A Continuous Plane Model to Machine Layout Problems Considering Pick-up and Drop-offpoints: an Evolutionary Algorithm

One of the well-known evolutionary algorithms inspired by biological evolution is genetic algorithm (GA) that is employed as a robust and global optimization tool to search for the best or near-optimal solution with the search space. In this paper, this algorithm is used to solve unequalsized machines (or intra-cell) layout problems considering pick-up and drop-off (input/output) points. Such problems may be formulated as a continuous plane model that is applicable to cellular manufacturing systems. The aim is to assign all the machines to the related locations within the floor plan in such a way that an objective function, as the total material handling is optimized while satisfying all the constraints. In this paper, a GA methodology is used to generate promising and feasible layout solutions and to find the best or near-optimal solution searched within the space problem. For this purpose, a new genetic presentation and genetic operators are designed. Finally, computational results reported by the GA program and a construction algorithm are presented in the context of the test problems. In addition to the above objective function, the dead space ratio of final layout solution is calculated for each test problem.