Due to the advances in wireless network environments, consumers/end-user behaviors continue to expand in cyberspace. Similarly, university students (i.e. universities’ consumers) can easily shift from one university to another. In recent years, decision-makers in educational organizations have faced multi-criteria decision making (MCDM) problems in e-service adoption in order to improve quality standards and maintain students’ retention in highly competitive education environments. Generally, many required criteria in MCDM cannot be evaluated accurately since accurate data cannot be obtained from the decision makers’ assessments. Thus, this research aims to propose a decision-making model for identifying the factors that highly impact on e-service adoption in educational organizations. This new model combined the fuzzy Decision MAking Trial and Evaluation Laboratory (fuzzy DEMATEL) and fuzzy Techniques for Order of Preference by Similarity to Ideal Solution (fuzzy TOPSIS) to weight the interactions among the factors which were defined from a comprehensive review of literature and to determine the relative importance of these factors. The findings from our new proposed model: fuzzy DEMATEL-TOPSIS showed that environmental factors are the most important for effective e-service adoption among educational organizations in India. The proposed decision making model could guide educational organizations to improve their decisions related to technology adoption in their organizations. The conclusions and practical insights gleaned from this research could also hopefully be useful to school authorities in assisting with the adoption, acceptance, and usage of e-services.

Variety, quality, and rapid response are becoming a trend in customer requirements in the contemporary competitive markets. Thus, an increasing number of manufacturers are frequently seeking alternatives such as redesigning their fabrication scheme and outsourcing strategy to meet the client’s expectations effectively with minimum operating costs and limited in-house capacity. Inspired by the potential benefits of delay differentiation, outsourcing, and quality assurance policies in the multi-item production planning, this study explores a single-machine two-stage multi-item batch fabrication problem considering the abovementioned features. Stage one is the fabrication of all the required common parts, and stage two is manufacturing the end products. A predetermined portion of common parts is supplied by an external contractor to reduce the uptime of stage one. Both stages have imperfect in-house production processes. The defective items produced are identified, and they are either reworked or removed to ensure the quality of the finished batch. We develop a model to depict the problem explicitly. Modeling, formulation, derivation, and optimization methods assist us in deriving a cost-minimized cycle time solution. Moreover, the proposed model can analyze and expose the diverse features of the problem to help managerial decision-making. An example of this is the individual/ collective influence of postponement, outsourcing, and quality reassurance policies on the optimal cycle time solution, utilization, uptime of each stage, total system cost, and individual cost contributors.

This study explores the optimal fabrication runtime for a buyer-vendor incorporated system featuring repairable items, stochastic breakdown, accelerated rate, and multi-delivery strategy. Operating in today’s competitive global market, transnational production firms make every effort to meet client requirements in terms of the due date and quality goods. Further, they also must handle all inevitable events incurred in the manufacturing process, such as unanticipated equipment breakdowns and defective products, with caution to avoid production schedule delay and cost overrun. To examine such a vendor-buyer incorporated system, we build a model to characterize the aforementioned features in the system. The function of total system cost is derived through formulation and analyses. The optimization method and a recursive algorithm are employed to help in deriving the optimal (i.e., cost minimization) fabrication runtime for our problem. An example numerically illustrates how our model, method, and algorithm work. It also reveals the capability of our model in analyzing the impact of each and/or joint feature(s) (e.g., the breakdown, accelerated rate, rework, multi-delivery strategy) on the system’s utilization, optimal runtime, total expenses, and individual cost contributor to assist in managerial decision making, and hence, enabling the firm to gain competitive advantage.

This study explores the quality control system featuring visual inspection and grade judgment for detecting distortion defects in spectacle lens fabrication. Spectacle lenses must be precisely curved to help accommodate nearsightedness and farsightedness. The curved shape allows the lens to have different curvatures in different areas during grinding. The spectacle lens will be prone to optical distortion when the curvature changes abnormally. Accordingly, this study proposes an automatic distortion flaw inspection system for spectacle lenses to substitute professional inspectors who rely on empirical judgment. We first apply the digital imaging of a concentric circle pattern through a testing lens to create an image of that lens. Second, the centroid–radii model is employed to stand for the shape property of each concentric circle in the image. Third, by finding the deviations of the centroid radii for detecting distortion flaws through a small variation control method, we obtain a different image showing the detected distortion regions. Four, based on the distortion amounts and locations, we establish the fuzzy membership functions and inference rulesets to measure distortion severity. Finally, the GA-ANFIS model is applied to determine the quality levels of distortion severity for the detected distortion flaws. Trial outcomes reveal that the proposed automatic inspection system can help practitioners in spectacle lens fabrication, for it attains a high 94% correct classification rate of quality grades in distortion severity, 81.09% distortion flaw detection rate, and 10.94% fake alert rate, in distortion inspection of spectacle lenses.

