Abstract:

Time-constrained intermediate storage multiproduct batch process scheduling with uncertain processing time is concerned in this paper. The triangular fuzzy number is applied to describe the imprecise processing time of products. An approach for ranking fuzzy numbers is used to estimate the value and uncertainty of the makespan which are employed to establish the mathematical model. An improved particle swarm optimization with estimation of distribution algorithm (IPSO-EDA) is proposed. The IPSO-EDA incorporates the global statistical information collected from personal best solutions of all particles into the particle swarm optimization (PSO), and therefore each particle has comprehensive search ability. Meanwhile, the NEH-based initialization and local search are introduced to construct good initial solutions and enhance the local exploitation, respectively. In addition, the influence of parameter settings of the IPSO-EDA is investigated based on the method of factorial design. The simulation results indicate the superiority of IPSO-EDA in terms of effectiveness and efficiency.

Key words: batch process, scheduling, intermediate storage, uncertain, particle swarm optimization, estimation of distribution algorithm

摘要：

针对产品处理时间不确定条件下中间存储时间有限多产品间歇生产过程调度问题, 采用三角模糊数描述处理时间的不确定性, 通过一种模糊排序的方法建立了以最小化模糊最大完工时间的值以及不确定度作为调度目标的数学模型, 提出一种基于改进粒子群和分布估计的混合算法(IPSO-EDA)。IPSO-EDA算法在粒子群更新公式中引入基于所有粒子自身最优位置的优质个体分布信息, 提高了算法的全局搜索能力, 同时采用NEH初始化获得理想的初始解, 采用NEH局部搜索提高算法的局部搜索能力。通过正交实验设计对算法的参数进行调节, 仿真结果表明了所提出算法的有效性和优越性。

关键词: 间歇过程, 生产调度, 中间存储, 不确定, 粒子群优化, 分布估计算法