Three essays on optimization models in manufacturing and service industries

Mathematical optimization problems find their applications in numerous industrial contexts. Decisions about building up and using capacities are crucial for a firm’s economic success, since building up capacities is expensive. Therefore, the decisions about where, when, and how much capacity to build up must be taken strategically. The use of decision-support tools is advantageous. This thesis presents optimization problems of building up and using capacities in two research fields. Ramp-up situations represent one research field because ramp-up planning problems in manufacturing and process industries are characterized by limited production capacities. When a new product is introduced into an existing production system or a new production system is built up, production starts at a low level and increases stepwise until full production capacity is reached. This effect is, amongst others, caused by learning behavior. Efficient utilization of the limited capacity is important to satisfy as much demand as possible during the introduction of a new product. Another research field, independent of ramp-up planning, is task assignment in the service industry where capacities are mainly related to personnel capacities. Human resources must be planned and utilized efficiently in order to minimize costs or to maximize customer satisfaction. These examples are covered in the three essays contained in this thesis. The first essay presents an overview of optimization problems of ramp-up situations in manufacturing and process industries. It classifies the ramp-up problems and the existing literature according to their characteristics with respect to the three dimensions of variability, i.e., diversity, dynamics, and uncertainty. The second essay addresses a long-term optimization problem for ramp-ups in automotive production networks. We refer to the problem as strategic ramp-up planning. In this problem, products are allocated to plants, and the optimal timing of ramp-ups and ramp-downs is determined. Further, the ramp-up duration, the shape of capacity, the production quantities, and the transportation volumes are optimized. The third essay provides a decision-support tool for an application of capacity optimization in the service industry. It addresses a task assignment problem for check-in counters at airports. Agents are assigned to check-in and boarding tasks under consideration of flight schedules, agents’ skills, and travel times between the counters