A decision support system for sequencing production in the manufacturing industry

In the context of labour shortages and an aging population, it is important to support knowledge transfer from experienced workers. Sometimes, this can be done through optimization models, but it is not always possible to get and/or have explicit rules and constraints that could impact a decision when making complex decisions, such as in job sequencing. In such situations, a gap can occur between what the decision models suggest and what experienced planners will actually do. This is because workers may take into account a range of information and knowledge that has been gained from previous decisions that cannot be included in the decision models. The objective of this paper is to address this point by providing a decision support tool based on learning, without producing an optimization model, that is capable of replicating the production sequencing decisions of an experienced planner in a dynamic and complex production context.