Development of Partition-Designed Outdoor Workwear with Optimal Fabric Selection

The necessity for anti-heat stress workwear to ensure the safety and performance of outdoor workers in hot, humid environments is clear. Yet, there is a gap in fabric selection techniques that consider multi-criteria decisions and account for the varying functional needs across different body regions. Here, we proposed a hybrid multi-criteria decision-making approach, merging the efficacy coefficient method, analytic hierarchy process, entropy weight, and technique of order preference by similarity to the ideal solution for developing ergonomic modular outdoor workwear. This method is tailored for anti-heat stress workwear, balancing competing functional demands. Initial research involved surveying workwear requirements in terms of human, clothing, and environmental factors, leading to the selection and testing of 15 fabrics for 6 partitioned designs. We established data standardization by the efficacy coefficient method and six evaluation index systems for human body requirements, with analytic hierarchy process and entropy methods determining subjective and objective criteria weights. The technique of order preference by similarity was used to produce the ideal solution then ranking of pre-screened fabrics, culminating in the optimal selection. For validation, three uniform prototypes were developed and tested through sweating manikin experiments and human wear trials to affirm the effectiveness of our approach. The partitioned design method proved more effective for anti-heat stress workwear for outdoor workers compared to existing solutions.