Exploiting travel sequences to optimise facility layouts with multiple input/output points

The facility layout problem (FLP) involves arranging departments on a shop floor to optimise specific objectives, traditionally focussing on pairwise flows between departments. However, these methods often underestimate total travel distances, especially when flows involve multiple input/output points and visits to more than two departments. To address this, connected movements – actual routes taken by transporters – must be considered. This study uses data captured from an Internet of Things (IoT) network and stored on cloud servers to analyze worker movements and accurately calculate travel distances. A mixed-integer programming model is proposed to minimise total travel distance using connected movements as input. Due to the problem's complexity, a biased random key genetic algorithm is employed to find optimal layouts. A case study at a fertiliser production company demonstrates the effectiveness of the approach, achieving a 15% reduction in travel distance compared to layouts generated by traditional methods. The IoT-enabled method also minimises productivity losses by optimising worker movements. While the study focuses on fertiliser manufacturing, the findings are applicable to other settings, such as warehousing, where complex movement sequences and multiple IO points are common in processes like picking, packing, and shipping.

设施布局问题（FLP）涉及在生产车间内排布各作业部门，以优化特定目标，传统方法通常聚焦于部门间的两两流动。然而这类方法往往会低估总出行距离，尤其当流动涉及多个输入/输出点，且需要往返于两个以上部门时。为此，必须考虑连通移动路径——即运输工具实际行驶的路线。本研究利用从物联网（IoT）网络采集并存储于云服务器的数据，对作业人员的移动路径进行分析，进而精准计算出行距离。本研究提出了以连通移动路径为输入的混合整数规划模型，以最小化总出行距离。鉴于该问题的复杂性，本研究采用带偏置的随机键遗传算法来求解最优布局方案。某化肥生产企业的案例研究验证了该方法的有效性：与传统方法生成的布局方案相比，该方法可将出行距离缩短15%。该基于物联网的方法还可通过优化作业人员的移动路径，最大限度减少生产力损失。尽管本研究聚焦于化肥制造场景，但其研究结论可推广至其他场景，例如仓储领域——在拣选、包装、发货等作业流程中，复杂的移动序列与多个输入/输出点是常见情况。