Mixed-Integer Nonlinear Programming for Energy-Efficient Container Handling

Formulation and Customized Genetic Algorithm

Journal Article (2022)
Authors

J. Xin (Zhengzhou University)

Chuang Meng (Zhengzhou University)

Andrea D’Ariano (University of Roma Tre)

Dongshu Wang (Zhengzhou University)

R.R. Negenborn (TU Delft - Transport Engineering and Logistics)

Research Group
Transport Engineering and Logistics
Copyright
© 2022 J. Xin, Chuang Meng, Andrea D'Ariano, Dongshu Wang, R.R. Negenborn
To reference this document use:
https://doi.org/10.1109/TITS.2021.3094815
More Info
expand_more
Publication Year
2022
Language
English
Copyright
© 2022 J. Xin, Chuang Meng, Andrea D'Ariano, Dongshu Wang, R.R. Negenborn
Research Group
Transport Engineering and Logistics
Issue number
8
Volume number
23
Pages (from-to)
10542-10555
DOI:
https://doi.org/10.1109/TITS.2021.3094815
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

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.

Files

Mixed_Integer_Nonlinear_Progra... (pdf)
(pdf | 2.74 Mb)
- Embargo expired in 01-07-2023
License info not available