考虑延迟作业和作业分割特性的同一并行机调度问题的绿色模型

Milad Asadpour , Zahra Hodaei , Marzieh Azami , Ehsan Kehtari , Najmeh Vesal
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引用次数: 4

摘要

在大多数组织中,尤其是以订单为导向的组织,按时完成任务至关重要。作业车间环境可以作为一个例子,其中决策者试图以最小化延迟作业(TJ)的方式调度所有作业。事实上,制造商收到客户的订单,并被要求不迟于确定的到期日交付。否则,必须向客户支付罚款,这可能会导致巨大的经济损失。此外,关于全球变暖和气候变化,制造商应该从环保的角度重新设计他们的工艺。基于这些问题,本文建立了一个考虑TJ和作业分割特性的绿色双目标模型来解决并行机调度问题。在该模型中,第一个目标函数是使TJ总数最小化,第二个目标函数是使总能耗最小化。采用增广ε约束方法求解小尺度问题。然而,为了解决大规模问题,我们开发了一种高效的模拟退火(SA)算法,并应用和谐搜索(HS)算法来检验所提出的模拟退火算法的质量。随机生成的问题被用来比较三种部署算法的结果。结果表明,该算法的性能明显优于其他算法。特别是,SA比其他方法更快地解决了问题,且其解更接近增广ε-约束方法的解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A green model for identical parallel machines scheduling problem considering tardy jobs and job splitting property

In most organizations, especially order-oriented ones meeting deadlines are crucial. Job shop environments could be mentioned as an example where decision makers are try to schedule all jobs in such a way that tardy jobs (TJ) are minimized. Indeed, manufacturers are received customers’ orders and required to deliver them no later than the determined due dates. Otherwise, penalties should be paid to customers and it can lead to a huge amount of financial loss. Also, regarding global warming and climate changes manufacturers should redesign their processes from an eco-friendly perspective. Motivated by these issues, in this paper, a green bi-objective model has been formulated to solve the problem of scheduling parallel machines considering TJ and job splitting property (JSP). In the proposed model, the first objective function minimizes the total number of TJ while the second objective function is minimization of total energy consumption. An augmented ε-constraint method has been deployed for solving small-scale problems. However, to solve large-scale problems, an efficient Simulated Annealing (SA) algorithm has been developed while a Harmony Search (HS) algorithm has also been applied to examine the quality of the proposed SA algorithm. Random generated problems have been used to compare the results of three deployed algorithms. Results approved that the proposed SA algorithm outperforms others significantly. In particular, SA solved the problems sooner than others while its solutions were closer to solutions of the augmented ε-constraint method.

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