Learning-based Hyper-Heuristic Algorithm for Space-Free Multi-Row Facility Layout Problem

To effectively reduce material handling costs, we investigate a generalization of the corridor allocation problem: the space-free multi-row facility layout problem. A new mixed-integer linear programming model is proposed for the space-free multi-row facility layout problem. The model is designed to address the practical needs of complex workshop layouts in modern manufacturing systems and incorporates relevant operational constraints. The CPLEX exact solver is applied to solve the small-scale instances to verify the accuracy of the model. To address the NP-hard characteristics, we propose a learning-based hyper-heuristic based on the reward mechanism. The algorithmic framework is divided into two layers. The low-level heuristics are composed of seven simple and efficient operators. The learning process is based on the quality of the solution and acts on the scores of the lower-level heuristic operators. The high-level strategy based on a reward mechanism automatically selects the most suitable low-level heuristic operators based on the scores. Moreover, the Monte Carlo acceptance criterion is incorporated to adaptively change the scores of the low-level heuristic operators. Three different strategies of hyper-heuristics are applied to solve the benchmark instances of different scales. Compared with the two contrastive algorithms, the proposed algorithm achieved a total of 53 superior objective values across 54 instances. And the experimental results are compared with those of different algorithms and the method in the related literature. For the 36 cases that produced identical objective values, the computational time is reduced by approximately 4 to 900 times in comparison with the reported methods. The results show that the developed method is stable and efficient for solving the space-free multi-row facility layout problem. Finally, it was applied to a workshop layout case, where the results demonstrated improvements in solution quality.