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Energy-based storage assignment in a multi-aisle warehouse

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Abstract

An order picker performs repetitive tasks, which may result in fatigue, body pain, and injuries. Therefore, it is essential to approach the storage location assignment from an ergonomic standpoint as well. This study presents an energy consumption based optimization model for storage location assignment in an industrial warehouse that stores metal bars. Through a full factorial experiment, we study how product-location parameters like diameter, weight and location height influence time elements like pull and lift/lower time. Using this information, we predict the energy expenditure of pickers for each product-location combination. Then, we formulate an optimization model to decide the storage location assignment that effectively minimizes the total energy expenditure of pickers. The full factorial experiment shows that all the primary factors are significant. Moreover, computational experiments show that storage assignment based on energy optimization is significantly beneficial as compared to distance-based optimization. The energy expenditure decreases with increase in number of vertical levels. The model can predict the energy expenditure of order picker for different warehouse layout designs and help warehouse manager to decide slot height, number of storage locations, blocks and vertical levels. Finally, we derive insights for future research and practical applications based on our findings.

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Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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Authors and Affiliations

  1. Indian Institute of Management Lucknow, Lucknow, 226013, India

    Danish Nasir, Rakesh Venkitasubramony & Suresh Kumar Jakhar

Authors
  1. Danish Nasir
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  2. Rakesh Venkitasubramony
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  3. Suresh Kumar Jakhar
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Contributions

DN contributed to data collection, experimentation, coding, and analysis. RV and SJ contributed to the conception of the study, model development, interpretation of results, review, and editing. DN wrote the first draft of the manuscript and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Rakesh Venkitasubramony.

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Appendix 1

Appendix 1

h

k

n

p

opt. EE

EE

(dist. Opt.)

fmax

fmin

rel. imp(%)

0.3

2

5

29

30,968.69

33,143.55

290

281

7.02

10

15

26,388.33

28,306.27

300

281

7.27

15

10

25,450.09

27,369.46

300

281

7.54

20

8

25,188.58

27,308.45

320

281

8.42

3

5

19

21,863.77

25,878.63

285

281

18.36

10

10

18,993.79

23,089.07

300

281

21.56

15

7

18,437.62

22,877.21

315

281

24.08

20

5

18,622.93

22,885.58

300

281

22.89

4

5

15

18,109.22

22,204.26

300

281

22.61

10

8

16,182.12

20,196.92

320

281

24.81

15

5

16,182.57

20,244.41

300

281

25.10

20

4

16,130.68

20,834.31

320

281

29.16

5

5

12

16,393.58

19,468.99

300

281

18.76

10

6

15,166.28

18,916.80

300

281

24.73

15

4

15,152.97

18,765.86

300

281

23.84

20

3

15,408.30

19,065.37

300

281

23.73

6

5

10

15,429.78

19,219.76

300

281

24.56

10

5

14,551.14

18,147.61

300

281

24.72

15

4

14,289.41

18,379.35

360

281

28.62

20

3

14,497.83

18,151.50

360

281

25.20

h: location height in meters; k: no. of vertical levels; n: no. of blocks in each partition; p: no. of partitions; opt. EE: optimised energy expenditure in Kcal; EE(dist. Opt.): distance optimized energy expenditure in Kcal; fmax: total no. of locations; fmin: minimum no. of locations; rel. imp(%): relative improvement of optimized energy expenditure over distance optimized energy expenditure

h

k

n

p

opt. EE

EE (dist. Opt.)

fmax

fmin

rel. imp(%)

0.4

2

5

24

26,685.04

29,398.29

240

231

10.17

10

12

22,706.56

25,488.78

240

231

12.25

15

8

21,967.02

25,183.25

240

231

14.64

20

6

21,915.04

24,538.05

240

231

11.97

3

5

16

19,584.72

23,207.56

240

231

18.50

10

8

17,294.05

20,822.81

240

231

20.40

15

6

16,714.14

20,615.24

270

231

23.34

20

4

17,220.64

20,767.39

240

231

20.60

4

5

12

17,043.02

20,335.63

240

231

19.32

10

6

15,595.02

19,073.39

240

231

22.30

15

4

15,576.17

18,972.80

240

231

21.81

20

3

15,879.87

19,308.98

240

231

21.59

5

5

10

15,774.17

18,694.85

250

231

18.52

10

5

14,769.80

17,460.25

250

231

18.22

15

4

14,578.69

17,792.55

300

231

22.04

20

3

14,810.60

17,687.42

300

231

19.42

h

k

n

p

opt. EE

EE (dist. Opt.)

fmax

fmin

rel. imp(%)

0.5

2

5

21

22,517.69

26,227.35

210

205

16.47

10

11

18,879.56

22,780.16

220

205

20.66

15

7

18,447.06

22,287.98

210

205

20.82

20

6

18,006.22

22,258.91

240

205

23.62

3

5

14

17,876.31

21,935.58

210

205

22.71

10

7

15,935.25

19,414.25

210

205

21.83

15

5

15,611.22

19,023.59

225

205

21.86

20

4

15,651.77

19,596.84

240

205

25.21

4

5

11

15,970.44

19,000.62

220

205

18.97

10

6

14,640.25

17,935.09

240

205

22.51

15

4

14,624.79

17,962.39

240

205

22.82

20

4

15,489.92

19,206.82

320

244

24.00

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Nasir, D., Venkitasubramony, R. & Jakhar, S.K. Energy-based storage assignment in a multi-aisle warehouse. OPSEARCH 60, 1951–1975 (2023). https://doi.org/10.1007/s12597-023-00672-x

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