0.084 0.017 -0.084 0.017 -0.409 0.145



regulatory focus)

0.076 0.034



Number of stopping node combinations 26 26 26

Number of picks 2720 2720 2720


Results 3 - intention for Productivity


Table 3. Linear mixed-effects models predicting pick time of products located in same aisle (different stopping node).

` Dependent variable: Pick Time in same aisle (Tukey transformation, 𝜆 0.325)



Constant 1.497 0.049 1.806 0.152 1.976 0.170


Base competence: High -0.269 0.040 -0.274 0.039 -0.274 0.039

Order picking experience 0.005 0.003 0.005 0.003 0.005 0.003


Progress (square root of pick timestamp) -0.006 0.001 -0.006 0.001 -0.006 0.001

Scenario: Human leading -0.084 0.017 -0.084 0.017 -0.409 0.145



regulatory focus)

0.076 0.034



Number of stopping node combinations 26 26 26

Number of picks 2720 2720 2720


Results 3 - intention for Productivity


RESULTS 4: in progress

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Transferring from manual picking to human and robot collaboration

Task satisfaction(Stone, 1977)

Self-esteem(Rosenberg, 1965)

Robot efficacy(Rosenthal Et Al., 2017)

Human leading + +

Human supporting ++ + +


RESULTS 4: in progress

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Transferring from manual picking to human and robot collaboration

Ease of Use Robot efficacy

Perceived Usefulness Task satisfactionRobot characteristics:

i. Anthropomorphism

ii. Animacy

iii. Likeability

iv. Perceived intelligence

v. Perceived safety

(Adapted T.A.M., Venkatesh, et al., 2000)

+

+

+

Task satisfaction(Stone, 1977)

Self-esteem(Rosenberg, 1965)

Robot efficacy(Rosenthal Et Al., 2017)

Human leading + +

Human supporting ++ + +


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5. Ride-on cobots


Joint work with: Mahmut Tutam, working paper, 2021

Problem Definition (Ride-on Cobots)

Advantages

Increase order-picking throughput

Decrease stress for joints

Relatively low costs compared to fully automated systems

Research Questions

Should the order picker walk or ride between pick locations?

What is the optimal collaboration strategy?


Problem Definition (Ride-on Cobots)

Optimal Routing (ride to all locations, walk to all

locations)

Optimal Collaboration (ride or walk)

Ride to clustered locations and walk to pick locations

Truck follows an optimal ride route

Only walk (Optimal) Ride or walk (Optimal)Only ride (Optimal)


Results for single block warehouse

L = 50m, v = 2.7 m, a = 5.3 m, sd = 1.8 m/s, sw = 0.7 m/s, e = 2s, s = 2s)

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Results for single block warehouse

L = 50m, v = 2.7 m, a = 5.3 m, sd = 1.8 m/s, sw = 0.7 m/s, e = 2s, s = 2s)

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Conclusion

• Optimal ride/drive policy can improve static policy with up to 5-10%

• When driving to all locations: large savings, if many picks/aisle

• In practice: improvement depends on #picks/aisle


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6. Sorting with AMRs


Robotic sorting systems

R. de Koster (c), Warehouse Robotics

puzzle-based storage – store and sort

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Implementation of GridFlow, KIT – courtesy: Kai Furmans

Robotic sortingConveyor/shuttle based

• FlexConveyor

• GridFlow

• GridSorter

AMR-based

• Tompkins T-sort

• LiBiao

• ….

R.de Koster, Warehouse Robotics 76

GridSorter – Gue et al., 2014, IEEE T on Automation science and Engineering

Robotic sortingConveyor/shuttle based

• FlexConveyor

• GridFlow

• GridSorter

AMR-based

• Tompkins T-sort

• LiBiao

• ….

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GridSorter – Gue et al., 2014, IEEE T on Automation science and Engineering

Only 3 vehicles

Raviv et al, (2021), working paper

https://youtu.be/Gla3A5OMK7g

Robot sorting model


a. Dual-lane (shortest) Path b. Single-lane (detour) Path

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More flexible, but also cheaper!


Zou, De Koster, Gong (2021), Transportation Science

Robot sorting systems

René de Koster | Warehouse Robotics 80

Implications:

+ In many cases cheaper than traditional sorters

+ Flexible in layout, space use

- Less flexible in parcel sizes that can be handled

- Manual parcel induction is not ergonomic

7. Wrap-up


Are Bots Going to Take Away a Picker’s Role?

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Not very

soon!


Wrap-up

Pick robots are here to come and here to stay


Wrap-up


But, humans will remain important

Warehouse performance is largely determined by the collaboration and how this

is managed


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Wrap-up


But, humans will remain important

Warehouse performance is largely determined by the collaboration and how this

is managed

Topics for academic research

- How to deploy and control cobot technologies; impact on performance?

- For collaborative robotic systems: How do people interact with these

systems?


Questions: [email protected]


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cobot order picking sci 2021 public

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