Multi-objective multi-population biased random-key genetic algorithm for the 3-D container loading problem-Reference-Cited by-同舟云学术

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Multi-objective multi-population biased random-key genetic algorithm for the 3-D container loading problem

Published:2015-11 Issue: Volume:89 Page:80-87
ISSN:0360-8352
Container-title:Computers & Industrial Engineering
language:en
Short-container-title:Computers & Industrial Engineering

Author:

Zheng Jia-Nian,Chien Chen-Fu,Gen Mitsuo

Funder

National Science Council Taiwan

Advanced Manufacturing and Service Management Research Center of National Tsing Hua University

Grant-in-Aid for Scientific Research (C) of Japan Society of Promotion of Science

Publisher

Elsevier BV

Subject

General Engineering,General Computer Science

Reference34 articles.

1. A comparative evaluation of heuristics for container loading;Bischoff;European Journal of Operational Research,1990

2. Re-entrant flow shop scheduling problem with time windows using hybrid genetic algorithm based on auto-tuning strategy;Chamnanlor;International Journal of Production Research,2014

3. An analytical model for the container loading problem;Chen;European Journal of Operational Research,1995

4. Using genetic algorithms (GA) and a coloured timed petri net (CTPN) for modeling the optimization-based schedule generator of a generic production scheduling system;Chien;International Journal of Production Research,2007

5. A novel timetabling algorithm for a furnace process for semiconductor fabrication with constrained waiting and frequency-based Setups;Chien;OR Spectrum,2007

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