The reshoring decision under uncertainty in the post-COVID-19 era

Purpose Using insights from the supply chain resilience perspective and the international business literature, this study aims to investigate the determinants of firms’ decisions to reshore manufacturing under the high levels of uncertainty brought about by the ongoing US–China trade war and COVID-19 pandemic. Design/methodology/approach The proposed conceptual framework is tested using survey data collected from 702 Taiwanese firms with manufacturing in China. The firms were drawn from a database compiled by Taiwan’s Ministry of Economic Affairs. Findings The results show that two supply chain factors (tariffs and supply chain completeness) and two non-location-bound factors (labor cost and material cost) are critical determinants of the decision to reshore under uncertainty. Originality/value This research elucidates and empirically validates several factors that influence the reshoring decision in uncertain environments. The findings provide valuable theoretical, practical and strategic insights into how firms should manage their value chains in the post-COVID-19 era.

Dynamic Two-sided Matching Group Decision-making Model With Multi-form Preference Information for Configuring Tasks and Resources in Cloud Manufacturing

Enterprises have been faced with the problem of how to optimize resource allocation in an uncertain environment by the expanding of manufacturing informatization. In the process of cloud manufacturing matching, group decision making organizations may provide uncertain preference information. However, preference information at various points have led to differing impacts of the final matching decision. it is necessary to study the dynamic two-sided matching. In this paper, the dynamic two-sided matching problem under the multi-form preference information was studied. Primarily, the problem of two-sided matching is described, then through group decision-making and uncertain preference information, an ordinal vector matrix is constructed. Afterwards, the comprehensive satisfaction matrix is calculated by using dynamic time-series weight and matching competition degree. Further, by introducing stable matching constraints, a multi-objective optimization model considering the satisfaction, fairness and stability of matching is constructed. Then the optimal matching result is obtained by solving the model. In addition, the presented method was verified through a case of cloud manufacturing. At the end, advantages of the presented model were demonstrated by comparison. Research results of this paper enrich the theoretical research of two-sided matching and provide an effective solution for cloud manufacturing matching in uncertain environments.

Acknowledging Sustainable Performance from the Global Crisis of COVID-19 in the Context of Bangladesh

Internationally, the World Health Organization (WHO) announced a public health extremity due to the outbreak of a novel coronavirus named COVID-19 in January 2020. It has been over a year since the globe was imprisoned by COVID-19, despite discovering numerous vaccines to combat the virus’s innumerable versions. With this aim, this research has organized around three themes to achieve this goal. Firstly, to explain the scenario of the influence of COVID-19 on the sustainable performance (environmental, social, and economic) of Bangladesh’s ready-made garment (RMG) industry. There has been a dramatic increase in the application of sustainable performance over recent decades, but less attention paid to developing countries, especially Bangladesh. Secondly, general online survey research has been conducted from July-August 2021 to empirically evaluate the effects of the COVID-19 crisis on the RMG industries of Bangladesh. Thirdly, the study has provided recommendations to overcome any pandemic to maintain sustainable business performance. According to the survey results, 55.9% of participants assume there will be a loss in revenue and sales volume, while 44.4 percent are concerned about employee health and an increase in waste (using PPE, gloves, masks, and so on) in the industries during COVID-19 pandemic as well as 52.8% of employees anticipate that, Bangladesh’s loss of position against Vietnam is due to an ineffective sustainable business system. However, this pandemic has proved that business organizations should be more conscious in dealing with uncertain environments while sustainable performance can be a strategic solution.

