Exploration of the consideration factors of pure e-commerce business for transforming into new retail model

Since the outbreak of COVID-19 in the early year 2020, many businesses and industries have encountered serious impact on their operation and maintenance, but the significant growth rate of e-Commerce industries has caught all people's eyes. Many domestic and foreign leading businesses are forced to speed up their configuration for moving into e-Commerce and m-Commerce. For e-Commerce businesses, they do not have much physical communication and interaction with consumers, it may eliminate the expensive management costs, but they cannot provide customers with good opportunities for experiencing. So, how to pure e-Commerce transform into the New Retail Omnichannel operating model is an important research issue. In view of all, this study intends to apply Analytic Hierarchy Process (AHP) in combination with the Fuzzy Theory to analyze a decision-making issue, namely, what factors are deemed important or concerns to "the pure e-Commerce businesses” in their future transformation into New Retail Omnichannel operating model. In the results of this study, "increase revenue”, "develop customer's potential market”, "increase stock turnover, "reduce reverse logistics costs”, and "increased management costs” are the five consideration factors most important to transformation into new retail model. The results from this study provide the pure e-Commerce businesses with considerably useful reference in the issue of transformation into New Retail operating model in their future. © 2022 International Consortium for Electronic Business. All rights reserved.

Elective surgery scheduling under uncertainty in demand for intensive care unit and inpatient beds during epidemic outbreaks.

Amid the epidemic outbreaks such as COVID-19, a large number of patients occupy inpatient and intensive care unit (ICU) beds, thereby making the availability of beds uncertain and scarce. Thus, elective surgery scheduling not only needs to deal with the uncertainty of the surgery duration and length of stay in the ward, but also the uncertainty in demand for ICU and inpatient beds. We model this surgery scheduling problem with uncertainty and propose an effective algorithm that minimizes the operating room overtime cost, bed shortage cost, and patient waiting cost. Our model is developed using fuzzy sets whereas the proposed algorithm is based on the differential evolution algorithm and heuristic rules. We set up experiments based on data and expert experience respectively. A comparison between the fuzzy model and the crisp (non-fuzzy) model proves the usefulness of the fuzzy model when the data is not sufficient or available. We further compare the proposed model and algorithm with several extant models and algorithms, and demonstrate the computational efficacy, robustness, and adaptability of the proposed framework.

Evaluation of priority strategies for the expansion of installed wind power capacity in Turkey using a fuzzy analytic network process analysis

Given its widespread potential and technological advancements, it is critical to use the greatest amount of wind power in reaching the 100% renewable energy target in power grids. SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis is frequently used to evaluate alternative strategies in multi-criteria decision issues. This paper employs a hybrid Fuzzy Analytic Network Process (FANP) approach based on SWOT to assess the future development of wind power capacities in Turkey in light of the sectoral effects of the Covid-19 outbreak in 2020. To validate the proposed approach, the results are compared to the results of the SWOT-based Analytic Network Process (ANP), Analytic Hierarchy Process (AHP), and fuzzy AHP (FAHP). According to the findings, “Development of domestic and efficient technologies (ST1)" and “Sustaining support mechanisms in investments and technological research (SO2)" are the best alternative strategies in all analysis models. While the priority ranks of other alternative strategies are the same in AHP and FAHP hierarchical techniques, FANP differs from ANP when the linguistic assessment process is taken into account. This study proposes long-term strategies for increasing wind power capacity and sector sustainability, and it demonstrates that FANP could be an appropriate approach for prioritizing these strategies in current scenarios.

Evaluation of Digital Marketing Technologies with Fuzzy Linguistic MCDM Methods

Technology is becoming the tool that changes how people live every day, and the marketing strategies of businesses are also gradually shifting to the industry 4.0 mindset of constant growth and development. Digital marketing has changed human habits of information accessibility, determined their interactions, and witnessed the birth of a variety of new marketing technologies. Marketers are creating digital marketing products and services that enhance the experience for consumers, products, and services that are also delivered through high digital marketing networks. As a result, data sources become more abundant and allow consumers to have more choices. All products, services, technologies, and data are increasingly meeting the needs of consumers, thereby confirming the effectiveness of digital marketing in today’s market. However, the evaluation and selection of digital marketing technology is very complex since it has many conflicting criteria and goals. The multi-criteria decision-making model (MCDM) is a powerful technique widely used for solving this type of problem. Thus, the author proposed a fuzzy linguistic MCDM method for evaluation of digital marketing technologies. After determining the evaluation criteria and alternatives, two MCDM methods, including Spherical Fuzzy Analytic Hierarchy Process (SF-AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), are used in the evaluation and selection of digital marketing technologies procedure. Finally, an application is present to demonstrate the potential use of the proposed methodology. The main contribution of this study is to propose a Spherical fuzzy MCDM model to support planners and decision makers in the digital marketing technology evaluation and selection processes. A case study is also performed to showcase the feasibility of the proposed approach.

