Process selection and tool assignment in automated cellular manufacturing using Genetic Algorithms

The purpose of this study is to develop an efficient heuristic for the process selection and part cell assignment problem. The study assumes a production environment where each part has several process plans, each manifested by a required set of tools. These tools can be assigned to different machines based on a tool–machine compatibility matrix. An additional assumption is that all relevant data such as periodic demand, processing time, processing cost, tool magazine capacity, tool changing time, tool life and tool cost are fixed and known. A mixed integer linear program which takes all relevant data into account is developed to minimize the production cost. The suggested solution approach to solve this model makes use of Genetic Algorithms: a class of heuristic search and optimization techniques that imitate the natural selection and evolutionary process. First, the encoding of the solutions into integer strings in presented, as well as the genetic operators used by the algorithm. Next, the efficiency and robustness of the solution procedure is demonstrated through several different examples.