The multi-row facility layout problem is a prevalent and significant planning challenge in manufacturing workshops. This problem requires distributing facilities with pairwise transport weights among several rows to attain a layout with minimal logistics costs. However, the significance of aisles in multi-row facility layout has frequently been overlooked. An efficient aisle structure can result in a smooth transportation path and reduced material-handling costs. This paper contributes to the existing literature by introducing a new multi-row facility layout problem that considers long-straight aisles. First, mathematical formulas for the actual transportation distance between facilities through aisles are defined, and a mixed-integer programming model is constructed. Second, a hybrid algorithm based on an intelligent algorithm and a mathematical model is proposed. This method utilizes an improved teaching-learning-based optimization algorithm as a framework for optimizing the discrete facility sequence, and two decoding methods based on linear programming are designed to obtain the facility locations and transportation paths. Experimental results demonstrate that the two decoding strategies have their own advantages in terms of solution quality, efficiency, and area utilization. Moreover, improvement strategies for teaching-learning-based optimization algorithms are observed to be effective. Finally, we present two actual workshop examples of multi-row layout designs. The comparison of different algorithms reveals that the proposed algorithm has significant advantages in terms of solution quality and stability.

The multi-workshop facility layout problem (MWFLP) focuses on the optimal distribution and placement of departments across multiple workshops to maximize material handling efficiency and space utilization in manufacturing systems. This study introduces constraint programming (CP) techniques to the facility layout problem and proposes a multi-objective optimization framework to address the MWFLP. The model employs interval variables to accurately represent facility dimensions and integrates cumulative functions to rigorously enforce non-overlapping and spatial constraints. To improve computational efficiency, symmetry-breaking constraints are introduced to minimize redundant solution spaces and accelerate the search for better layout. The effectiveness of the CP model is demonstrated through comprehensive experimental evaluations, where it consistently delivers superior solution quality and computational efficiency compared to the mixed-integer programming model. In multi-objective optimization scenarios, the CP framework outperforms the multi-objective particle swarm optimization method, offering enhanced solution precision and stability across diverse problem scales.

As an essential influencing factor in the facility layout, the reasonable setting of material handling positions plays a vital role in improving operational efficiency and reducing material handling costs. In this paper, a corridor allocation problem (CAP) that allows irregular logistics material handling positions (IMHPCAP) is proposed. Compared with previous CAP studies, IMHPCAP allows facilities to set irregular material handling positions according to their characteristics to increase material handling flexibility. However, the IMHPCAP is more challenging than the traditional CAP finding the best layout solution. In order to solve this problem, a mixed-integer programming model of IMHPCAP is established. A clonal selection algorithm with social engineering optimiser based on a two-stage solution was proposed to obtain the best layout for large-scale problems. In the first stage of this algorithm, the rows in which facilities are placed, and the arrangement sequence of the facilities are established. In the second stage, the precise coordinates of the MHPs are obtained using a mathematical programming method. Finally, some benchmark instances of the CAP and IMHPCAP were solved to verify the performance of the algorithm. Furthermore, an actual case of a baby plastic product processing plant was considered to validate the IMHPCAP. The results showed that the hybrid clone algorithm performed well in solving the CAP and IMHPCAP, thereby exhibiting practical significance.

针对现有过道布置问题研究忽略布局面积对成本的影响以及未考虑矩形设施布置方向的不足,以最小化总物料搬运成本和布局面积为目标,提出考虑设施方向的双目标过道布置问题,并建立混合整数非线性规划模型.由于该问题具有NP-hard属性,提出一种基于Pareto占优的多目标改进分散搜索算法,该算法采用双层编码方式构造可行解,并据此设计双层交叉和变异算子;为有效处理多目标结果,引入Pareto占优思想和拥挤距离机制,将自适应模拟退火双向改进搜索结构嵌入分散搜索算法,通过设置双阈值实现算法对参考集的自适应改进并减少不必要的迭代过程.通过对比所提算法与LINGO数学规划软件对40个算例的运算结果,验证了所提算法的有效性.最后采用所提算法求解双目标过道布置问题,并将所得结果与相关文献对比,证明了所提算法的优越性.

