A nonlinear optimization approach for solving facility layout problems

Facility layout research has many subproblems for which optimization models can be developed. Although modeling these problems is often simple, the resulting large size of these models can make solution by decision makers very difficult. This paper is concerned with the development of new heuristics to be used as aids in finding good solutions to the facility layout problem. In particular, nonlinear programming (NLP) techniques are developed that minimize the material handling cost. Descriptions of the problems to be solved are presented along with the formulations of the NLP models to be optimized and the methods used to solve them. Also presented are computational results and a comparison of these results to those of other layout algorithms. The heuristics developed are shown to produce solutions comparable to, and in many cases better than, those of other algorithms. The approach is particularly effective for problems with unequal-size departments. As well, the models are shown to be computationally practical for solving real-world problems.