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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

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!

  • 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

  • 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

  • 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


Three Principal Parameters


Preference functions for Block and Crane agents


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

  • Multi-agent simulation GUI

  • Implemented in NETLOGO

  • Stationary block and Mobile crane agents


Design of Experiments


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


  • 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 YouQuestions ?


A sample example


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