Multi-objective evolutionary simulated annealing optimisation for mixed-model multi-robotic disassembly line balancing with interval processing time
Multi-objective evolutionary simulated annealing optimisation for mixed-model multi-robotic disassembly line balancing with interval processing time
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DOI：
10.1080/00207543.2019.1602290
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发表时间：
2019-04
影响因子：
9.2
通讯作者：
Yilin Fang;Hao Ming;Miqing Li;Quan Liu;D. Pham
中科院分区：
文献类型：
--
作者：
Yilin Fang;Hao Ming;Miqing Li;Quan Liu;D. Pham
研究了不确定条件下多机器人工作站混流拆卸线的设计与平衡问题。不同型号的任务由具有不同拆卸能力的机器人同时执行。机器人具有不同的任务时间和能量消耗。任务优先关系图用于描述任务之间的优先关系。考虑到拆卸过程中的不确定性，任务处理时间被假定为区间数。提出了一个混合整数数学规划模型，以最小化周期时间，峰值工作站能耗，总能耗。该模型在实际的拆卸线系统中具有重要的管理意义。由于所研究的问题是NP难的，基于进化模拟退火算法的元启发式方法。实验结果表明，该算法在优化质量和计算效率上优于其他多目标算法。
This paper considers the design and balancing of mixed-model disassembly lines with multi-robotic workstations under uncertainty. Tasks of different models are performed simultaneously by the robots which have different capacities for disassembly. The robots have unidentical task times and energy consumption respectively. Task precedence diagrams are used to model the precedence relations among tasks. Considering uncertainties in disassembly process, the task processing times are assumed to be interval numbers. A mixed-integer mathematical programming model is proposed to minimise the cycle time, peak workstation energy consumption, and total energy consumption. This model has a significant managerial implication in real-life disassembly line systems. Since the studied problem is known as NP-hard, a metaheuristic approach based on an evolutionary simulated annealing algorithm is developed. Computational experiments are conducted and the results demonstrate the proposed algorithm outperforms other multi-objective algorithms on optimisation quality and computational efficiency.