• CN:11-2187/TH
  • ISSN:0577-6686

机械工程学报 ›› 2021, Vol. 57 ›› Issue (6): 200-210.doi: 10.3901/JME.2021.06.200

• 交叉与前沿 • 上一篇    下一篇

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考虑产品制造过程内含能的选择性拆解规划能耗优化研究

任亚平1,2, 郭洪飞1,2, 张超勇3, 李磊4, 孟磊磊5, 屈挺1,2, 何平1,2   

  1. 1. 暨南大学智能科学与工程学院 珠海 519070;
    2. 暨南大学物联网与物流工程研究院 珠海 519070;
    3. 华中科技大学数字制造装备与技术国家重点实验室 武汉 430074;
    4. 合肥工业大学机械工程学院 合肥 230009;
    5. 聊城大学计算机学院 聊城 252059
  • 收稿日期:2020-04-27 修回日期:2020-10-15 出版日期:2021-03-20 发布日期:2021-05-25
  • 通讯作者: 郭洪飞(通信作者),男,1980年出生,博士,副教授,硕士研究生导师。主要研究方向为工业工程、智能制造及军民融合等。E-mail:ghf-2005@163.com
  • 作者简介:任亚平,男,1995年出生,博士,副教授。主要研究方向为可持续设计与制造、优化算法设计及应用。E-mail:renyp1@163.com
  • 基金资助:
    广东省基础与应用基础研究基金联合基金(2019A1515110399)、中央高校基本科研业务费专项资金(21620360)、广州市科技计划(202002030321)、广东省学位与研究生教育改革研究(2019JGXM15)、广州市创新领军团队(201909010006)、国家自然科学基金(51875251)、“广东特支计划”本土创新创业团队(2019BT02S593)、国家教育部“蓝火计划”(惠州)产学研联合创新基金(CXZJHZ20)、内蒙古自治区科技计划(2019GG238)和呼和浩特市科技计划重大专项(2020-高-重-4)资助项目。

Energy Consumption Optimization of Selective Disassembly Planning Considering Product Embodied Energy during Manufacturing

REN Yaping1,2, GUO Hongfei1,2, ZHANG Chaoyong3, LI Lei4, MENG Leilei5, QU Ting1,2, HE Ping1,2   

  1. 1. School of Intelligent Systems Science and Engineering, Jinan University, Zhuhai 519070;
    2. Institute of Physical Internet, Jinan University, Zhuhai 519070;
    3. State Key Lab of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074;
    4. School of Mechanical Engineering, Hefei University of Technology, Hefei 230009;
    5. School of Computer Science, Liaocheng University, Liaocheng 252059
  • Received:2020-04-27 Revised:2020-10-15 Online:2021-03-20 Published:2021-05-25

摘要: 废旧产品的回收利用以及再制造过程不仅可以实现产品资源的循环利用,促进循环经济发展,同时也能起到节能减排的作用。论文从产品生命周期角度剖析产品制造过程各环节/阶段产生的能耗,研究考虑产品制造过程内含能的选择性拆解规划(Selective disassembly planning, SDP)能耗优化问题,并计算高效节能的拆解方案,以实现废旧产品回收过程经济效益和节能效益的综合最佳。首先,基于SDP的基本数学模型和产品制造过程的内含能建立SDP能耗优化模型,考虑最大化废旧产品回收过程产生的利润和节省的能耗两个评估指标(决策目标);然后提出改进的人工蜂群算法(Improved artificial bee colony algorithm, IABC)对多目标SDP能耗优化模型进行高效求解,获取回收利润高、节能效果好的综合性拆解方案;最后,通过拆解回收废旧液晶电视的实际案例来验证论文构建的模型和提出的算法的可行性和有效性,计算结果表明SDP能耗优化模型的均衡性明显优于SDP的基本数学模型,IABC在收敛性、鲁棒性和计算效率方面均表现优越。

关键词: 回收再制造, 拆解规划, 内含能, 能耗优化, 人工蜂群算法

Abstract: The recovery and remanufacturing of end-of-life (EOL) products can not only realize the recycling of product resources and promote the development of circular economy, but also play a role in energy saving and emission reduction. From the perspective of the product life cycle, energy consumption generated is focused during each step/stage of the product manufacturing process and the optimization of energy consumption in the selective disassembly planning (SDP) is studied during the product manufacturing process. The decisions of disassembly level, disassembly sequence, and the recovery options of subassemblies are simultaneous made in our problem to identify the optimal disassembly solution that maximizes economic and energy-saving benefits from the recovery of EOL products. First, based on the basic mathematical model of SDP and embodied energy of the product manufacturing process, an energy consumption optimization model for SDP is established, considering two evaluation indicators (objectives) i.e. the maximum of profit generated and energy consumption saved from the recovery of EOL products. Then, an improved artificial bee colony algorithm (IABC) is proposed to efficiently solve the energy consumption optimization model of SDP and obtain a comprehensive disassembly solution with large recovering profit and good energy-saving benefit. Finally, a real case for recovering a used LCD television is applied to verify the feasibility and effectiveness of the proposed model and algorithm. The computational results show that the balance of the energy consumption optimization model of SDP is significantly better than the basic mathematical model of SDP, and IABC performs excellent in terms of the convergence, robustness, and computational efficiency.

Key words: recovery and remanufacturing, disassembly planning, embodied energy, energy consumption optimization, artificial bee colony algorithm

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