Scheduling projects in operating systems: an application on assembly line balancing

Operations Research investigates the (best) ways to configure and coordinate systems or operations with optimization procedures. Usually, the optimization of a system is modeled based on the aimed final configuration. However, little is published about how to reach or implement such optimal configurations in the systems. This master thesis proposes a new class of optimization problem: a scheduling of operations between initial and final states of a system, the Implementation Problem. The scheduling of operations is especially important to assembly lines. The automotive industry strongly relies on production lines that can operate 24 hours a day. Thus, the intervention opportunities to modify or optimize the production system are very few. The implementation conditions of balancing on assembly lines are discussed, and the observed characteristics result in the proposal of the Assembly Line Implementation Problem (ALIP). The master thesis proposes a Mixed-Integer Linear Programming (MILP) modeling guide for the formulation of several variations of Implementation Problems. The modeling instructions are used to develop a set of MILP models for the Assembly Line Implementation Problem. For the results, a dataset is proposed and a sensitivity analysis on each of the consistent parameters of the dataset is performed. The proposed formulations are compared, along with the different forms of presenting and solving the problem. Furthermore, a model based decomposition method is used to solve a real-world industrial problem. The modeling correctly represents the division of the implementation of changes in assembly lines. The results show that the division of the effort in multiple stages only need a few more changes (around 7% for the small and medium cases) comparing to a straightforward implementation. The possibility of scheduling the implementation in smaller steps increases the applicability of projects that otherwise would require a large system’s stoppage time.