Research in Industrial Engineering spans a number of fields, including: the design and control of production systems; health care; project management; ergonomics; labor productivity; supply chain management; learning and forgetting; and the use of simulators in employee training. The problems studied are generally based on real life systems.
Research in the industrial engineering may include field studies, controlled experiments, and/or practical optimization. Very often, researchers and scientists construct mathematical models to aid in understanding and analysis.
Field studies are designed to collect data about organizations and individuals performing operations. Data collection is the basis for a better understanding of how operations are performed and how they can be improved.
Controlled experiments are used to test hypotheses regarding operations and the people performing such operations. Design of experiments and statistical analysis are the main tools in this branch of industrial engineering research.
Practical optimization begins with a mathematical model, and proceeds to develop the best possible solution. Models are solved either heuristically or optimally. The models employed vary and may be, for example, discrete or continuous, as well as deterministic or stochastic. The tools used to work with these models are even more varied than the models themselves. Common tools used in this branch of industrial engineering research include queueing, mathematical analysis, linear/integer/convex/non-linear programming, dynamic programming, branch-and-bound algorithms, heuristic algorithms, simulations, meta-heuristics, complexity analysis, and approximation algorithms.