Hybrid flow shop scheduling considering machine electricity consumption cost
Hybrid flow shop scheduling considering machine electricity consumption cost
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DOI：
10.1016/j.ijpe.2013.01.028
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发表时间：
2013-12
影响因子：
12
通讯作者：
H. Luo;B. Du;G. Huang;Hua-ping Chen;Xiaolin Li
中科院分区：
文献类型：
--
作者：
H. Luo;B. Du;G. Huang;Hua-ping Chen;Xiaolin Li
混合流水车间（HFS）调度已被广泛研究，其主要目标是提高生产效率。然而，随着绿色制造的出现，对能源消耗的考虑受到了有限的关注。提出了一种新的蚁群优化（MOACO）元启发式算法，该算法不仅考虑了生产效率，而且考虑了分时电价下的电力成本。解决方案被编码为作业的排列。采用列表调度算法，利用人工蚂蚁构造排序，生成完整的调度表。一个右移程序，然后使用调整的开始时间的操作，旨在最大限度地减少EPC的时间表。在理论研究方面，计算实验结果表明，所提出的MOACO的效率和有效性与NSGA-II和SPEA 2。从实际应用的角度，研究了多目标优化中的偏好设置准则。这一结果在真实的生产中具有重要的管理意义。参数分析还表明，峰谷时段的持续时间和机器的处理速度对调度结果有很大的影响，因为较长的非高峰期和使用更快的机器提供了更大的灵活性，将高能量的操作转移到非高峰期。
Hybrid flow shop (HFS) scheduling has been extensively examined and the main objective has been to improve production efficiency. However, limited attention has been paid to the consideration of energy consumption with the advent of green manufacturing. This paper proposes a new ant colony optimization (MOACO) meta-heuristic considering not only production efficiency but also electric power cost (EPC) with the presence of time-of-use (TOU) electricity prices. The solution is encoded as a permutation of jobs. A list schedule algorithm is applied to construct the sequence by artificial ants and generate a complete schedule. A right-shift procedure is then used to adjust the start time of operations aiming to minimize the EPC for the schedule. In terms of theoretical research aspect, the results from computational experiments indicate that the efficiency and effectiveness of the proposed MOACO are comparable to NSGA-II and SPEA2. In terms of practical application aspect, the guideline about how to set preference over multiple objectives has been studied. This result has significant managerial implications in real life production. The parameter analysis also shows that durations of TOU periods and processing speed of machines have great influence on scheduling results as longer off-peak period and use of faster machines provide more flexibility for shifting high-energy operations to off-peak periods.