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

2020

Complex Intell. Syst.

A layered partial-consensus fuzzy collaborative forecasting approach is proposed in this study to forecast the unit cost of a dynamic random access memory (DRAM) product. In the layered partial-consensus fuzzy collaborative forecasting approach, the partial-consensus fuzzy intersection (PCFI) operator is applied instead of the prevalent fuzzy intersection (FI) operator to aggregate the fuzzy forecasts by experts. In this way, some meaningful information, such as the suitable number of experts, can be obtained through observing changes in the PCFI result when the number of experts varies. After applying the layered partial-consensus fuzzy collaborative forecasting approach to a real case, the experimental results revealed that the layered partial-consensus fuzzy collaborative forecasting approach outperformed three existing methods. The most significant advantage was up to 13%.

Section: Discussionmentioning

confidence: 99%

Forecasting the unit cost of a DRAM product using a layered partial-consensus fuzzy collaborative forecasting approach

2020

Complex Intell. Syst.

“…Traditionally, the organ required by a patient has to be prepared and transported to the hospital where the patient is. Now, the 3D file of the required organ can be transmitted via the Internet to the 3D printer of the hospital to print without transportation [52] .…”

Section: Smart Technologies For the Digital Transformation Of Health mentioning

confidence: 99%

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

Health Policy and Technology

2020

a b s t r a c tSmart 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.

“…3D printing has great potential for establishing a CMfg service. However, the planning, optimization, and control of a 3D printing-based CMfg system have not been sufficiently discussed [38].…”

Section: Capacity and Production Planning Under A Cmfg Environmentmentioning

confidence: 99%

“…Chen and Lin formulated a LP model and a nonlinear programming (NLP) model for splitting an order among 3D printing facilities and determining the sequence of picking up the printed pieces, respectively. Similarly, in the CMfg system established by Wang et al [38], an order was split among several 3D printing facilities by solving a mixed-integer linear programming (MILP) problem. Subsequently, a mixed-integer quadratic programming (MIQP) model was optimized to form a delivery plan to pick up the printed pieces.…”

Section: Capacity and Production Planning Under A Cmfg Environmentmentioning

confidence: 99%

A fuzzy mid-term capacity and production planning model for a manufacturing system with cloud-based capacity

Wang

2020

Complex Intell. Syst.

Most of the past cloud manufacturing (CMfg) studies investigated the short-term production planning or job scheduling of a CMfg system, while the mid-term or long-term capacity and production planning of a CMfg system has rarely been addressed. In addition, most existing methods are suitable for CMfg systems comprising three-dimensional (3D) printers, computer numerical control (CNC) machines or robots, but ignore the coordination and transportation required for moving jobs across factories. To fill these gaps, a fuzzy mid-term capacity and production planning model for a manufacturer with cloud-based capacity is proposed in this study. The proposed methodology guides a manufacturer in choosing between noncloud-based capacity and cloud-based capacity. It can be applied to factories utilizing machines with different degrees of automation including highly automatic equipment (such as 3D printers, CNC machines, and robots) and lowly automatic (legacy) machines, while existing methods assume that orders can be easily transferred between machines that are often highly automatic. In the proposed methodology, first, various types of capacity are unequally prioritized. Then, a fuzzy mixed-integer nonlinear programming model is formulated and optimized to make the mid-term or long-term capacity and production plan of a factory. The fuzzy capacity and production planning model is designed for factories with parallel machines. The proposed methodology has been applied to a case to illustrate its applicability. According to the experimental results, the proposed methodology successfully reduced total costs by up to 8%. The advantage of the proposed methodology over existing practices in fulfilling customers' demand was also obvious.