Service supplier selection under fuzzy and stochastic uncertain environments

Owing to the heterogeneity and inherent uncertainty of services, the selection of service suppliers is a complicated multi-attribute group decision-making (MAGDM) problem in which fuzzy criteria and stochastic criteria coexist. During the past few decades, many real-world supplier selection problems have been resolved using MAGDM methods. Nevertheless, extant research on supplier selection considers either fuzzy criteria or stochastic criteria, and hence most of these methods cannot address the complex and unstructured nature of contemporary service supplier selection problems. In this study, a novel technique for order preference by similarity to the ideal solution (TOPSIS) approach, integrating both fuzzy criteria and stochastic criteria, is developed; in this approach, the interval-valued intuitionistic fuzzy (IVIF) cross-entropy for fuzzy criteria and the Euclidean distance for stochastic criteria are used to acquire the rankings of alternatives. Moreover, a sensitivity analysis is conducted for a case study of hoisting service supplier selection, and a comparative analysis with other existing methods is performed to confirm the effectiveness and efficiency of the proposed approach.

Dynamic Two-sided Matching Group Decision-making Model with Multi-form Preference Information for Configuring Tasks and Resources in Cloud Manufacturing

Enterprises have been faced with the problem of how to optimize resource allocation in an uncertain environment by the expanding of manufacturing informatization. In the process of cloud manufacturing matching, group decision making organizations may provide uncertain preference information. However, preference information at various points have led to differing impacts of the final matching decision. it is necessary to study the dynamic two-sided matching. In this paper, the dynamic two-sided matching problem under the multi-form preference information was studied. Primarily, the problem of two-sided matching is described, then through group decision-making and uncertain preference information, an ordinal vector matrix is constructed. Afterwards, the comprehensive satisfaction matrix is calculated by using dynamic time-series weight and matching competition degree. Further, by introducing stable matching constraints, a multi-objective optimization model considering the satisfaction, fairness and stability of matching is constructed. Then the optimal matching result is obtained by solving the model. In addition, the presented method was verified through a case of cloud manufacturing. At the end, advantages of the presented model were demonstrated by comparison. Research results of this paper enrich the theoretical research of two-sided matching and provide an effective solution for cloud manufacturing matching in uncertain environments.

Reliability-Based Multi-Vehicle Path Planning Under Uncertainty Using a Bio-Inspired Approach

Identifying a reliable path in uncertain environments is essential for designing reliable off-road autonomous ground vehicles (AGV) considering post-design operations. This paper presents a novel bio-inspired approach for model-based multi-vehicle mission planning under uncertainty for off-road AGVs subjected to mobility reliability constraints in dynamic environments. A physics-based vehicle dynamics simulation model is first employed to predict vehicle mobility (i.e., maximum attainable speed) for any given terrain and soil conditions. Based on physics-based simulations, the vehicle state mobility reliability in operation is then analyzed using an adaptive surrogate modeling method to overcome the computational challenges in mobility reliability analysis by adaptively constructing a surrogate. Subsequently, a bio-inspired approach called Physarum-based algorithm is used in conjunction with a navigation mesh to identify an optimal path satisfying a specific mobility reliability requirement. The developed Physarum-based framework is applied to reliability-based path planning for both a single-vehicle and multiple-vehicle scenarios. A case study is used to demonstrate the efficacy of the proposed methods and algorithms. The results show that the proposed framework can effectively identify optimal paths for both scenarios of a single and multiple vehicles. The required computational time is less than the widely used Dijkstra-based method.

Vague multi-attribute group decision-making method based on evidence theory with a new aspect to solve weights

In order to solve the problems of weight solving and information aggregation in the Vague multi-attribute group decision-making, this paper first solves the weight of Vague evaluation value, and then fuses the information of Vague sets through evidence theory, and obtains an information aggregation algorithm for Vague multi-attribute group decision-making. Firstly, The algorithm draws on the idea of solving the weight of evidence in the improved evidence theory algorithm, and calculates the weight of Vague evaluation value, and revises the original evaluation information after obtaining the weight of each Vague evaluation value. Secondly, this algorithm analyzes the mathematical relationship between the Vague sets and the evidence theory, and uses the evidence theory to fuse the evaluation information to obtain the final Vague evaluation value of each alternative. Finally, this algorithm uses a score function to calculate the score of each alternative to determine the best alternative. The algorithm given in the paper enables decision-makers to make rational decisions in uncertain environments, and then select the best alternative.