Environment-Aware Production Schedulingfor Paint Shops in Automobile Manufacturing: A Multi-Objective Optimization Approach.
Environment-Aware Production Schedulingfor Paint Shops in Automobile Manufacturing: A Multi-Objective Optimization Approach.
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汽车制造涂装车间的环境感知生产调度：一种多目标优化方法
DOI：
10.3390/ijerph15010032
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发表时间：
2017-12-25
影响因子：
--
通讯作者：
Zhang R
中科院分区：
文献类型：
--
作者：
Zhang R
传统的生产流程调度方式往往侧重于利润驱动的目标（如周期时间或材料成本），而往往忽视了碳排放和其他不良副产品形式的制造活动对环境的负面影响。为了弥补这一差距，本文研究了汽车制造业中一个典型的喷漆车间的环境感知生产调度问题。在所研究的问题中，定义了一个目标函数，以尽量减少化学污染物的排放所造成的清洗油漆设备，必须进行每次之前的颜色变化发生。同时，由于下游装配车间相互关联，且由有限容量的缓冲区连接，因此也考虑了最小化下游装配车间的交货期违规问题。首先，我们已经开发了一个混合整数规划公式来描述这个双目标优化问题。然后，为了解决实际规模的问题，我们提出了一种新的多目标粒子群优化（MOPSO）算法，其特点是针对具体问题的改进策略。一个分支定界算法的目的是准确地评估最有前途的解决方案。最后，大量的计算实验表明，所提出的MOPSO是能够匹配的解决方案质量的精确求解器上的小实例，并优于两个国家的最先进的多目标优化在文献中的大型实例多达200汽车。
The traditional way of scheduling production processes often focuses on profit-driven goals (such as cycle time or material cost) while tending to overlook the negative impacts of manufacturing activities on the environment in the form of carbon emissions and other undesirable by-products. To bridge the gap, this paper investigates an environment-aware production scheduling problem that arises from a typical paint shop in the automobile manufacturing industry. In the studied problem, an objective function is defined to minimize the emission of chemical pollutants caused by the cleaning of painting devices which must be performed each time before a color change occurs. Meanwhile, minimization of due date violations in the downstream assembly shop is also considered because the two shops are interrelated and connected by a limited-capacity buffer. First, we have developed a mixed-integer programming formulation to describe this bi-objective optimization problem. Then, to solve problems of practical size, we have proposed a novel multi-objective particle swarm optimization (MOPSO) algorithm characterized by problem-specific improvement strategies. A branch-and-bound algorithm is designed for accurately assessing the most promising solutions. Finally, extensive computational experiments have shown that the proposed MOPSO is able to match the solution quality of an exact solver on small instances and outperform two state-of-the-art multi-objective optimizers in literature on large instances with up to 200 cars.
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影响因子：
1.1
作者：
Epping, T;Hochstättler, W;Oertel, P
通讯作者：
Oertel, P
影响因子：
9.2
作者：
Spieckermann, S;Gutenschwager, K;Voss, S
通讯作者：
Voss, S
影响因子：
9.2
作者：
Sun, Hui;Fan, Shujin;Zhou, Jiangong
通讯作者：
Zhou, Jiangong
影响因子：
14.3
作者：
Ratnaweera, A;Halgamuge, SK;Watson, HC
通讯作者：
Watson, HC
影响因子：
14.3
作者：
Deb, K;Pratap, A;Meyarivan, T
通讯作者：
Meyarivan, T