Detailed research on the impact of longitudinal material transportation mode and facility direction on the layout based on the double-floor corridor allocation problem (DFCAP) is lacking. Hence, we proposed a mixed-integer nonlinear programming (MINLP) model of a multi-objective DFCAP (MODFCAP) for minimising the material handling cost (MHC), minimising total layout area, and optimising the equilibrium index of double elevators. Moreover, we proposed a multi-objective clonal selection algorithm with variable neighbourhood search (VNS) operations (ICSAVNS) for solving MODFCAP efficiently. ICSAVNS performs a deep search of the population using the Metropolis-based VNS operation and also performs a breadth search through the two-segment mutation simultaneously. The accuracy of the model and algorithm is validated experimentally using a 9-scale calculation instance. We designed the Taguchi experiment to explore reasonable algorithm parameters and analysed the advantages and disadvantages of the layout schemes under different target preferences based on the results of a set of 24-scale production examples. Finally, the simulation instances of MODFCAP and bi-objective CAP are tested and compared with a series of algorithms. The results show that ICSAVNS can achieve the solution performance of the current advanced multi-objective algorithm.

Aiming at the lack of relevant research on relationship constraints between facilities in the corridor allocation problem (CAP). In this paper, fixed position constraints and ordering constraints are considered in CAP, and the logistics cost is minimized. Considering that the existing search technology is complicated and time-consuming in dealing with such constrained CAP (cCAP), and immune clone selection algorithm with variable neighborhood operation (ICSAVNS) is provided for solving this problem. Two approaches to initial solution generation are designed to improve the quality of the initial population. A variable neighborhood search operator is embedded to improve the accuracy of the local search. A threshold is set in the mutation operation of the ICSAVNS to achieve population expansion better. A double index of sequences consisting of affinity values and constrained facility index values is used to select and reselect, achieving population compression in the clonal selection part. Finally, by exactly solving the model, the rationality of the model is verified. The hybrid clone selection algorithm is used to solve the cCAP and cbCAP benchmark instances of different sizes, and compared with the state-of-the-art optimization algorithms. The results show that the proposed algorithm exhibits better performance.

The optimisation of the corridor allocation problem (CAP) belongs to the optimisation of the efficiency of the automated production line. The goal is to reduce the material handling cost (MHC) in the production process through a reasonable layout of the facilities, so as to save expenses for the enterprise. In recent years, with the acceleration of market changes, product design and production process adjustments have become more frequent, and more attention has been paid to the research on the layout of facilities under the condition of changes in the flow of materials between production facilities over time. On the basis of the CAP model, this paper considers the optimisation problem of row layout when the flow of materials between facilities fluctuates in a certain range. The new model can be utilised to obtain the overall optimisation solution under the condition of the floating material flow matrix, so as to achieve the goal of optimising the total MHC in the entire production process. As the new model introduces more variables and intermediate parameters, a two-stage solution method is previously required, which greatly increases the time to solve the problem. This paper proposes a targeted meta-heuristic algorithm optimisation method combining the advantages of tabu search algorithm and harmony search algorithm, which simplifies the solution phase of calling the precise solver in the two-stage algorithm of row facility layout problem, improves the problem solving efficiency, and makes the solution of large-scale problems become possible. The proposed model is verified through Lingo software, and then the model and the hybrid algorithm in the MATLAB environment are verified with each other. Finally, the proposed simplified algorithm is utilised to solve the large-scale problems that could not be solved by the two-stage algorithm before.

针对基于中央回路的物料搬运系统中设施的布置,提出了双向多路径交互环形过道布置问题,其通道形状为首尾相通的封闭回路,设施之间的物料通过环形路径双向流动.针对所提问题,构建了混合整数规划模型,随后通过优化求解器进行精确求解,验证了模型的正确性.为了更快速高效地求解该问题,设计了一种混合鲸鱼算法.该算法将差分进化算法嵌入到鲸鱼算法气泡网觅食阶段,以提高算法局部搜索能力,并引入禁忌搜索机制,提高全局搜索性能.通过对标准算例进行试验,并与其他算法进行对比,验证了所提算法对解决双向环形过道布置问题的优越性.

为了结合生产中环形布局的特征分析不同交互路径对环形过道布置问题的影响,提出一种多路径交互环形过道布置问题,并构建其混合整数规划模型.设计了一种将随机行走机制与迭代机制融合的改进蚁狮算法,算法利用蚁狮衍生蚂蚁种群方式增强局部搜索能力,提高算法求解性能.通过精确求解小规模环形过道布置问题算例验证了模型的合理性与正确性.将该算法与遗传算法、禁忌搜索算法对所提问题求解的结果进行对比,表明改进蚁狮算法在求解质量与效率上更具有效性和优越性.