Novel Models and Algorithms for Integrated Production Planning and Scheduling Ph.D. Thesis

This thesis is concerned with production planning and scheduling in make-to-order manufacturing system. We seek effective modelling and efficient solution techniques that can help increase the productivity and the service level of an enterprise, together with reducing production costs, by supporting the management to make smarter decisions on these two levels of the planning hierarchy. In the thesis, we define a novel formulation of the aggregate production planning problem, with the objective of finding production plans that can be refined into feasible detailed schedules. We achieve this by constructing the aggregate representation from detailed production data in an automated way, by an aggregation procedure based on fast, polynomial-time tree partitioning algorithms. We review the possibilities of solving detailed production scheduling problems by using constraint-based techniques. We point out that although constraint programming provides a rich collection of modelling tools for the description of scheduling problems, the solution of such problems often challenges the currently known algorithms. Hence, we aim at boosting the efficiency of these algorithms by the exploitation of structural properties commonly present in industrial problem instances. For this purpose, we define new, so-called consistency preserving transformations. On both levels, we laid emphasis on solving real problems that arise in the industry. We developed a pilot integrated production planner and scheduler software, and used this system to test our algorithms on real-life planning and scheduling problems, originating from an industrial partner.