Fuzzy hierarchical production planning (with a case study)

Hierarchical production planning (HPP) is a well-known approach to cope with the complexity of multi-level production planning and scheduling problems in real-world industrial cases. However, negligence of some issues such as inherent uncertainty in critical input data (i.e., market demands, production capacity and unit costs) as well as possible infeasibility of these problems due to imposing the decisions made at a higher level as a hard constraint to the inferior level without allowing any deviation often result in the inefficiency of HPP approach in practice. In this regard, we incorporate the fuzzy set theory into the HPP structure to handle the uncertainty and infeasibility issues. Inspired by a real industrial case, a fuzzy HPP (FHPP) model is proposed which is composed of two decision making levels. At first, an aggregate production plan is determined by solving a fuzzy linear programming model at the product family level and then it is disaggregated through another fuzzy linear programming model at the next level to find a disaggregated production plan in final products level. The FHPP model is implemented for the real industrial case and it is compared with the previously developed crisp model. The corresponding results are discussed and some important managerial implications are provided.