与无关的平行机器安排问题的混合流动商店是一个典型 NP 难的组合优化问题,并且它在化学药品广泛地存在,生产并且药品的工业。在这个工作,为与无关的平行机器(HFSPUPM ) 安排问题的混合流动商店的一个新奇数学模型被建议。另外,分发算法的一个有效混合评价被建议解决 HFSPUPM,利用在数学模型的特征。在优化算法,一个新单个表示方法被采用。(EDA ) 当教学习基于的优化(TLBO ) 策略被用于本地搜索时,结构被用于全球搜索。基于 HFSPUPM 的结构,这个工作介绍一系列分离操作。模拟结果与另外的算法相比显示出建议混合算法的有效性。
The hybrid flow shop scheduling problem with unrelated parallel machine is a typical NP-hard combinatorial optimization problem, and it exists widely in chemical, manufacturing and pharmaceutical industry. In this work, a novel mathematic model for the hybrid flow shop scheduling problem with unrelated parallel machine(HFSPUPM) was proposed. Additionally, an effective hybrid estimation of distribution algorithm was proposed to solve the HFSPUPM, taking advantage of the features in the mathematic model. In the optimization algorithm, a new individual representation method was adopted. The(EDA) structure was used for global search while the teaching learning based optimization(TLBO) strategy was used for local search. Based on the structure of the HFSPUPM, this work presents a series of discrete operations. Simulation results show the effectiveness of the proposed hybrid algorithm compared with other algorithms.