This paper describes a hierarchical production planning approach with decision support features for energy intensive industries with particular reference to a tile manufacturing factory. In the tiling industry, the facilities which contribute most to the consumption of energy (and, hence, to the production costs) are usually the kilns where the curing operation is carried out. Frequently, the kilns are also the bottleneck in terms of capacity utilization. Thus, in order to save on energy costs, a planning approach which aims at minimizing the number of active kilns throughout the year is needed besides optimizing the process design in the curing department. To achieve the latter goal, it is necessary to take into account demand fluctuations as well as detailed capacity restrictions while deciding on the lot sizes of the products and the kilns on which the products are loaded. Rather than adopting a monolithic mathematical model for developing a desirable production plan, a hierarchical approach which decomposes the problem into two sub-problems is preferred. In the first level, products and capacity are aggregated over the planning horizon to achieve an overall consideration of demand fluctuations over time. Then, the solution provided by the aggregate solution for the current planning period is disaggregated into a detailed lot sizing and loading solution. The disaggregated problem is difficult to solve and hence, a heuristic is proposed here. This planning approach is sustained by a Decision Support System which enables the elimination of possible inconsistencies in the production plan by providing an effective interaction with the decision maker.

Additional Metadata
Keywords Hierarchical production planningLoading and lot sizingEnergy intensive production systems
Persistent URL dx.doi.org/10.1016/S0925-5273(98)00076-0, hdl.handle.net/1765/118011
Journal International Journal of Production Economics
Citation
Ozdamar, L., & Birbil, S.I. (1999). A hierarchical planning system for energy intensive production environments. International Journal of Production Economics, 58(2), 115–129. doi:10.1016/S0925-5273(98)00076-0