Local search enhanced multi-objective PSO algorithm for scheduling textile production processes with environmental considerations
Local search enhanced multi-objective PSO algorithm for scheduling textile production processes with environmental considerations
复制标题
考虑环境因素的纺织生产过程调度的局部搜索增强多目标PSO算法
DOI：
10.1016/j.asoc.2017.08.013
复制
发表时间：
2017-12
影响因子：
8.7
通讯作者：
Wu Cheng
中科院分区：
文献类型：
--
作者：
Zhang Rui;Chang Pei Chann;Song Shiji;Wu Cheng
纺织品染色通常构成服装生产中的瓶颈工序，因为染色过程耗时且受到严重限制。同时，染色过程不可避免地产生水污染物的排放，特别是当所涉及的设备进行清洁操作时。排程除了可以改善生产绩效（如减少交货延误）外，还可以作为一种系统级的工具来减少污染物的排放。为此，我们制定了纺织印染过程调度问题作为一个双目标优化模型，其中一个目标是与拖期成本，而另一个目标反映的污染物排放水平。由于所产生的问题的NP-难性质，我们提出了一个多目标粒子群优化算法增强了特定问题的局部搜索技术（MO-PSO-L），以寻求高质量的非支配解决方案。该混合算法的特点是一个定制的解决方案初始化方法，一组时变参数和一些独特的机制，用于处理多目标优化（包括面向密度的解决方案排序和个人/全球最佳解决方案处理）。基于喷射链的局部搜索技术是专门设计用于改善一些有前途的解决方案，重点是污染相关的目标。所提出的解决方案的方法已被验证的计算实验上的一个大的测试实例与公平的比较与两个国家的最先进的算法。
Textile dyeing often constitutes a bottleneck procedure in the production of clothing because the dyeing process is time-consuming and heavily constrained. Meanwhile, dyeing processes inevitably produce emissions of water pollutants especially when the involved equipment undergoes cleaning operations. Scheduling could be utilized as a system-level tool to reduce the amount of pollutant emission besides its normal role for improving the production performance (eg, reducing delivery tardiness). To this end, we have formulated the textile dyeing process scheduling problem as a bi-objective optimization model, in which one objective is connected with tardiness cost while the other objective reflects the level of pollutant emission. Due to the NP-hard nature of the resulting problem, we have proposed a multi-objective particle swarm optimization algorithm enhanced by problem-specific local search techniques (MO-PSO-L) to seek high-quality non-dominated solutions. The proposed hybrid algorithm is characterized by a tailored solution-initialization method, a set of time-variant parameters and several unique mechanisms for dealing with multi-objective optimization (including density-oriented solution sorting and personal/global best solution handling). The local search technique based on ejection chains has been specifically designed for improving a number of promising solutions with a focus on the pollution-related objective. Superiority of the proposed solution approach has been verified by computational experiments on a large set of test instances together with fair comparisons with two state-of-the-art algorithms.
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DOI：
10.1016/j.engappai.2010.01.031
发表时间：
2010-09-01
影响因子：
8
作者：
Kashan, Ali Husseinzadeh;Karimi, Behrooz;Jolai, Fariborz
通讯作者：
Jolai, Fariborz
影响因子：
6.4
作者：
Potts, CN;Kovalyov, MY
通讯作者：
Kovalyov, MY
影响因子：
9.2
作者：
M. Abedi;Hany Seidgar;H. Fazlollahtabar;Rohollah Bijani
通讯作者：
M. Abedi;Hany Seidgar;H. Fazlollahtabar;Rohollah Bijani
影响因子：
2
作者：
L. L. Liu-L.;C. T. Ng;T. Cheng
通讯作者：
L. L. Liu-L.;C. T. Ng;T. Cheng
影响因子：
9.2
作者：
Shie-Gheun Koh *;Pyung-Hoi Koo;Jaewon Ha;Woon-Seek Lee
通讯作者：
Shie-Gheun Koh *;Pyung-Hoi Koo;Jaewon Ha;Woon-Seek Lee