A Fuzzy Collaborative Intelligence Approach to Group Decision-Making: a Case Study of Post-COVID-19 Restaurant Transformation.

In a fuzzy group decision-making task, when decision makers lack consensus, existing methods either ignore this fact or force a decision maker to modify his/her judgment. However, these actions may be unreasonable. In this study, a fuzzy collaborative intelligence approach that seeks the consensus among experts in a novel way is proposed. Fuzzy collaborative intelligence is the application of biologically inspired fuzzy logic to a group task. The proposed methodology is based on the fact that a decision maker must make a choice even if he/she is uncertain. As a result, the decision maker's fuzzy judgment matrix may not be able to represent his/her judgment. To solve such a problem, the fuzzy judgment matrix of each decision maker is decomposed into several fuzzy judgment submatrices. From the fuzzy judgment submatrices of all decision makers, a consensus can be easily identified. The proposed fuzzy collaborative intelligence approach and several existing methods have been applied to the case of the post-COVID-19 transformation of a Japanese restaurant in Taiwan. Because such transformation was beyond the expectation of the Japanese restaurant, the employees lacked consensus if existing methods were applied to identify their consensus. The proposed methodology solved this problem. The optimal transformation plan involved increasing the distance between tables, erecting screens between tables, and improving air circulation. In a fuzzy group decision-making task, an acceptable decision cannot be made without the consensus among decision makers. Ignoring this or forcing decision makers to modify their preferences is unreasonable. Identifying the consensus among experts from another point of view is a viable treatment.

A calibrated piecewise-linear FGM approach for travel destination recommendation during the COVID-19 pandemic.

After months of lockdown due to the COVID-19 pandemic, more people are planning regional trips because overseas travel is still not feasible. However, choosing a suitable travel destination during the COVID-19 pandemic is challenging because the factors critical to the selection process are very different from those usually considered. Furthermore, without sufficient literature or data for reference, existing methods based on psychological analyses or mining past experiences may not be applicable. Consequently, a fuzzy multi-criteria decision-making method - the calibrated piecewise-linear fuzzy geometric mean (FGM) approach - is proposed in this study for travel destination recommendation during the COVID-19 pandemic. The contribution of this research is twofold. First, the critical factors that affect the selection of a suitable travel destination during the COVID-19 pandemic are discussed. Second, the accuracy and efficiency using existing fuzzy analytic hierarchy process (FAHP) methods have been enhanced. The calibrated piecewise-linear FGM approach has been successfully applied to recommend suitable travel destinations to fifteen travelers for regional trips in Taiwan during the COVID-19 pandemic.

Recommending Suitable Smart Technology Applications to Support Mobile Healthcare after the COVID-19 Pandemic Using a Fuzzy Approach.

The COVID-19 pandemic seems to be entering its final stage. However, to restore normal life, the applications of smart technologies are still necessary. Therefore, this research is dedicated to exploring the applications of smart technologies that can support mobile healthcare after the COVID-19 pandemic. To this end, this study compares smart technology applications to support mobile healthcare within the COVID-19 pandemic with those before the pandemic, so as to estimate possible developments in this field. In addition, to quantitatively assess and compare smart technology applications that may support mobile healthcare after the COVID-19 pandemic, the calibrated fuzzy geometric mean (CFGM)-fuzzy technique for order preference by similarity to ideal solution (FTOPSIS) approach is applied. The proposed methodology has been applied to evaluate and compare nine potential smart technology applications for supporting mobile healthcare after the COVID-19 pandemic. According to the experimental results, "vaccine passport and related applications" and "smart watches" were the most suitable smart technology applications for supporting mobile healthcare after the COVID-19 pandemic.

Analyzing the Impact of Vaccine Availability on Alternative Supplier Selection Amid the COVID-19 Pandemic: A cFGM-FTOPSIS-FWI Approach.

