Mixed-Integer Nonlinear Programming for Energy-Efficient Container Handling: Formulation and Customized Genetic Algorithm

Jianbin Xin, Chuang Meng, Andrea D'Ariano, Dongshu Wang, Rudy R. Negenborn

Research output: Contribution to journalArticleScientificpeer-review

7 Citations (Scopus)
64 Downloads (Pure)

Abstract

Energy consumption is expected to be reduced while maintaining high productivity for container handling. This paper investigates a new energy-efficient scheduling problem of automated container terminals, in which quay cranes (QCs) and lift automated guided vehicles (AGVs) cooperate to handle inbound and outbound containers. In our scheduling problem, operation times and task sequences are both to be determined. The underlying optimization problem is mixed-integer nonlinear programming (MINLP). To deal with its computational intractability, a customized and efficient genetic algorithm (GA) is developed to solve the studied MINLP problem, and lexicographic and weighted-sum strategies are further considered. An $\epsilon $ -constraint algorithm is also developed to analyze the Pareto frontiers. Comprehensive experiments are tested on a container handling benchmark system, and the results show the effectiveness of the proposed lexicographic GA, compared to results obtained with two commonly-used metaheuristics, a commercial MINLP solver, and two state-of-the-art methods.

Original languageEnglish
Pages (from-to)10542-10555
JournalIEEE Transactions on Intelligent Transportation Systems
Volume23
Issue number8
DOIs
Publication statusPublished - 2022

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • Automated container terminals
  • Containers
  • Cranes
  • Energy consumption
  • energy efficiency
  • genetic algorithm.
  • Genetic algorithms
  • Job shop scheduling
  • mixed-integer nonlinear programming
  • Optimization
  • Task analysis

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