Optimising Production Systems Using Genetic Algorithms

Abstract

As the most widespread type of evolution algorithms, genetic algorithms (GA) have become a very popular means of heuristically resolving optimisation-related issues. GA have proven extremely powerful particularly in cases when solutions from a wide scope of examination are required. Hitherto, GA have resolved non-deterministic problems. Solutions have not been sought in pre-determined ways and an array thereof has been handled simultaneously which has proven particularly appropriate for problems faced in complex production and business processes. Instead of certainty, order and logical deductions, we are faced with randomisation, evolution and self-organisation.In this paper, GA are applied on the case-basis of production of aluminium products (variation of production parameters or comparison of equipment layout). The main target of the presented model is to achieve an optimum production plan by taking into consideration dynamic conditions, such as: customer orders, inventories, capacity utilisation rate, quality or raw materials and products and original or cellular layout of equipment.