Learning Priority Indices for Energy-Aware Scheduling of Jobs on Batch Processing Machines

IF 2.3 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Transactions on Semiconductor Manufacturing Pub Date : 2023-10-24 DOI:10.1109/TSM.2023.3326865

Daniel Sascha Schorn;Lars Mönch

引用次数: 0

Abstract

A scheduling problem for parallel batch processing machines (BPMs) with jobs having unequal ready times in semiconductor wafer fabrication facilities (wafer fabs) is studied in this paper. A blended objective function combining the total weighted tardiness (TWT) and the total electricity cost (TEC) under a time-of-use (TOU) tariff is considered. A genetic programming (GP) procedure is designed to automatically discover priority indices for a heuristic scheduling framework. Results of computational experiments are reported that demonstrate that the learned priority indices lead to high-quality schedules in a short amount of computing time.

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学习优先级指数，在批量处理机上进行节能作业调度

本文研究了半导体晶圆制造厂（Wafer fabs）中作业准备时间不等的并行批量处理机（BPM）的调度问题。在使用时间（TOU）关税下，考虑了总加权延迟（TWT）和总电费（TEC）的混合目标函数。设计了一个遗传编程（GP）程序，用于自动发现启发式调度框架的优先级指数。计算实验结果表明，学习到的优先级指数能在较短的计算时间内实现高质量的调度。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Semiconductor Manufacturing 工程技术-工程：电子与电气

CiteScore

5.20

自引率

11.10%

发文量

101

审稿时长

3.3 months

期刊介绍： The IEEE Transactions on Semiconductor Manufacturing addresses the challenging problems of manufacturing complex microelectronic components, especially very large scale integrated circuits (VLSI). Manufacturing these products requires precision micropatterning, precise control of materials properties, ultraclean work environments, and complex interactions of chemical, physical, electrical and mechanical processes.