A multi-dimensional robust optimization approach for cold-chain emergency medical materials dispatch under COVID-19: A case study of Hubei Province

For the optimization problem of the cold-chain emergency materials (CEM) distribution routes with multi-demand centers and soft time windows and to solve dispatching materials to medical treatment institutions in various places of the disaster areas under COVID-19, a multi-dimensional robust optimization (MRO) model was proposed, which was solved by a hybrid algorithm combined Pareto genetic algorithm and the improved grey relative analysis (IGRA). The proposed model comprehensively takes into consideration of the cost factors of the cold-chain logistics and robustness of solution with the purpose of minimizing the costs and maximizing robustness. The availability of the proposed approach and hybrid algorithm were thoroughly discussed and qualified through a real-world numerical simulation test case, which was a previous risk area located at Hubei Province. Research results show an average-cost reduction of 4.51% and a robustness increment of 11.69% in addition to consider the urgencies of demand. Consequently, not only the costs can be slightly reduced and the robustness be heightened, but also the blindness of the distribution can be avoided effectively with the demand urgency being considered. Research result indicates that when combining with the specific process of supplies dispatching in the prevention and control, the proposed approach is in a far better agreement in practice, and it could meet the diverse requirements of the emergency scenarios flexibly.

Overlapping community detection in complex networks using fuzzy theory, balanced link density, and label propagation

Complex networks represent various real‐world systems. Overlapping community detection is one of the critical tasks in studying these networks and has significance to a wide variety of applications, including the exploration of online social networks because of the natural attitude of persons to participate in multiple communities at the same time. Despite a large number of existing community detection algorithms for detecting disjoint communities, the efficient and fast uncovering of overlapping communities has remained a challenging problem. To provide an efficient solution, on the one hand, the balanced link density label propagation (BLDLP) algorithm, proposed by the authors of the current study, is a fast, stable, and efficient method for disjoint community detection. On the other hand, the fuzzy theory is a worthwhile approach for overlapping community detection since it provides the membership rate of the overlapping nodes as well as the detection of overlapping communities. Hence, in this paper, based on the synergy of the BLDLP algorithm and the fuzzy theory, a novel method, called fuzzy BLDLP, for overlapping community detection is proposed. Fuzzy BLDLP is fast and efficient. The proposed method needs no prior information about the number of network communities to discover them. The experiments on both synthetic and real‐world known networks, including Zachary, Dolphins, and COVID‐19 Co‐authorship, have revealed that the proposed method successfully detects the overlapping nodes and communities and hence is comparable with the state‐of‐the‐art overlapping community detection algorithms in terms of recall, precision, F‐score and overlapping normalized mutual information. [ FROM AUTHOR] Copyright of Expert Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Fuzzy logic programming and adaptable design of medical products for the COVID-19 anti-epidemic normalization.

BACKGROUND: The COVID-19 prevention and control constantly affects lives worldwide. In this paper, household medical products were analyzed using fuzzy logic. Considering the household anti-epidemic status, economic and environmental benefits, the adaptable design method of anti-epidemic products in the vestibule was proposed. The measure of adaptable design method still have shortcomings. Therefore, an improved method that is based on fuzzy logic programming is required. METHOD: Firstly, common medical product types used in vestibules and household anti-epidemic products were identified and summarized into product sets. Then matching degree matrix was obtained by functional configuration decomposition and matching calculations. Secondly, experts were invited to evaluate the paired comparative probability matrices and linguistic variables, and the evaluation data were converted by trapezoidal membership functions, fuzzy numbers and the defuzzification method to obtain the usage probability values (PR) for product functions. Finally, the matching degree value (P) and the product function (PF) were calculated by adaptability measure formula, and product function, the adaptability factor and the adaptability (A) were obtained. RESULTS AND DISCUSSION: Our results show that the degree of adaptability of each product function in the product set from PF1 to PF10can be evaluated. Based on the principles of sorting of values from high to low, the top five PF (n = 10) for P value is PF10, PF5, PF6, PF8 and PF1; The top five PF for P value is PF2, PF1, PF3, PF7 and PF8; The top five PF for A value is PF2(0.242), PF1(0.232), PF5(0.225), PF8(0.222) and PF3(0.221). These values allow us to summarize and draw visual charts according to the above data sorting mode. The higher the value of the product function, the more it can be prioritized for design development with functional cost savings, simplification or clustering. CONCLUSION: This study proposes an adaptable design method based on fuzzy logic programming. The data results in this study can guide the development and programming of the vestibule anti-epidemic products. The higher adaptability value of a product function indicates that it is more capable of being simplified, clustered, and adapting to changes in the product set.