Algorithmic-Mathematical Scheduling Methodology Applied to an Industrial Size Test Case

Abstract

This paper presents a batch process scheduling methodology for multiproduct multistage facilities. A mixedinteger linear programming (MILP) mathematical model based on a time-slot representation is used. The model is complemented with an iterative algorithm that solves a sequence of subproblems, through which the bottleneck stage is identified and fixed at each step. The proposed methodology seeks to attain good quality solutions for industrial size problems in reasonable computational times. It has been applied to a real case study from the pharmaceutical industry, comprising the scheduling of 30 products in a plant with 17 units and 6 processing stages. Even though the proposed method does not guarantee the optimality of the best solution found, in contrast to other heuristic approaches it provides a rigorous lower bound from which its quality can be determined.