Although the concept of Industrial 4.0 has been well accepted, only few studies have dealt with real-time production scheduling of smart factories. Due to the advantages of simplicity, efficiency and quick response, heuristic rules have become the most promising technology to solve such problems. However, they suffer some drawbacks, such as high development and maintenance costs, low solution quality, and excessive emphasis on local information. To design heuristics from the perspective of system optimization and ensure the performance of heuristics in real-time production scheduling environments, this study develops a network-based dynamic dispatching rule generation mechanism. The complex network theory is introduced to extract a series of low-level heuristics from the perspective of system optimization, while the automatic heuristic generation problem is formulated as a multiple attribute decision making problem. Given that the dispersity of local features indicates their value for decision-making, the entropy weighting method is employed to automatically produce an adequate combination of the provided easy-to-implement low-level heuristics. Finally, the open shop scheduling problem with dynamic job arrivals is taken as an example to evaluate the effectiveness of the proposed algorithm. Numerical results demonstrate the excellent performance of the proposed algorithm in terms of algorithm effectiveness and computational time.

尽管工业 4.0 的概念已被广泛接受，但很少有研究涉及智能工厂的实时生产调度。由于具有简单、高效和快速响应的优点，启发式规则已成为解决此类问题的最有前途的技术。但是，它们也存在一些缺点，例如开发和维护成本高、解决方案质量低以及过分强调本地信息。为了从系统优化的角度设计启发式算法并保证启发式算法在实时生产调度环境中的性能，本研究开发了一种基于网络的动态调度规则生成机制。引入复杂网络理论，从系统优化的角度提取出一系列低级启发式，而自动启发式生成问题被表述为一个多属性决策问题。鉴于局部特征的分散性表明它们对决策的价值，熵加权方法被用来自动生成提供的易于实现的低级启发式的充分组合。最后，以具有动态作业到达的开放式车间调度问题为例，评估所提出算法的有效性。数值结果证明了该算法在算法有效性和计算时间方面的优异性能。熵加权方法用于自动生成提供的易于实现的低级启发式的适当组合。最后，以具有动态作业到达的开放式车间调度问题为例，评估所提出算法的有效性。数值结果证明了该算法在算法有效性和计算时间方面的优异性能。熵加权方法用于自动生成提供的易于实现的低级启发式的适当组合。最后，以具有动态作业到达的开放式车间调度问题为例，评估所提出算法的有效性。数值结果证明了该算法在算法有效性和计算时间方面的优异性能。