Task-driven optimization of production networks

Efficient scheduling of production systems is a critical task in modern manufacturing industries. This master thesis focuses on addressing the scheduling problem in production systems that involve a network of plants sharing production capacity. The complexity of this problem is further compounded by the need to consider client preferences, adding a layer of intricacy to the optimization process. Given that the scheduling problem is known to be NP-Hard, traditional methods often fail to provide satisfactory solutions within reasonable time frames. In this work, we propose a novel approach based on a iterative multi-objective Bipartite Matching algorithm to optimize scheduling in production systems with shared capacity and client preferences. The objective is to simultaneously minimize production delays and maximize customer satisfaction, taking into account various production constraints and preferences of the clients. We show how, given a particular network of facilities and a set of products to deliver, we can efficiently compute Pareto optimal scheduling solutions.