energy efficient container stacking

Alex Goussiatiner P.Eng., Senior Container Terminal and Transportation Specialist

Sandwell Engineering Inc.Vancouver, Canada

04/11/23 A.Goussiatiner 2

EECS Software Component

Reduces average energy consumption per TEU [kWh/teu] in the container stack by deploying ‘optimized’ placement strategy


Q: Where does the energy come from?

A: From existing operational deficiencies:

Large number of the unproductive moves (reshuffling) during retrieval from the stack

Inefficient handling of the heavy containers. Energy required for hosting and trolley traveling depends on the container weight. Not taking into account the weight leads to the situation when ‘heavy’ lifts are traveled the same distances as the ‘light’ lifts.


Reinforcement Learning: Science of making compromises between short and

long term gains

first three elements in the formula gives us an estimation for the complete power input required for handling container during its life time inside the section. This sum represents immediate factors that are used for the decision making

last element is an estimation of the energy input after action is taken and represents long term consequences

( ) ( ) ( ) ( ) ( ) ( )t pl t t res ret t t ta y a p a y gw y a we a “Policy” Function


Reshuffling Index (analysis of the container columns)

1 2 3 4( ) 1 ((1 ( , ))*(1 ( , ))*(1 ( , ))*(1 ( , ))t t t tP R P C C P C C P C C P C C

Note: Can be used to determine which column should be reshuffled during the house-keeping

1( , )tP C C - probability of the event that container

1C is retrieved before Ct


Reshuffling Probability (“Retrieval Distributions”)

0.0000

0.0500

0.1000

0.1500

0.2000

0.2500

0.3000

0 1 2 3 4 5 6 7 8 9 10 11

Retrieval Day

Ca Cb


Reshuffling Probability (“Who comes First”)

0 1 2 3 4 5 6 7 8

0.0253 0.1240 0.1013 0.1467 0.2847 0.1031 0.1031 0.0419 0.0140

0 0.0391 0.00099 0.00485 0.00396 0.00574 0.01113 0.00403 0.00403 0.00164 0.00055

1 0.2162 0.00548 0.02682 0.02191 0.03173 0.06157 0.02229 0.02229 0.00907 0.00302

2 0.2737 0.00693 0.03394 0.02773 0.04016 0.07792 0.02821 0.02821 0.01147 0.00382

3 0.2120 0.00537 0.02630 0.02148 0.03111 0.06037 0.02185 0.02185 0.00889 0.00296

4 0.0926 0.00235 0.01149 0.00938 0.01359 0.02637 0.00955 0.00955 0.00388 0.00129

5 0.0379 0.00096 0.00470 0.00384 0.00556 0.01079 0.00391 0.00391 0.00159 0.00053

6 0.0424 0.00107 0.00526 0.00430 0.00622 0.01207 0.00437 0.00437 0.00178 0.00059

7 0.0319 0.00081 0.00395 0.00323 0.00468 0.00908 0.00329 0.00329 0.00134 0.00045

8 0.0174 0.00044 0.00216 0.00177 0.00256 0.00497 0.00180 0.00180 0.00073 0.00024

9 0.0153 0.00039 0.00190 0.00155 0.00225 0.00437 0.00158 0.00158 0.00064 0.00021

10 0.0153 0.00039 0.00190 0.00155 0.00225 0.00437 0.00158 0.00158 0.00064 0.00021

11 0.0060 0.00015 0.00075 0.00061 0.00088 0.00171 0.00062 0.00062 0.00025 0.00008

0.229 0.5*0.143( , ) 0.452

0.666a bP C C


Learning Process

EECS interacts with the environment and learns by updating the prediction function. This prediction function is in a form of large lookup table which maps state-action pairs with

• For each step, upon generating action, CS remembers action and waits for outcome after container is retrieved from the section

• CS uses the outcome and performs correction for the in the lookup table

( )t te a


Why does this matter?


Reduce Energy

ConsumptionPer TEU

RL Component Implementation

Reduce Number

Of Unproductive

Moves

Increase Stack

Capacity

Reduce Pollution

Reduce OperatingExpenses

Increase Customer

Satisfaction

Reduce Number

Of Cranes

Reduce Capital

Cost

ReduceRoad Truck

Waiting

Reduce Handling of the

Heavy Loads


Energy Regeneration Technologies vs. EECS


Energy Regeneration Technologies

EECS

Reduce Energy

Consumption

Per TEU

YES YES

ReduceNumber Of Cranes

NO YES

Increase

Stack Capacity NO YES

Reduce Road Truck

Waiting NO YES

EECS and Energy Regeneration can complement each other


References

1. A. Goussiatiner, Energy Efficient Box Stacking, Container Management, May-June 2008.

2. Sutton, Richard S.; Andrew G. Barto. Reinforcement Learning: An Introduction. MIT Press. 1998.

energy efficient container stacking

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