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Presentation at Graduate Student Day, 2012 Inter-Block Yard Crane Scheduling a t a Marine Container Terminal by Omor Sharif, Nathan Huynh, Mashrur Chowdhury , Jose Vidal Paper submitted to International Journal of Transportation Science and Technology.

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Presentation at graduate student day 2012 inter block yard crane scheduling

Presentation at Graduate Student Day, 2012

Inter-Block Yard Crane Scheduling

at a Marine Container Terminal

by

Omor Sharif, Nathan Huynh, MashrurChowdhury, Jose Vidal

Paper submitted to

International Journal of Transportation Science and Technology


A very brief review of a container terminal

A Very Brief Review of A Container Terminal!

  • An interface between ocean and land transport

  • Containers are loaded/unloaded to/from a ship

  • Operation involves a large number of decisions

  • Capacity Constraints, Environmental Concerns etc


Research problem and motivation

Research problem and Motivation

  • Container Yard and Yard Crane (YC)

  • A container yard is made up of several blocks of containers

  • Workload varies among blocks during operational hours

  • Efficiency of YCs impacts truck waiting/ship waiting time

  • Assign/relocate YCs among blocks to finish most work

  • Known as Interblock YC Scheduling Problem


Model assumptions

Model Assumptions

  • Total operational hours is divided in several planning periods

  • A workload forecast for blocks is known at start (time-units)

  • At most two cranes work at a block at the same time

  • At most one transfer per planning period

  • Other operations will not introduce delay

  • 10 minutes transfer time for each longitudinal block traveled


Initial assignment of yard cranes

Initial Assignment of yard cranes


Three principal parameters

Three Principal Parameters


Preference functions for block and crane agents

Preference functions for Block and Crane agents


An algorithm to assign cranes to blocks

An algorithm to assign cranes to blocks

  • 1. Each crane j proposes to first block i from its preference list

  • 2. Each block i receiving more than qi proposals, ‘holds’ the most preferred qi cranes and rejects all others.

  • n. Each crane j rejected at step n − 1 removes the block i rejecting the crane from its preference list.

  • Then the rejected crane j makes a new proposal to its next most preferred block i who hasn’t yet rejected it.

  • Go to step n − 1.


Implementation

Implementation

  • Multi-agent simulation GUI

  • Implemented in NETLOGO

  • Stationary block and Mobile crane agents


Design of experiments

Design of Experiments


Results percentage incomplete work volume case i average number of cranes per block 1 0

ResultsPercentage incomplete work volume: Case I - average number of cranes per block = 1.0


Results contd percentage incomplete work volume case ii average number of cranes per block 1 5

Results (Contd..)Percentage incomplete work volume: Case II - average number of cranes per block = 1.5


Concluding remarks

  • Summary of Findings

  • Future Work

Concluding remarks

  • In ‘medium’ condition all work can be finished

  • In ‘heavy’ condition the percentage incomplete is 1% or less

  • In ‘above capacity’ condition the percentage remaining is within 3% of the lower bound.

  • Scalable; a test case with 30 blocks can be solved within 3 seconds

  • Consider relocating cranes multiple time

  • Include forecasts for multiple planning periods

  • Solve integrated problems involving other related decisions


Thank you questions

Thank YouQuestions ?


A sample example

A sample example


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