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With the increase in air travel, comfort is becoming an important issue with which airlines differentiate themselves in a competitive market. Passengers are paying more attention to their comfort experience during flights. However, passenger comfort is affected by various factors that result from the complex cabin system. Besides, the factors have interrelations between one another. How to identify the key factors and comb through their interrelations to improve passenger comfort efficiently is a critical problem in cabin design. To solve this problem, a hybrid model is proposed to identify the key factors and discuss the interrelation between them based on the questionnaire data. First, factor analysis is conducted to segment complex influencing factors into groups and to extract the most important factors. Decision Making Trial and Evaluation Laboratory (DEMATEL) method and fuzzy set theory are combined in the next analysis, considering both the interrelation between factors and the fuzziness of subjective comfort perception concurrently. It forms a structural model and then visualizes the causal relationships among factors through a causal diagram. The results of an empirical study show that the proposed model is capable of identifying the critical factors that affect passenger comfort effectively and could offer a priority of factors to improve passenger comfort.

Cloud design has become a potential emerging design service mode. More and more design service providers are clustered in a cloud design platform to become crowdsourcing members. Collaborative tasks published by the platform are usually performed by several crowdsourcing members, and the collaborative mode of crowdsourcing members is an asynchronous collaborative working mode. However, due to the variety of crowdsourcing members in terms of ability, time, and so on, how to combine crowdsourcing members into a rational crowdsourcing team has become a challenge. At the same time, due to the looseness and uncertainty of their collaborative mode, there is no effective guidance mechanism in the cooperation process. Therefore, we propose a method of crowdsourcing the optimal combination and scheduling of members. By evaluating the comprehensive abilities of crowdsourcing members, the method optimizes the selection of suitable crowdsourcing members for each subtask and combines them into a crowdsourcing team. At the same time, besides establishing a collaborative workflow simulation model to plan the timing of subtasks and the workflow of crowdsourcing members, the method also optimizes and schedules crowdsourcing members to process different subtasks in turn. Finally, in the case of the collaborative design task of a medical analgesia pump, this method is used to establish a rational crowdsourcing team for collaborative tasks and effectively plan the collaborative work process of crowdsourcing members. The results demonstrate that the proposed method is feasible and effective.