Parts loading scheduling in a flexible forging machine using an advanced genetic algorithm
A flexible forging machine (FFM) is one of the most important machines in a general flexible manufacturing system. The scheduling problem of parts loading in FFM is to reduce or preferably eliminate the changeover cost, and is an NP (Nondeterministic Polynomial solvable)-hard combinatorial optimization problem. The genetic algorithm (GA) is known to be a modern heuristic search algorithm, and is suitable for solving such a problem. When applying GA to the scheduling problem, we frequently obtain a local optimal solution rather than a best approximate solution. The goal of this paper is to solve the above-mentioned problem of falling into a local optimal solution by introducing a measure of diversity of population using the concept of information entropy. Thus, we can obtain a best approximate solution of the parts loading scheduling problem of FFM by using an advanced GA.
flexible forging machine genetic algorithms diversity of population information entropy