Course Descriptions

Objects and classes, constructors, static variables, constants and methods, visibility modifiers, passing objects and object arrays to methods, immutability, variable scopes, class abstraction and encapsulation, super and subclass concepts, inheritance, polymorphism, overriding, overloading, abstract classes, object-oriented design.

Essentials of macroeconomics. Measuring a nation's income and cost of living. Inflation, unemployment, investment and saving. Money and banking. Open economy macroeconomics. International integrations. Introduction to economic growth and development.

Introduction of university specific rules and regulations. Introduction to engineering profession and to its sub disciplines. History of engineering and relation to other professions. Ethics and other related topics.

Economic analysis for engineering decision making; the finance function in an industrial enterprise, time value of money; basic interest formulas; annual cost comparison; present value analysis; rate of return; depreciation and taxes; multiple alternatives. Mathematical models for equipment replacement. Introduction to decision analysis; concepts of cost engineering.

Modeling concepts. Linear programming: problem formulation, simplex and dual simplex methods, duality and sensitivity analysis. Transportation model and its variants, network models. Goal programming. Integer linear programming.

Dynamic programming. Classical optimization theory: optimization in one variable, convexity, unconstrained and constrained optimization in many variables, Karush-Kuhn-Tucker optimality conditions; Nonlinear programming: direct search and gradient methods. Computational complexity. Major heuristic approaches: tabu search, simulated annealing, genetic algorithms.

Basic concepts of discrete-event simulation modeling and analysis. Event-scheduling versus process-interaction approach. Random number and random variate generation; inverse transformation and other selected techniques. Input data analysis and goodness of fit tests. Specific computer simulation languages, analysis of simulation output and model validation.

Structure of production planning in manufacturing systems. Basic concepts in forecasting, product mix and inventory. Production planning at various levels: aggregate production planning, materials requirement planning and scheduling.

Fundamentals of CIM and automation; high volume discrete parts production systems; numerical control manufacturing systems; robotics, group technology. Work in process concepts; line balancing and layout problems. Motion studies; scheduling and storage issues.

Principles of quality control systems; process control concepts; specification and tolerances; process capability studies; control charts; acceptance sampling plans; cost aspects of quality decisions; quality improvement programs; quality information systems.