A Genetic Programming-Based Scheduling Approach for Hybrid Flow Shop With a Batch Processor and Waiting Time Constraint

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@Article{Qin:2019:ASE,

• author = "Ming Qin and Runsen Wang and Zhongshun Shi and Lingxuan Liu and Leyuan Shi",
• journal = "IEEE Transactions on Automation Science and Engineering",
• title = "A Genetic Programming-Based Scheduling Approach for Hybrid Flow Shop With a Batch Processor and Waiting Time Constraint",
• year = "2019",
• abstract = "This article investigates a hybrid flow shop scheduling problem that consists of a batch processor in the upstream and a discrete processor in the downstream. Limited waiting time between the batch processor and discrete processor is taken into consideration. Such a scheduling problem is commonly seen as bottlenecks in the production of precision parts, back-end process of semiconductor products, and glass and steel industries. A mixed-integer linear programming model is presented to minimize the makespan. Considering the complexity of this problem and the imperative requirement in real-time optimization, we first develop a constructive heuristic together with the worst case analysis by exploiting the key decision structure of the problem. Based on the decision structure, we then develop a learning-based scheduling approach via customized genetic programming to automatically generate effective heuristics for this problem. Lower bounds are also developed to provide a measurement for the performance of proposed algorithms. Numerical results show that our proposed algorithms outperform the existing metaheuristics and are capable of providing high-quality solutions using less computational time.",
• keywords = "genetic algorithms, genetic programming, Job shop scheduling, Processor scheduling, Batch production systems, Heating systems, Ovens, Approximation algorithm, batch processor, flow shop, genetic programming (GP)",
• DOI = "doi:10.1109/TASE.2019.2947398",
• ISSN = "1558-3783",
• notes = "Also known as \cite{8896881}",

}

Genetic Programming entries for Ming Qin Runsen Wang Tony Zhongshun Shi Lingxuan Liu Leyuan Shi

Citations