Minimizing energy consumption and cycle time in two-sided robotic assembly line systems using restarted simulated annealing algorithm
Minimizing energy consumption and cycle time in two-sided robotic assembly line systems using restarted simulated annealing algorithm
复制标题
使用重新启动的模拟退火算法最大限度地减少两侧机器人装配线系统的能耗和周期时间
DOI：
10.1016/j.jclepro.2016.06.131
复制
发表时间：
2016-11-01
影响因子：
11.1
通讯作者：
Zhang, LiPing
中科院分区：
文献类型：
--
作者：
Li, Zixiang;Tang, Qiuhua;Zhang, LiPing
双面装配线通常用于生产大型大批量产品。近年来，机器人被广泛应用于这条生产线，以取代人工处理和管理分配的任务。对于机器人装配线来说，能源消耗是一项主要的支出，能源成本的增加越来越受到制造企业的关注。据笔者所知，目前还没有关于双面机器人装配线能耗的研究报道。提出了一种新的混合整数规划模型，使能耗和循环时间同时最小化。针对模型的复杂性，提出了一种重新启动模拟退火算法，利用具有三个相邻结构的新局部搜索和基于拥挤距离分配过程的重新启动阶段来获得扩散良好的帕累托最优集。设计了测试用例来衡量所提方法的性能，并将重新启动模拟退火算法与快速精英非支配排序遗传算法进行了比较。计算结果表明，该模型能够有效地降低总能量消耗，重新启动模拟退火算法在收敛和扩散准则上都优于非支配排序遗传算法。(C) 2016 Elsevier Ltd.版权所有。
Two-sided assembly lines are usually utilized to produce large-sized high-volume products. Recently robots are widely utilized in this line to replace the manual handling and manage the allocated tasks. For a robotic assembly line, the energy consumption is a major expense and the increased energy cost draws much more attentions from manufacturing enterprises. To the best knowledge of the authors, there is no research reported on the energy consumption of two-sided robotic assembly line. This paper presents a new mixed-integer programming model to minimize the energy consumption and cycle time simultaneously. A restarted simulated annealing algorithm is developed to deal with the complexity of the model, which utilizes new local search with three neighbor structures and restart phase based on the crowding distance assignment procedure to obtain well-spread Pareto-optimal set. Testing cases are designed to measure the performance of the proposed method and the restarted simulated annealing algorithm is compared with the fast elitist non-dominated sorting genetic algorithm. The computational results demonstrate that the proposed model is useful to reduce the total energy consumption and the restarted simulated annealing algorithm outperforms the non-dominated sorting genetic algorithm in both convergence and spread criteria. (C) 2016 Elsevier Ltd. All rights reserved.