In a time of competitiveness and scarcity of raw materials, an industrial (indeed, any) system must work in a state not far from its optimum, "small" improvements being sometimes crucial for success or even survival.  Operational Research (OR*) supplies specific techniques to optimize and manage, and promotes habits of analysis arising from the inspection of the system model. The central objective of OR is optimization, i.e., "to do things best under the given circumstances", to the greatest profit or smallest cost.  This general concept has many applications: agricultural planning, biotechnology, distribution of goods and resources, engineering systems design, environmental management, health care management, inventory control, manpower and resource allocation, manufacturing of goods, military operations, production process control, sequencing and scheduling of tasks, telecommunications, traffic control.

Linear Programming Historical note.  Model.  Dantzig’s simplex algorithm; matrix method; duality.  Computational resolution.

Transportation Problem  Model.  Stepping-stone algorithm.  Computational resolution.

Monte Carlo simulation Sampling experiments on models.  Random number generation.

Queueing (waiting line) theory Structure of the models.  Poisson arrivals, exponential servicing.  Infinite and finite populations.  Computational resolution. (This chapter introductorily or if time permits.)

Inventory management Models.  Uniform demand; random demand.  Optimal inventory level.  Computational resolution. (This chapter introductorily or if time permits.)

Travelling Salesman Problem  Route optimization in cycles.  Computational resolution. (This chapter introductorily or if time permits.)