ABSTRACT
This chapter introduces general concepts of optimisation. It considers specific optimisation techniques including linear programming, differential calculus, separable programming, dynamic programming and genetic algorithms. The optimisation techniques deal only with problems that have a single objective function. Optimisation involves the selection of values for a number of decision variables. The simplest type of optimisation is based on engineering experience and judgement. Real engineering optimisation problems may have hundreds or even thousands of decision variables, several objectives and hundreds of constraints. Many general concepts of optimisation can be demonstrated using linear programming. There are a number of approaches to optimisation including subjective, combinatorial and analytical optimisation. Subjective optimisation is based on engineering judgement and experience and is used in practice when the problem is too complicated to allow combinatorial or analytical methods to be used. Genetic algorithms can be applied to these large combinatorial optimisation problems.
