A Hybrid Evolutionary Hyper-Heuristic Approach for Intercell Scheduling Considering Transportation Capacity

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@Article{Li:2016:ieeeASE,

• author = "Dongni Li and Rongxin Zhan and Dan Zheng and Miao Li and Ikou Kaku",
• journal = "IEEE Transactions on Automation Science and Engineering",
• title = "A Hybrid Evolutionary Hyper-Heuristic Approach for Intercell Scheduling Considering Transportation Capacity",
• year = "2016",
• volume = "13",
• number = "2",
• pages = "1072--1089",
• abstract = "The problem of intercell scheduling considering transportation capacity with the objective of minimizing total weighted tardiness is addressed in this paper, which in nature is the coordination of production and transportation. Since it is a practical decision-making problem with high complexity and large problem instances, a hybrid evolutionary hyper-heuristic (HEH) approach, which combines heuristic generation and heuristic selection, is developed in this paper. In order to increase the diversity and effectiveness of heuristic rules, genetic programming is used to automatically generate new rules based on the attributes of parts, machines, and vehicles. The new rules are added to the candidate rule set, and a rule selection genetic algorithm is developed to choose appropriate rules for machines and vehicles. Finally, scheduling solutions are obtained using the selected rules. A comparative evaluation is conducted, with some state-of-the-art hyper-heuristic approaches which lack some of the strategies proposed in HEH, with a meta-heuristic approach that is suitable for large scale scheduling problems, and with adaptations of some well-known heuristic rules. Computational results show that the new rules generated in HEH have similarities to the best-performing human-made rules, but are more effective due to the evolutionary processes in HEH. Moreover, the HEH approach has advantages over other approaches in both computational efficiency and solution quality, and is especially suitable for problems with large instance sizes.",
• keywords = "genetic algorithms, genetic programming, Job shop scheduling, Processor scheduling, Search problems, Vehicles, Discrete event systems, manufacturing automation, scheduling, transportation",
• DOI = "doi:10.1109/TASE.2015.2470080",
• ISSN = "1545-5955",
• month = apr,
• notes = "Also known as \cite{7270346}",

}

Genetic Programming entries for Dongni Li Rongxin Zhan Dan Zheng Miao Li Ikou Kaku

Citations