Numerous organizations are striving to maximize the profit of their businesses by the effective implementation of competitive advantages including cost reduction, quick delivery, and unique high-quality products. Effective production-scheduling techniques are methods that many firms use to attain these competitive advantages. Implementing scheduling techniques in high-mix low-volume (HMLV) manufacturing industries, especially in Industry 4.0 environments, remains a challenge, as the properties of both parts and processes are dynamically changing. As a reaction to these challenges in HMLV Industry 4.0 manufacturing, a newly advanced and effective real-time production-scheduling decision-support system model was developed. The developed model was implemented with the use of robotic process automation (RPA), and it comprises a hybrid of different advanced scheduling techniques obtained as the result of analytical-hierarchy-process (AHP) analysis. The aim of this research was to develop a method to minimize the total production process time (total make span) by considering the results of risk analysis of HMLV manufacturing in Industry 4.0 environments. The new method is the combination of multi-broker (MB) optimization and a genetics algorithm (GA) that uses general key process indicators (KPIs) that are easy to measure in any kind of production. The new MB–GA method is compatible with industry 4.0 environments, so it is easy to implement. Furthermore, MB–GA deals with potential risk during production, so it can provide more accurate results. On the basis of survey results, 16% of the asked companies could easily use the new scheduling method, and 43.2% of the companies could use it after a little modification of production.

中文翻译：

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工业4.0中高产量，小批量生产计划的实时决策支持系统

许多组织都在努力通过有效实施竞争优势（包括降低成本，快速交付和独特的高质量产品）来最大化其业务利润。有效的生产计划技术是许多公司用来获得这些竞争优势的方法。由于零件和流程的属性都在动态变化，因此在高混合小批量（HMLV）制造业中，尤其是在工业4.0环境中实施调度技术仍然是一个挑战。为了应对HMLV工业4.0制造中的这些挑战，开发了一种新的先进，有效的实时生产计划决策支持系统模型。开发的模型是通过使用机器人过程自动化（RPA）来实现的，它包含各种不同的高级调度技术的混合，这些技术是作为分析层次结构（AHP）分析的结果而获得的。这项研究的目的是通过考虑工业4.0环境中HMLV制造的风险分析结果，开发一种使总生产过程时间（总制造跨度）最小化的方法。新方法是多经纪人（MB）优化和遗传算法（GA）的结合，该遗传算法使用易于在任何类型的生产中进行度量的通用关键过程指标（KPI）。新的MB–GA方法与工业4.0环境兼容，因此易于实现。此外，MB–GA处理生产过程中的潜在风险，因此可以提供更准确的结果。根据调查结果，被调查公司中有16％可以轻松使用新的计划方法，而有43个公司可以轻松使用。