A genetic algorithm-estimation of distribution algorithm for a kind of heterogeneous parallel machine scheduling problem with multiple operations in chemical production

LI Zuocheng; QIAN Bin; HU Rong; LUO Rongjuan; ZHANG Guilian

doi:10.3969/j.issn.0438-1157.2014.03.031

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A genetic algorithm-estimation of distribution algorithm for a kind of heterogeneous parallel machine scheduling problem with multiple operations in chemical production

更新时间：2025-12-10

- A genetic algorithm-estimation of distribution algorithm for a kind of heterogeneous parallel machine scheduling problem with multiple operations in chemical production
- CIESC Journal Issue 3, Pages: 981-992(2014)
- 作者机构：
  
  1. 昆明理工大学信息工程与自动化学院,云南,昆明,650500
  2. 云南省计算机技术应用重点实验室,云南,昆明,650500
  3. 云南大学经济学院,云南,昆明,650091
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0438-1157.2014.03.031
  CLC： Q021.8
- Online First：05 March 2014，
  
  Published：05 March 2014
- 稿件说明：
移动端阅览
LI Zuocheng, QIAN Bin, HU Rong, et al. A genetic algorithm-estimation of distribution algorithm for a kind of heterogeneous parallel machine scheduling problem with multiple operations in chemical production[J]. CIESC Journal, 2014, (3): 981-992.
DOI：

LI Zuocheng, QIAN Bin, HU Rong, et al. A genetic algorithm-estimation of distribution algorithm for a kind of heterogeneous parallel machine scheduling problem with multiple operations in chemical production[J]. CIESC Journal, 2014, (3): 981-992. DOI： 10.3969/j.issn.0438-1157.2014.03.031.

摘要

针对化工生产中广泛存在的一类带多工序的异构并行机调度问题，即部分产品需多工序加工，同时不同产品间带序相关设置时间的异构并行机调度问题（heterogeneous parallel machine scheduling problem with multiple operations and sequence-dependent setup times， HPMSP_MOSST），提出了一种遗传-分布估计算法（genetic algorithm-estimation of distribution algorithm， GA-EDA），用于优化最早完工时间（makespan）。首先，提出了一种基于GA的概率模型训练机制，用来提高概率模型在算法进化初期的信息积累量，进而提高搜索的效率；其次，设计了一种有效的GA与EDA混合策略，使得算法的全局探索和局部开发能力得到合理平衡。计算机模拟验证了GA-EDA的有效性和鲁棒性。

Abstract

A genetic algorithm-estimation of distribution algorithm (GA-EDA) was proposed to optimize the makespan criterion for a kind of heterogeneous parallel machine scheduling problem

i.e.

the heterogeneous parallel machine scheduling problem with multiple operations and sequence-dependent setup times (HPMSP_MOSST)

which widely existed in chemical production. Firstly

a probability model training mechanism based on GA was presented and used to increase the information accumulation of the probability model at the initial stage of the evolution

and then the efficiency of search was improved. Secondly

an effective hybrid strategy of GA and EDA was designed to help the algorithm achieve a reasonable balance between global exploration and local exploitation abilities. Computer simulation showed the effectiveness and robustness of the proposed GA-EDA.

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