The supply chain disruption caused by the coronavirus disease 2019 (COVID-19) pandemic has forced many manufacturers to look for alternative suppliers. How to choose a suitable alternative supplier in the COVID-19 pandemic has become an important task. To fulfill this task, this research proposes a calibrated fuzzy geometric mean (cFGM)-fuzzy technique for order preference by similarity to ideal solution (FTOPSIS)-fuzzy weighted intersection (FWI) approach. In the proposed methodology, first, the cFGM method is proposed to accurately derive the priorities of criteria. Subsequently, each expert applies the FTOPSIS method to compare the overall performances of alternative suppliers in the COVID-19 pandemic. The sensitivity of an expert to any change in the overall performance of the alternative supplier is also considered. Finally, the FWI operator is used to aggregate the comparison results by all experts, for which an expert's authority level is set to a value proportional to the consistency of his/her pairwise comparison results. The cFGM-FTOPSIS-FWI approach has been applied to select suitable alternative suppliers for a Taiwanese foundry in the COVID-19 pandemic.

A diversified AHP-tree approach for multiple-criteria supplier selection.

A decision maker usually holds various viewpoints regarding the priorities of criteria, which complicates the decision making process. To overcome this concern, in this study, a diversified AHP-tree approach was proposed. In the proposed diversified AHP-tree approach, the judgement matrix of a decision maker is decomposed into several subjudgement matrices, which are more consistent than the original judgement matrix and represent diverse viewpoints on the relative priorities of criteria. Thus, a nonlinear programming model was established and optimized, for which a genetic algorithm is designed. To assess the effectiveness of the proposed diversified AHP-tree approach, it was applied to a supplier selection problem. The experimental results showed that the application of the diversified AHP-tree approach enabled the selection of multiple diversified suppliers from a single judgement matrix. Furthermore, all suppliers selected using the diversified AHP-tree approach were somewhat ideal.

Smart and automation technologies for ensuring the long-term operation of a factory amid the COVID-19 pandemic: an evolving fuzzy assessment approach.

The COVID-19 pandemic has severely impacted factories all over the world, which have been closed to avoid the spread of COVID-19. As a result, ensuring the long-term operation of a factory amid the COVID-19 pandemic becomes a critical but challenging task. To fulfill this task, the applications of smart and automation technologies have been regarded as an effective means. However, such applications are time-consuming and budget-intensive with varying effects and are not necessarily acceptable to workers. In order to make full use of limited resources and time, it is necessary to establish a systematic procedure for comparing various applications of smart and automation technologies. For this reason, an evolving fuzzy assessment approach is proposed. A case study has been conducted to demonstrate the effectiveness of the evolving fuzzy assessment approach in ensuring the long-term operation of a factory amid the COVID-19 pandemic.

Assessing the Robustness of a Factory Amid the COVID-19 Pandemic: A Fuzzy Collaborative Intelligence Approach.

The COVID-19 pandemic has affected the operations of factories worldwide. However, the impact of the COVID-19 pandemic on different factories is not the same. In other words, the robustness of factories to the COVID-19 pandemic varies. To explore this topic, this study proposes a fuzzy collaborative intelligence approach to assess the robustness of a factory to the COVID-19 pandemic. In the proposed methodology, first, a number of experts apply a fuzzy collaborative intelligence approach to jointly evaluate the relative priorities of factors that affect the robustness of a factory to the COVID-19 pandemic. Subsequently, based on the evaluated relative priorities, a fuzzy weighted average method is applied to assess the robustness of a factory to the COVID-19 pandemic. The assessment result can be compared with that of another factory using a fuzzy technique for order preference by similarity to ideal solution. The proposed methodology has been applied to assess the robustness of a wafer fabrication factory in Taiwan to the COVID-19 pandemic.

Assessing factors critical to smart technology applications to mobile health care - the fgm-fahp approach.

Smart technologies present numerous opportunities for enhancing mobile health care. However, some concerns regarding the viability of smart technology applications must be addressed. This study investigated these concerns by reviewing the current practices of smart technology applications to mobile health care. As a result, five factors critical to the applicability of a smart technology to mobile health care are identified, and the fuzzy geometric mean-fuzzy analytic hierarchy process (FGM-FAHP) approach is proposed to assess the relative importance levels of the identified factors. The experimental results showed that the three most critical factors identified include: (a) the relaxation of the related medical laws; (b) unobtrusiveness; and (c) the precise need and situation of a user. Accordingly, approximately 44%, 26%, and 15% of the budget should be allocated to the realization of the three critical factors, respectively. In addition, the challenges involved and opportunities for enhancing the effectiveness of existing applications are discussed.

