Decentralized freight intelligence in

the parcel delivery industry

Rosemarie Cramer

Paul Buijs

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Traditional planning approaches

Centralized planning approach Decentralized planning approach

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Decentralized freight intelligence

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Decentralized freight intelligence

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Route efficiency vs situational awareness

› Goal: more efficient

operations for more driving

time

› Challenge: Drivers lose

some situational awareness

Loading

process

Way up

First to last

stop on-

route

Break

Waiting

time

Way downUnloading

process

16,85%

58,43%

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Experiment

› Field experiment in real-life operations

an international parcel delivery

company▪ 2 drivers – each six days in total

- 1 time benchmark

- 2 times some information

- 2 times more information

› Measure: search time

Segments LIFO

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Search times as % of stop time

(i) benchmark

5.23%

94.77%

15.11%

84.89%

5.55%

94.45%

(ii) segments (iii) LIFO

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Search time distribution over the trips

0:00

0:17

0:35

0:52

1:09

1:26

1:44

2:01

2:18

2:36

2:53

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65

Sea

rch t

ime

(m:s

s)

Parcel (chronological)

0:00

0:17

0:35

0:52

1:09

1:26

1:44

2:01

2:18

2:36

2:53

1 4 7

10

13

16

19

22

25

28

31

34

37

40

43

46

49

52

55

58

61

64

67

70

73

76

79

82

85

88

91

94

97

100

103

106

109

112

Sea

rch t

ime

(mm

:ss)

Parcel (chronological)

(ii) segments (iii) LIFO

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Decentralized intelligence

› Trade-off

▪ Efficiency gain by utilizing loading

time for driving

▪ Efficiency drop by loss of situational

awareness

› Use of decentralized information

▪ Efficiency drop can be mitigated

already with very little information

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Volume capacity

2% 2%

5%

11%

27%

24%

18%

8%

2% 1% 0% 0% 0% 0%0%

5%

10%

15%

20%

25%

30%

% o

f to

urs

No. parcerls per tour

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Efficiency gains

› Goal: More efficient operations ->

more parcels per vehicle -> less

vehicles on the road

› Means: use loading time for

driving, provide drivers with

information

› Routing constraints

▪ Total driving time constraints

▪ Volume constraints (no. of parcels

per vehicle)

No. of parcels per vehicle

Some information (segments)

More information

(LIFO)

160 86.8% 77.2%

155 88.7% 78.9%

150 90.4% 80.4%

145 92.9% 82.6%

140 97.1% 86.4%

135 104.6% 93.0%

130 110.1% 98.4%

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Key take-aways

› Decentralized freight intelligence may result in loss of

situational awareness, but this can be mitigated already

with very little information

› Reduction of 20% in the number of vehicles is feasible

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