Solving the Multi-Objective Facility Layout Problem Using Evolutionary Multi-Objective Optimization Algorithms

The multi-objective facility layout problem is defined in the literature as an extension of the famous quadratic assignment problem (QAP). Most previous mathematical models tried to combine both the quantitative and the qualitative objectives into a single objective by using weighting factors. This paper introduces a multi-objective mathematical model and solves it using the revised Strength Pareto Evolutionary Algorithm (SPEAII). The purpose of this paper is to find an efficient set of solutions “Pareto optimal set” which could be introduced to the decision maker to select the best alternative, while considering conflicting and noncommensurate objectives. A computer program is developed to define the mathematical model, code candidate solutions into genetic form, and use Evolutionary Multi-Objective Optimization algorithms (EMO) to find the efficient set of solutions. The problem model is built according to its customized data input. The suggested model and solution algorithms are applied to a wide set of different benchmark problems. Results showed the superiority of the suggested models and algorithms in terms of the quality of solution and objective space exploration.