Service industry is constantly growing and services attract increasing importance for example in combination with industrial products. Typical characteristics of services are intangibility, customer integration, non-transportability, non-stockability and the promise of future performance. This results in complexity, uncertainty and involvement of different interest groups to be handled with when services are planned and provided. Mathematical optimization methods are perfectly suited to determine best solutions for complex situations. Several extensions allow finding ideal compromises for different interests and appropriate solutions in uncertain situations.
After plenty of successful applications of optimization in industry, health care management increasingly uses optimization models to improve performance and quality or to reduce costs. In this presentation the focus is on providing services with scarce resources within a short period of time. This is of prime importance for fire fighters and emergency medical services and highly depends on the best location of respective departments and equipment. Decisions on operating room schedules also influence customer satisfaction by treating as many patients as possible during their preferred time. Often, time and capacity needed for a service are not precisely known in advance. Then stochastic or robust optimization models are used to find admissible and best solutions. Several examples from health care management and logistics demonstrate how different optimization methods cope with uncertainty and multi-criteria and determine optimal solutions for services in different application areas.