Sequencing mixed-model assembly lines with genetic algorithms

This research introduces the use of an artificial-intelligence based technique, genetic algorithms (GA), to solve mixed-model assembly-line sequencing problems. This paper shows how practitioners can comfortably implement this approach to solve practical problems. A substantial example is given for which GA produces a solution in just a matter of seconds that improves upon Toyota's Goal Chasing Algorithm. The new method is then investigated on a test bed of 80 problems. Results indicate GA generates an improved sequence over Goal Chasing on 50 of the problems and also shows a performance advantage of 2% across all 80 problems using Toyota's variability of parts consumption criterion. The paper concludes that further investigation to fine tune the GA methodology is warranted. It also points out that the GA approach can readily be used by practitioners to address a variety of managerial goals concurrently, such as inventory and work load equalization. Copyright (C) 1996 Elsevier Science Ltd