Dynamic Production Planning using Bayesian Network for Management Operation Skills

考虑到世界各地经济的糟糕状况，几乎每个制造基地都得到了大量兼职、临时或职业生涯中期人员的支持。而一线工人的专家经理必须设计更复杂的生产计划，考虑到工人的技能。因此，我们的研究目的是利用专家管理者的操作历史建立一个贝叶斯网络模型，并验证一些因素，使非专家更容易分配人力资源。首先，收集操作历史记录。接下来，讨论了专家经理和非专家的生产计划程序的一些差异，分为最小化完工时间或工作量的目的。最后，通过构建基于操作记录的贝叶斯网络模型，验证了专家管理者操作的有效性，并用概率推理的方法对此进行了讨论。

Given the poor state of the economies all over the world, almost every manufacturing site has been supported by a lot of part-time, temporary, or mid-career personnel. And expert managers of front-line workers must design more complex production planning that take into consideration the workers' skills. Therefore, the purpose of our study is to develop a model with a Bayesian network using the operation histories of expert managers, and to verify some factors that would make it easier for nonexperts to assign human resources. First, the operation histories are collected. Next, some differences of production planning procedures for expert managers and nonexperts are discussed by dividing into the purposes of either minimizing makespan or workload. Finally, the effectiveness of the expert managers' operations is verified by constructing a Bayesian network model based on the operation records, and is this discussed by way of probabilistic inference.