A Mathematical Programming Model for Tactical Planning with Set-up Continuity in a Two-stage Ceramic Firm

[EN] It is known that capacity issues in tactical production plans in a hierarchical context are relevant since its inaccurate determination may lead to unrealistic or simply non-feasible plans at the operational level. Semi-continuous industrial processes, such as ceramic ones, often imply large setups and their consideration is crucial for accurate capacity estimation. However, in most of production planning models developed in a hierarchical context at this tactical (aggregated) level, setup changes are not explicitly considered. Their consideration includes not only decisions about lot sizing of production, but also allocation, known as Capacitated Lot Sizing and Loading Problem (CLSLP). However, CLSLP does not account for set-up continuity, specially important in contexts with lengthy and costly set-ups and where product families minimum run length are similar to planning periods. In this work, a mixed integer linear programming (MILP) model for a two stage ceramic firm which accounts for lot sizing and loading decisions including minimum lot-sizes and set-up continuity between two consecutive periods is proposed. Set-up continuity inclusion is modelled just considering which product families are produced at the beginning and at the end of each period of time, and not the complete sequence. The model is solved over a simplified two-stage real-case within a Spanish ceramic firm. Obtained results confirm its validity.Pérez Perales, D.; Alemany, ME. (2016). A Mathematical Programming Model for Tactical Planning with Set-up Continuity in a Two-stage Ceramic Firm. International Journal of Production Management and Engineering. 4(2):53-64. doi:10.4995/ijpme.2016.5209SWORD53644

An SKU Decomposition Algorithm for the Tactical Planning in the FMCG Industry

In this paper we address the optimization of the tactical planning for the Fast Moving Consumer Goods (FMCG) industry, in which numerous trade-offs need to be considered over possibly thousands of Stock-Keeping Units (SKUs). An MILP model for the optimization of this tactical planning problem is proposed. This model is demonstrated for a case containing 10 SKUs, but is intractable for realistically sized problems. Therefore, a decomposition algorithm based on decomposing the model into single-SKU submodels is proposed in this paper. To account for the interaction between SKUs, slack variables are introduced into the capacity constraints. These slack variables initially allow the capacity to be violated. In an iterative procedure the cost of violating the capacity is slowly increased, and eventually a feasible solution is obtained. Even for the relatively small 10 SKU case, the required CPU time could be reduced from 4427s to 472s using the algorithm. Moreover, the algorithm was used to optimize cases of up to 1000 SKUs, whereas the full model is intractable for cases of 25 or more SKUs. The solutions obtained with the algorithm are typically within a few percent of the global optimum.</p

An SKU decomposition algorithm for the tactical planning in the FMCG industry

In this paper we propose an MILP model to address the optimization of the tactical planning for the Fast Moving Consumer Goods (FMCG) industry. This model is demonstrated for a case containing 10 Stock-Keeping Units (SKUs), but is intractable for realistically sized problems. Therefore, we propose a decomposition based on single-SKU submodels. To account for the interaction between SKUs, slack variables are introduced into the capacity constraints. In an iterative procedure the cost of violating the capacity is slowly increased, and eventually a feasible solution is obtained. Even for the relatively small 10-SKU case, the required CPU time could be reduced from 1144 s to 175 s using the algorithm. Moreover, the algorithm was used to optimize cases of up to 1000 SKUs, whereas the full model is intractable for cases of 25 or more SKUs. The solutions obtained with the algorithm are typically within a few percent of the global optimum