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A two stage yard crane workload partitioning and job sequencing algorithm for container terminals

Published: 22 March 2010 Publication History

Abstract

We propose a new YC workload partitioning scheme which employs dynamic data driven simulations to conduct what-if experiments in container terminals. Both the optimal partition of the workload in a row of yard blocks and the optimal dispatching sequences for individual YCs are achieved. The practical consideration of the safety constraints is included. A dynamic programming (DP) approach is used to avoid re-computation. An efficient two stage workload partition algorithm (TSWP) is proposed which successfully reduces the number of full what-if simulations while maintaining solution optimality. An effective lower bound (LB) generator with adjustable LB accuracy is designed in supporting the TSWP algorithm. Experimental results show that the TSWP algorithm outperforms the pure DP approach in all tested scenarios and takes less than 1 part per thousand computational time of the DP approach.

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      cover image ACM Conferences
      SAC '10: Proceedings of the 2010 ACM Symposium on Applied Computing
      March 2010
      2712 pages
      ISBN:9781605586397
      DOI:10.1145/1774088
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Published: 22 March 2010

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      Author Tags

      1. decision-making
      2. optimization
      3. yard crane dispatching

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      March 22 - 26, 2010
      Sierre, Switzerland

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      SAC '10 Paper Acceptance Rate 364 of 1,353 submissions, 27%;
      Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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