Fall detection is a critical task in an aging society. To fulfill this task, smart technology applications have great potential. However, it is not easy to choose a suitable smart technology application for fall detection. To address this issue, a fuzzy collaborative intelligence approach is proposed in this study. In the fuzzy collaborative intelligence approach, alpha-cut operations are applied to derive the fuzzy weights of criteria for each decision maker. Then, fuzzy intersection is applied to aggregate the fuzzy weights derived by all decision makers. Subsequently, the fuzzy technique for order preference by similarity to the ideal solution is applied to assess the suitability of a smart technology application for fall detection. The fuzzy collaborative intelligence approach is a posterior-aggregation method that guarantees a consensus exists among decision makers. After applying the fuzzy collaborative intelligence approach to assess the suitabilities of four existing smart technology applications for fall detection, the most and least suitable smart technology applications were smart carpet and smart cane, respectively. In addition, the ranking result using the proposed methodology was somewhat different from those using three existing methods.

Three-dimensional (3D) printing has great potential for establishing a ubiquitous service in the medical industry. However, the planning, optimization, and control of a ubiquitous 3D printing network have not been sufficiently discussed. Therefore, this study established a collaborative and ubiquitous system for making dental parts using 3D printing. The collaborative and ubiquitous system split an order for the 3D printing facilities to fulfill the order collaboratively and forms a delivery plan to pick up the 3D objects. To optimize the performance of the two tasks, a mixed-integer linear programming (MILP) model and a mixed-integer quadratic programming (MIQP) model are proposed, respectively. In addition, slack information is derived and provided to each 3D printing facility so that it can determine the feasibility of resuming the same 3D printing process locally from the beginning without violating the optimality of the original printing and delivery plan. Further, more slack is gained by considering the chain effect between two successive 3D printing facilities. The effectiveness of the collaborative and ubiquitous system was validated using a regional experiment in Taichung City, Taiwan. Compared with two existing methods, the collaborative and ubiquitous 3D printing network reduced the manufacturing lead time by 45% on average. Furthermore, with the slack information, a 3D printing facility could make an independent decision about the feasibility of resuming the same 3D printing process locally from the beginning.

With the widespread vaccination against COVID-19, people began to resume regional tourism. Outdoor attractions, such as leisure agricultural parks, are particularly attractive because they are well ventilated and can prevent the spread of COVID-19. However, during the COVID-19 pandemic, the considerations around choosing a leisure agricultural park are different from usual, and will be affected by uncertainty. Therefore, this research proposes a fuzzy collaborative intelligence (FCI) approach to help select leisure agricultural parks suitable for traveler groups during the COVID-19 pandemic. The proposed FCI approach combines asymmetrically calibrated fuzzy geometric mean (acFGM), fuzzy weighted intersection (FWI), and fuzzy Vise Kriterijumska Optimizacija I Kompromisno Resenje (fuzzy VIKOR), which is a novel attempt in this field. The effectiveness of the proposed FCI approach has been verified by a case study in Taichung City, Taiwan. The results of the case study showed that during the COVID-19 pandemic, travelers (especially traveler groups) were very willing to go to leisure agricultural parks. In addition, the most important criterion for choosing a suitable leisure agricultural park was the ease of maintaining social distance, while the least important criterion was the distance from a leisure agricultural park. Further, the successful recommendation rate using the proposed methodology was as high as 90%.

As a viable means to enhance the sustainability and competitiveness of aircraft manufacturing and maintenance, three-dimensional (3D) printing has been extensively used in the aircraft industry. However, due to the growing number of suitable 3D printers and the often-high prices of these 3D printers, aircraft manufacturers still face many obstacles in screening possible 3D printers. In addition, dependencies between criteria make it difficult for decision makers to properly assess their absolute priorities. Existing methods fail to address these issues. To solve this problem, this study proposes a nonlinear fuzzy geometric mean (FGM) and dependency-considered fuzzy vise kriterijumska optimizacija i kompromisno resenje (fuzzy VIKOR) approach. The first novel treatment is to design the nFGM method to ensure that the absolute priorities assigned to criteria are correct. Subsequently, in the dependency-considered fuzzy VIKOR, the dependencies between criteria are considered, and a realistic reference point is defined by measuring the distance from each 3D printer to it for proper evaluation. The nonlinear FGM and dependency-considered fuzzy VIKOR approach has been applied to assess and compare five 3D printers for manufacturing aircraft components.

In this study, a customizable generative artificial intelligence (GenAI) system is constructed to facilitate users to formulate and solve job scheduling problems in a flexible manufacturing system (FMS) consisting of multiple factories. Such problems are usually powered by bio-inspired algorithms, which are difficult to comprehend, trust, and/or apply. In the GenAI system, users first express their scheduling requirements in natural language through the system interface. Since the functional scope of job scheduling is narrow and factory workers have similar knowledge backgrounds and may be asked to enter more professional terms, a pre-trained natural language parser is used instead of a large language model (LLM) to extract the relative position and combination of keywords to establish an extended three-field notation (ETFN) for the job scheduling problem in FMSs. Based on the ETFN, a genetic algorithm (GA) is generated to solve the customized job scheduling problem. The optimal solution can be relayed to the project management system to facilitate subsequent operations. The customizable GenAI system has been applied to an FMS consisting of three factories and eighteen machines. According to the experimental results, the natural language parser successfully identified user’s scheduling requirements with an accuracy of up to 92% for unlearned data. The customizable GenAI system improved ease of application, required minimal execution time, and achieved the highest fitness compared to several current practices.