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In-Depth Information
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the supervisory controller receives the minimal time from the lower level;
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the supervisory controller computes the schedule for all pieces of equipment;
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the supervisory controller sends the operation transport time to each piece
of equipment;
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QC, AGV and ASC receive the transport time to execute the relating task
and save energy if possible.
3.4 Heuristic Control of Equipment
In earlier works, each piece of equipment is considered to operate at its max-
imal speed (e.g. [2, 3]). Such a heuristic approach is here modified taking the
constraints of the speed and the acceleration into account. The schedule of
the supervisory controller is determined in the same way as in Section 3.1.
This alternative approach is considered for comparison in the study below.
4 Case Study
Next we present simulations in which three pieces of equipment are used to
transport containers from the quayside area to the stacking area. The trajectories
of the pieces of equipment have to be determined. We compare the performance
of the proposed hierarchical control architecture and the heuristic controller.
Several assumptions are made in this case study:
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The initial position of the QC is set to its unloading position. The initial
position of the AGV is set to its loading position. The initial position of the
ASC is set to its loading position;
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Each piece of equipment can only transport one container at a time;
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The service time of the QC, the AGV and the ASC are ignored;
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The length of a QC is 120m, the traveling distance of an AGV is 300m and
the moving distance of an ASC is 100m;
-
The maximum speed
v
max
oftheQC,AGVandASCareassumedtobe4
(m/s), 6 (m/s) and 4 (m/s), respectively;
-
The maximum acceleration
u
max
of the QC, AGV and ASC are assumed to
be 0.4 (m/s
2
), 1 (m/s
2
)and0.4(m/s
2
), respectively;
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The weight of trolley for the QC, the empty AGV and the ASC are assumed
to be 10t, 25t and 240t, respectively. The type of one container is considered
to be 20TEU with the weight of 25t.
The result of the scheduling problem (11) to (18) in the higher level is shown
in Tables 1 and 2. Based on the available operation time for each piece of equip-
ment to transport containers, the minimal energy consumption problem is locally
solved as a quadratic programming problem. The reduction of energy consump-
tion are demonstrated in Table 3. The resulted behaviors of each piece of equip-
ment are shown in Figure 7 and Figure 8.
Typically each piece of equipment inside a container terminal is operated
at its maximal speed as the basis of the scheduling problem, as outlined in