This study develops a postponement model for the multi-item replenishment decision featuring commonality, an overtime strategy, and product quality reassurance. A single machine is used to meet the steady demand for multiple products wherein product commonality exists among these end products. The proposed postponement model assumes that all pertinent common parts are fabricated in Stage 1 and the finished products are sequentially fabricated in Stage 2. Random nonconformance rates are associated with both fabrication stages, the repairable nonconforming common parts are separated from scrap, and reworking in each cycle helps ensure product quality for each completed batch. An overtime strategy is used to reduce the lengthy fabrication and rework times for common parts. Mathematical analyses and derivation allow us to obtain the total system costs. The optimization method helps find the optimal replenishment decision. We provide a numerical illustration to show (1) how our model works; (2) the individual impact of the system features (e.g., the overtime factor, commonality in terms of the common part completion rate and its relative value, and the issues pertaining to scrap/rework) on the optimal decision, utilization, and the total system cost; and (3) the collective influence of system features on the highlighted problem. This proposed decision-support model helps production managers achieve the operating goals of lowering total system expenses and cutting the length of the production cycle.

Production managers face the growing trend of rapid-response orders and inevitable production defects and failures; they must carefully measure these factors’ effects to minimize operating expenditures and operational disruption. Inspired by assisting producers decide the optimal runtime policy under these real situations, this work investigates the collective impact of rework, expedited-rate, external source, and machine failures on such a specific fabrication system. A partial outsourcing and expedited manufacturing rate are considered in the studied system to reduce the batch fabricating time. Additionally, defects rework and repair failure machines are implemented to retain the quality and avoid production disruption. Our research scheme consists of (1) developing a model for the mentioned manufacturing characteristics; and (2) analytical and optimization techniques for deciding the best batch runtime decision by minimizing the system’s overall expenses. Lastly, we provide numerical examples to demonstrate the model’s applicability and disclose important, in-depth characteristics that facilitate managerial decision-making.

Operating in today’s highly competitive global markets, transnational enterprises always seek to optimize internal vendor-buyer coordinated systems to ensure timeliness and quality deliveries, given the reality of unreliable machines and limited capacity. To facilitate accurate decision making to help organizations gain competitive advantages in such situations, this study explores an intra-supply-chain problem featuring a partial outsourcing batch fabrication plan, random scrap, Poisson-distributed breakdown rate, and multiple shipments of end-product. First, we build a model to characterize the problem clearly. Then, we carry out formulations, analyses, and derivations of the model to attain the problem’s cost function. We then use differential calculus and propose a specific algorithm to confirm the convexity of the obtained cost function and derive the optimal runtime. Finally, we offer a numerical illustration to demonstrate the result’s applicability for other business circumstances. Additional elements of the problem are then discussed, including the individual and combined influence of variations in scrap, outsourcing, breakdown, and shipping frequency. The features of an optimal operating policy and cost relevant parameters are now revealed to assist management with strategic planning and decision making in real-world intra-supply-chain environments.

After the outbreak of COVID-19, Taiwan has implemented rigorous border control and taken specific measures such as virus detection, contact tracing, and quarantine since 2020. Its epidemic prevention performance has been quite outstanding. Even in May 2021, when the epidemic situation worsens, the people in Taiwan fully cooperate with the government’s control measures so as to successfully alleviate and control the epidemic in less than three months. Among them, the detection policy has played a pivotal role. We analyze and discuss the false positive and false negative problems from rapid antigen and PCR detection in the screening policy as well as the timing of using these two instruments. This paper provides theoretical verification of the appropriateness of screening policy in Taiwan, offering a few feasible suggestions for related policies in other countries or regions at different stages of this and other potential epidemics.

When operating in highly competitive business environments, contemporary manufacturing firms must persistently find ways to fulfill timely orders with quality ensured merchandise, manage the unanticipated fabrication disruptions, and minimize total operating expenses. To address the aforementioned concerns, this study explores the optimal runtime decision for a manufacturing system featuring an expedited fabrication rate, random equipment failures, and scrap. Specifically, the proposed study considers an expedited rate that is linked to higher setup and unit costs. The fabrication process is subject to random failure and scrap rates. The failure instance follows a Poisson distribution, is repaired right away, and the fabrication of interrupted batch resumes when the equipment is restored. The defective goods are identified and scrapped. Mathematical modeling and optimization method are used to find the total system cost and the optimal runtime of the problem. The applicability and sensitivity analyses of research outcome are illustrated through a numerical example. Diverse critical information regarding the individual/joint impacts of variations in stochastic time-to-failure, expedited rate, and random scrap on the optimal runtime decision, total system expenses, different cost components, and machine utilization, can now be revealed to assist in in-depth problem analyses and decision makings.

This research constructs a mathematical scheme to explore replenishment-shipment decisions for a multiproduct producer-client coordinated finite production rate (FPR) model with the postponement, rework, and subcontracting plan. The considered multiple goods have a common component, and a batch FPR fabrication with postponement is planned to meet the annual multiproduct requirements. The first fabricating phase makes only the standard components needed for a batch and subcontracts a proportion of them (with additional cost) to expedite the process. In contrast, the second fabricating phase produces the finished multiple merchandise in sequence. The in-house rework processes with extra expense help retain the desirable quality. Each merchandise’s finished batch is transported to the clients in equal-sized numerous shipments. This study derives the optimal batch cycle length and transporting frequency by minimizing the overall fabricating-shipment expenses (including clients’ holding costs). This work offers a numerical example demonstrating various crucial system features influenced by the factors of subcontracting, postponement, rework, and transportation policies to facilitate managerial decision making in industries.