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Multi-Objective Bi-Level Programming for the Energy-Aware Integration of Flexible Job Shop Scheduling and Multi-Row Layout

Zhang, Hongliang ; Ge, Haijiang ; Pan, Ruilin ; Wu, Yujuan

Algorithms, 2018-12, Vol.11 (12), p.210 [Periódico revisado por pares]

Basel: MDPI AG

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  • Título:
    Multi-Objective Bi-Level Programming for the Energy-Aware Integration of Flexible Job Shop Scheduling and Multi-Row Layout
  • Autor: Zhang, Hongliang ; Ge, Haijiang ; Pan, Ruilin ; Wu, Yujuan
  • Assuntos: bi-level programming model ; Efficiency ; Emissions ; Energy consumption ; Energy management ; Genetic algorithms ; improved multi-objective hierarchical genetic algorithm ; integration of FJSSP and MRWLP ; Job shop scheduling ; Job shops ; Layouts ; Manufacturing ; Manufacturing cells ; Materials handling ; Mixed integer ; Optimization ; Programming ; Scheduling
  • É parte de: Algorithms, 2018-12, Vol.11 (12), p.210
  • Descrição: The flexible job shop scheduling problem (FJSSP) and multi-row workshop layout problem (MRWLP) are two major focuses in sustainable manufacturing processes. There is a close interaction between them since the FJSSP provides the material handling information to guide the optimization of the MRWLP, and the layout scheme affects the effect of the scheduling scheme by the transportation time of jobs. However, in traditional methods, they are regarded as separate tasks performed sequentially, which ignores the interaction. Therefore, developing effective methods to deal with the multi-objective energy-aware integration of the FJSSP and MRWLP (MEIFM) problem in a sustainable manufacturing system is becoming more and more important. Based on the interaction between FJSSP and MRWLP, the MEIFM problem can be formulated as a multi-objective bi-level programming (MOBLP) model. The upper-level model for FJSSP is employed to minimize the makespan and total energy consumption, while the lower-level model for MRWLP is used to minimize the material handling quantity. Because the MEIFM problem is denoted as a mixed integer non-liner programming model, it is difficult to solve it using traditional methods. Thus, this paper proposes an improved multi-objective hierarchical genetic algorithm (IMHGA) to solve this model. Finally, the effectiveness of the method is verified through comparative experiments.
  • Editor: Basel: MDPI AG
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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