For example, in manufacturing, you might want to formulate an optimization model that seeks to minimize cycle time by manipulating the number of workers and machines, while restricting capital investment and operational costs as well as maintaining a minimum utilization level of all resources.
A model for this optimization problem would consist of decision variables associated with labor and machines as well as a performance measure based on a cycle time obtained from running the simulation of the manufacturing facility.
Changes may entail adding, deleting, and modifying processes, process times, resources required, schedules, work rates within processes, skill levels, and budgets.
Performance objectives may include throughput, costs, inventories, cycle times, resource and capital utilization, start-up times, cash flow, and waste.
In the context of business process management and improvement, simulation can be thought of as a way to understand and communicate the uncertainty related to making the changes while optimization provides the way to manage that uncertainty.
For further reading on this issue, an appendix that provide a hands-on discussion of simulation and optimization is available at Developer.com and an article on The Economic, Cultural and Governance Issues of SOA is available on CIO Update.
Marcia Gulesian is an IT strategist, hands-on practitioner, and advocate for business-driven architectures. She has served as software developer, project manager, CTO, and CIO. Marcia is author of well more than 100 feature articles on IT, its economics and its management, many of which appear on CIO Update.