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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
04/11/23 A.Goussiatiner 3
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.
04/11/23 A.Goussiatiner 4
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
04/11/23 A.Goussiatiner 5
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
04/11/23 A.Goussiatiner 6
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
04/11/23 A.Goussiatiner 7
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
04/11/23 A.Goussiatiner 8
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
04/11/23 A.Goussiatiner 9
Why does this matter?
04/11/23 A.Goussiatiner 9
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
04/11/23 A.Goussiatiner 10
Energy Regeneration Technologies vs. EECS
04/11/23 A.Goussiatiner 10
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
04/11/23 A.Goussiatiner 11
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.