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A container allocation model at container terminals with double wide gantry cranes

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A Trolley Routing and Scheduling Strategy for Low-Viaduct Rail Transportation ... Number of trolleys = 2. Number of containers 420 imports 420 exports ... – PowerPoint PPT presentation

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Title: A container allocation model at container terminals with double wide gantry cranes


1
A container allocation model at container
terminals with double wide gantry cranes
  • 11-04-2007
  • Presentation by
  • Karthik Mohan
  • Advisor Dr. Anne Goodchild
  • Civil and Environmental Engineering
  • University of Washington, Seattle

2
Problem statement
  • Minimize processing time containers at storage
    yard container terminal double-wide yard gantry
    cranes

3
Motivation and significance
  • Container terminals efficiency operating costs,
    congestion, throughput
  • Operational efficiencies New container terminal
    system LVRT (Low Viaduct Horizontal Rail
    Transportation System) Double wide yard crane,
    horizontal rail interface, Automated Guided
    vehicles (AGVs)
  • Application to non-automated, automated systems,
    DSS

4
Generic container terminal
5
LVRT system
LVRTS / Automated Container terminal system
Water side equip. Gantry crane
Interface Two way Rail system, Gantry Crane
on viaduct, AGVs
Land side equipment Double-wide Gantry crane
on rail, AGVs
6
LVRT system contd.
  • Quay crane
  • Container ship
  • Ref Liangcai Dong, Anne Goodchild, and Hanzhi
    Ding. A Trolley Routing and Scheduling Strategy
    for Low-Viaduct Rail Transportation System with
    Double-Wide Yard Crane. Transportation Research
    Board Annual Meeting 2007

7
Assumptions in the study
  • Problem scope
  • Half-bay import/export
  • Two trolleys and three cranes
  • Capacity of bay 60 containers
  • Influence area
  • Containers continuously loaded and unloaded from
    ship Imports picked hoist
  • Export containers previous day
  • Assume no rehandling of containers required

8
Problem formulation
Export/ Import assignment
Transfer and Drop-off/pick-up
Non-overlapping Influence areas
Bay capacity
3) Constraints
Minimize Processing time
LVRT problem
1) Objective
2) Decision variables
Import transfer
Export transfer
Import drop-off
Export Pick-up
Section bay 1
Section bay 2
9
Decision variables
Section bay 1
Section bay 2
Import Drop-off bay

Import transfer bay
Export transfer bay
Export Pick-up bay
10
Model components
  • Optimization model G.A.
  • Objective function Simulation model

11
Simulation model
  • Used to compute the objective function
  • Discrete event deterministic simulation
  • Simulation based on cycles
  • In each cycle, one import and one export
    container processed
  • Entities in simulation Cranes and trolleys
  • 5 states for crane and 3 states for the trolleys
  • Next event Chosen based on current time (time at
    last updation) comparisons of trolleys at state
    changes

12
Simulation example
Event Pick trolley 1 State change 0 gt 1
Trolley 1 Current state 0 Current time 400
Trolley 2 Current state 2 Current time 405
13
Simulation example
Event Pick trolley 2 State change 2 gt 0
Trolley 1 Current state 1 Current time 412
Trolley 2 Current state 2 Current time 405
14
Simulation example
Event Pick trolley 1 State change 1 gt 2
Trolley 1 Current state 1 Current time 412
Trolley 2 Current state 0 Current time 489
15
Solution Methodology for the model
Start
  • yes
  • no

Initial solution set
New solution set
Selection
Fitness evaluation through sim. model for each
solution in pop.
Is gen. lt total Gen.
Mutation
Crossover
Stop
16
G.A. parameters
  • Mutation probability 0.0001
  • Crossover probability 0.9
  • Pop size 300 500
  • Generations 600 800
  • Implementation NSGA II

17
Sensitivity to no. generations
18
Sensitivity to pop. size
19
Input to the model
  • Crane speed 0.91 ft/s (1.2 m/s)
  • Trolley speed 0.366 ft/s (3 m/s)
  • Number of bays 15
  • Capacity of each bay 265 60
  • Number of cranes 3
  • Number of trolleys 2
  • Number of containers 420 imports 420 exports
  • Time taken to unload/load a container 60
    seconds
  • Time taken at the hoist 102 seconds

20
Processing time for different trolley speeds
21
Processing time for different crane speeds
22
Sensitivity of section bays to trolley speed
23
Section bay variation with trolleyspeeds
Trolley Speed 1.2
Trolley Speed 2
Trolley Speed 4
24
Conclusions
  • Takes 13 hrs to process 840 containers
  • Processing time is more sensitive to trolley
    speeds than crane speeds (24 improvement - 1.2
    m/s to 4 m/s for trolley as compared to 5.6
    improvement 1.2m/s to 4m/s for crane)
  • Storage section bays move away from the hoist as
    the trolley speed increases

25
The road ahead
LVRT model Future work
Adding one more crane/ trolley
Dynamic variation of Storage sections
Extension to other models
26
(No Transcript)
27
Additional slides
28
(No Transcript)
29
Simulation variables
  • Trolley
  • current time - ct_t(i),
  • current state - state_t(i),
  • current cycle
  • Crane
  • current time - ct_c(j)
  • current state - state_c(j)
  • current distance from the hoist

30
Simulation model flowchart
Pick trolley i
Start

  • no
    no
  • yes yes
    yes
  • yes no
  • no
  • yes

Is State_t (i) 2
Is State_t (i) 1
Is State_t (i) 0
Update cycle i
Update Ct_c (j) State_c (j) 1,2,0
Update Ct_c (j) State_c (j) 3,4,0
Check for termination
Update Ct_t (i) Set State_t (i) 0
Update Ct_t (i) Set State_t (i) 2
Update Ct_t (i) Set state_t (i) 1
Terminate
Is Ct_t (i) gt Ct _t(1-i)
i 1-i
31
Simulation model
  • No
  • Yes

Pick trolley i
Current state , current time
New current state, New current time
Is New current time i gt Current time(1-i)
Set i 1-i
32
Genetic Algorithms (G.A.)

33
Outline
  • Problem Statement
  • Motivation for the study
  • Problem formulation
  • Model components
  • Results
  • Future work
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