考虑碳限额的制造/再制造混合系统生产优化决策
作者:
作者单位:

武汉理工大学

作者简介:

通讯作者:

中图分类号:

TP391; F224

基金项目:

国家自然科学基金项目(面上项目,重点项目,重大项目)


Production Optimization Decision of Manufacturing/Remanufacturing under Carbon Emission Permits
Author:
Affiliation:

Wuhan University of Technology

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    在碳限额交易机制下,提出了考虑客户需求差异和库存成本的多周期产品制造/再制造混合生产优化问题,以最大化系统总利润和最小化碳排放量为目标建立了问题的数学模型,其中考虑了碳减排技术投入产生的成本和收益。提出了一种多种群混合布谷鸟算法(Multi-population Hybrid Cuckoo Search,MPHCS)。该算法采用多种群协同进化的思想,以优秀个体指导种群进化,实现各子种群信息共享;设计了一种自适应步长控制因子,改善算法进化阶段的搜索范围;提出了一种自定义偏好随机游走策略,增加优秀个体对全局搜索的影响;同时引入混沌搜索,用于对MPHCS算法全局搜索发现的优质解区域进行精细搜索,提高局部搜索能力。最后以某生产企业的混合生产计划为例,验证了该算法和模型的合理性和有效性。

    Abstract:

    Aiming at the decision-making of manufacturing/remanufacturing hybrid production under the Carbon Limit Trading Policy, a hybrid production decision model which maximized profit and minimized total carbon emissions was established for manufacturing/remanufacturing process of single products in multi-cycle with considering the impact of mission reduction technology investment. To solve the model, a multi-population hybrid cuckoo search (MPHCS) algorithm was proposed. Firstly, the idea of multi-group co-evolution was utilized to realize the information sharing of each sub-population and guide the population evolution with excellent individuals. Secondly, an adaptive factor of step size was designed to control the search scopes in the evolution phases. Thirdly, a self-defined preference random walk strategy is proposed to increase the influence of excellent individuals on global search. Fourth, the chaotic search was introduced to exploit the excellent sub-regions obtained by MPHCS’s global search, so that improved local search ability. Finally, an example of hybrid production planning in a company was addressed to validate the efficiency and rationality of the proposed algorithm.

    参考文献
    相似文献
    引证文献
引用本文
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2019-10-18
  • 最后修改日期:2021-03-23
  • 录用日期:2020-02-29
  • 在线发布日期:
  • 出版日期: