# A Review of the High-Mix, Low-Volume Manufacturing Industry
## 4 Optimisation of the HMLV Layout and Flow
### Layout and Flow Optimisation  
#### 4.2.1 Cellular Manufacturing  
The layout design focuses on optimising product distribution and machine arrangement with mathematical modelling and simulation [34]. Group technology (GT) is a  
Figure 10: The trend of the most common methodologies.  
method where components are identified according to size, shape, and the type of processing required. With that, it optimises similarities in product design and manufacturing processes, allowing greater product variety at low volume. Cellular manufacturing (CM) applies the concept of group technology by assigning a similar group of parts into machines/manufacturing process clusters called cells. This shortens the setup times, in-process inventory, tooling, and improves product quality. Group technology approach was used to incorporate new parts and machines into the existing CM setup  
[PERSON] and [PERSON] [35] devised a group technology model to incorporate new parts and machines into the existing cellular manufacturing system. The model uses a modified Boolean matrix concept to consider two cases. First is the inclusion of a new part family with all its operations on existing machines, where no new machines are added. This is where a company introduces a new variant of an existing design. The second case accounts for introducing a new part family with some operations on machines not in the existing layout; as such, new machines need to be introduced. This would apply to the company that introduces a new technology in the design.  
[PERSON], et al. [36] developed mathematical formulae to solve group setups with minimal feeder change (GSMFC) problems. The devised model would have to optimise between two competing objectives: (1) to keep the number of product groups low and (2) to minimise the number of required feeder changeovers. The formulae included weightage, which has a natural interpretation of real-world situations, and different scenarios were analysed with changes in the weightage. From there, heuristic algorithms were devised to solve the GSMFC problem efficiently.  
Simulation is also carried out on the effect of HMLV manufacturing with cellular manufacturing (CM) in the presence of demand fluctuations. [PERSON] [37] has shown that cellular manufacturing provides faster delivery than job shop when the volume flexibility increases. CM also increases routing flexibility; the simulation model shows that the high-volume flexibility is balanced with high routing flexibility. By accounting for volume, routing, and labour flexibilities, the simulation shows CM's benefits in reacting to the volatile demand in HMLV.  
The implementation of CM has also been described by [PERSON] and [PERSON] [38]. It provided a framework and created a DES that defined the optimal time distribution for universal work cells with data analysis and a similarity analysis, followed by a work-cell definition for every product type and balancing of the workstation. This serves as an analysis tool for suggesting new factory layouts and redesigning the replenishment processes as a practical guide for work-cell implementation for enterprises.  
In the context of Industry 4.0, [PERSON], et al. [39] presented a novel model of digital twins for manufacturing cells and a graph node network (GNN) for production routing. With a robotic operating system (ROS 2), individual digital twins are represented as nodes to create a decentralised and distributed architecture. With the GNN, the most time-effective path to the destination is calculated with the BFS algorithm. Meanwhile, [PERSON]'s algorithm is used to determine the shortest path when the paths are weighted.