Genetic Programming with Delayed Routing for Multi-Objective Dynamic Flexible Job Shop Scheduling

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@Article{Binzi-Xu:EC,

• author = "Binzi Xu and Yi Mei and Yan Wang and Zhicheng Ji and Mengjie Zhang",
• title = "Genetic Programming with Delayed Routing for Multi-Objective Dynamic Flexible Job Shop Scheduling",
• journal = "Evolutionary Computation",
• year = "2021",
• volume = "29",
• number = "1",
• pages = "75--105",
• month = "Spring",
• keywords = "genetic algorithms, genetic programming, dynamic flexible job shop scheduling, dispatching rule discovery, delayed routing, energy efficiency",
• ISSN = "1063-6560",
• URL = "https://meiyi1986.github.io/publication/xu-2020-genetic/xu-2020-genetic.pdf",
• URL = "https://meiyi1986.github.io/publication/xu-2020-genetic/",
• DOI = "doi:10.1162/evco_a_00273",
• size = "31 pages",
• abstract = "Dynamic Flexible Job Shop Scheduling (DFJSS) is an important and challenging problem, and can have multiple conflicting objectives. Genetic Programming HyperHeuristic (GPHH) is a promising approach to fast respond to the dynamic and unpredictable events in DFJSS. A GPHH algorithm evolves dispatching rules (DRs) that are used to make decisions during the scheduling process (i.e. the so-called heuristic template). In DFJSS, there are two kinds of scheduling decisions: the routing decision that allocates each operation to a machine to process it, and the sequencing decision that selects the next job to be processed by each idle machine. The traditional heuristic template makes both routing and sequencing decisions in a non-delay manner, which may have limitations in handling the dynamic environment. we propose a novel heuristic template that delays the routing decisions rather than making them immediately. This way, all the decisions can be made under the latest and more accurate information. We propose three different delayed routing strategies, and automatically evolve the rules in the heuristic template by GPHH. We evaluate the newly proposed GPHH with Delayed Routing (GPHH-DR) on a multi-objective DFJSS that optimises the energy efficiency and mean tardiness. The experimental results show that GPHH-DR significantly outperformed the state-of-the-art GPHH methods. We further demonstrated the efficacy of the proposed heuristic template with delayed routing, which suggests the importance of delaying the routing decisions.",
• notes = "School of Electrical Engineering, Anhui Polytechnic University, Wuhu, China

  ",

}

Genetic Programming entries for Binzi Xu Yi Mei Yan Wang Zhicheng Ji Mengjie Zhang

Citations