A Collaborative and Ubiquitous System for Fabricating Dental Parts Using 3D Printing Technologies.

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.

Ubiquitous Multicriteria Clinic Recommendation System.

Advancements in information, communication, and sensor technologies have led to new opportunities in medical care and education. Patients in general prefer visiting the nearest clinic, attempt to avoid waiting for treatment, and have unequal preferences for different clinics and doctors. Therefore, to enable patients to compare multiple clinics, this study proposes a ubiquitous multicriteria clinic recommendation system. In this system, patients can send requests through their cell phones to the system server to obtain a clinic recommendation. Once the patient sends this information to the system, the system server first estimates the patient's speed according to the detection results of a global positioning system. It then applies a fuzzy integer nonlinear programming-ordered weighted average approach to assess four criteria and finally recommends a clinic with maximal utility to the patient. The proposed methodology was tested in a field experiment, and the experimental results showed that it is advantageous over two existing methods in elevating the utilities of recommendations. In addition, such an advantage was shown to be statistically significant.

Enhancing scheduling performance for a wafer fabrication factory: the biobjective slack-diversifying nonlinear fluctuation-smoothing rule.

A biobjective slack-diversifying nonlinear fluctuation-smoothing rule (biSDNFS) is proposed in the present work to improve the scheduling performance of a wafer fabrication factory. This rule was derived from a one-factor bi-objective nonlinear fluctuation-smoothing rule (1f-biNFS) by dynamically maximizing the standard deviation of the slack, which has been shown to benefit scheduling performance by several previous studies. The efficacy of the biSDNFS was validated with a simulated case; evidence was found to support its effectiveness. We also suggested several directions in which it can be exploited in the future.

Predicting wafer-lot output time with a hybrid FCM-FBPN approach.

Output-time prediction is a critical task to a wafer fab. To further enhance the accuracy of wafer-lot output-time prediction, the concept of input classification is applied to Chen's fuzzy backpropagation network (FBPN) approach in this paper by preclassifying wafer lots with the fuzzy c-means (FCM) classifier before predicting the output times. In this way, similar wafer lots are clustered in the same category. The data of wafer lots of different categories are then learned with different FBPNs but with the same topology. After learning, these FBPNs form an FBPN ensemble that can be applied in predicting the output time of a new lot. The output of the FBPN ensemble determines the cycle/output time forecast and is obtained by aggregating the outputs of the component FBPNs. Production simulation is applied in this paper to generate test data. According to experimental results, the prediction accuracy of the hybrid FCM-FBPN approach was significantly better than those of many existing approaches.

Complete recovery time after exhaustion in high-intensity work.

This study was aimed to investigate complete recovery time (CRT) after exhaustion in high-intensity work. Twenty-four subjects were divided into two groups based on the cardiorespiratory capability index, which was measured in a maximum capacity test. Each subject then performed two cycling tests (at 60% and 70% maximum working capacity). The subject continued cycling until exhaustion in each test and then sat recovering until he/she no longer felt fatigue or until the oxygen uptake (VO2) and heart rate (HR) returned to their baselines, whichever was longer. The results indicated that HR required the longest time to recover and, consequently, HR data were adopted to set the CRT. The CRT was significantly correlated with the cardiorespiratory capability index and the relative workload indices: RVO2 and RHR. The RVO2 was the average elevation in VO2 during work from the resting level as a percentage of maximum VO2 reserve. The RHR's definition was similar to that of RVO2. Based on the obtained CRT-prediction model, the CRT for a high-cardiorespiratory-capability person was 20.8, 22.1, 23.4, and 24.7 min at 50%, 60%, 70%, and 80% RHR levels, respectively. These suggested CRT values should be increased by 10 min for a low-cardiorespiratory-capability person.