# A Facility Layout Algorithm for Logistics Scenarios Driven by Transport Lines
###### Abstract  
The layout of facilities in a logistics scenario involves not only the working facilities responsible for processing materials but also the transport lines responsible for transporting materials. The traditional facility layout methods do not take into account the transportation facilities nor calculate the material handling cost by Manhattan distance, thus failing to fulfill the actual requirements of industrial logistics scenarios. In this paper, a facility layout algorithm framework MOSA-FD driven by transport lines is proposed. A multi-objective simulated annealing (MOSA) algorithm is designed for both material handling cost (MHC) and transport facility cost (TFC) objectives. Then, a force-directed (FD) algorithm is applied to correct the unreasonable solutions according to the material transport lines in the logistics workshop, and a better solution is quickly obtained. Finally, by comparing the results with those of other metaheuristic multi-objective algorithms, the acceleration of the force-directed algorithm in this layout problem is demonstrated in experimental instances of different scales, and our method, compared to the MOSA algorithm, can reach optimal ratios of 36% and 80%, respectively, on the multi-objective. logistics facilities layout problem; multi-objective optimization; force-directed algorithm; collaborative computing +
Footnote †: journal: Applied  
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algorithms [4; 5]. Although exact algorithms can obtain the optimal solution by exhaustive search in feasible solutions, they can only solve a small-scale, symmetric instance search space, and the modeling and constraints for the facility layout problem are relatively uncomplicated. The mainstream approach to studying the facility layout problem is to collaboratively compute an approximate optimal solution using metaheuristic algorithms within a reasonable time frame, mainly including the genetic algorithm, the particle swarm algorithm, and the simulated annealing algorithm. The genetic algorithm possesses excellent global search capability, allowing it to efficiently explore a vast space of potential facility layouts and find reasonable solutions. By employing the co-evolution of multiple populations and implementing a broader search space sampling scheme, population diversity can be effectively maintained [6; 7; 8]. Refined crossover and variation operators in genetic algorithms can yield facility layout solutions with lower costs [9; 10]. Integrating new solution reconstruction and decomposition strategies can further enhance the layout exploration capability of genetic algorithms [11]. For multi-objective facility layout problems, selecting solutions using genetic algorithms cannot be determined based on a single objective value alone. Each solution may have mutual advantages on different objectives, and the Pareto dominance relationship is employed to evaluate the dominant and non-dominant solution; thus, NSGA II [12; 13; 14; 15] is widely used for facility layout optimization. The particle swarm algorithm optimizes the objective by sharing information among particles, so each one can obtain a better layout solution based on its local and global optimal position. By mapping the discrete search space of the facility layout problem to the continuous search space through coding, high-quality layout solutions can be explored within the feasible region [16; 17; 18; 19]. Under the multi-objective function, the algorithm records the Pareto optimal solution set through data archiving, and the algorithm optimizes the layout of each facility by archiving and updating the extreme values of each particle using a roulette wheel selection strategy [20; 21; 22; 23].