Tax is the main source of national and regional income, and the efficiency of tax collection and administration is of great significance to the sustainable development. In order to improve the efficiency of tax collection and administration in the less developed regions of western China due to the slow economic development, the low volume of tax revenue, and the high cost of tax collection and administration, this study extends the two‐stage DEA model to the case of asymmetric two‐stage efficiency values and analyzes the tax collection and administration efficiency of Yunnan Province by the improved model. The results show that the average value of tax collection and administration efficiency of Yunnan Province in 2020 is 0.944, and the overall efficiency values of tax collection and administration in most of the states and cities are within the range of 0.8–1.0. The overall tax collection and administration efficiency of Yunnan Province is relatively ineffective and can be improved. This study also makes relevant suggestions on how to improve the efficiency of organizations to promote the sustainable development of regions and the nation.

We consider a task assignment problem in crowdsourcing, which is aimed at collecting as many reliable labels as possible within a limited budget. A challenge in this scenario is how to cope with the diversity of tasks and the task-dependent reliability of workers; for example, a worker may be good at recognizing the names of sports teams but not be familiar with cosmetics brands. We refer to this practical setting as heterogeneous crowdsourcing. In this letter, we propose a contextual bandit formulation for task assignment in heterogeneous crowdsourcing that is able to deal with the exploration-exploitation trade-off in worker selection. We also theoretically investigate the regret bounds for the proposed method and demonstrate its practical usefulness experimentally.

Logistics service quality (LSQ) is one of the key influential factors in the success of an ecommerce business. In view of the complexity of the topic, this paper proposes a novel model for fresh ecommerce cold chain LSQ evaluation based on the catastrophe progression method. In the proposed methodology, first an index system for evaluating the fresh ecommerce cold chain LSQ is established from the perspective of service recipients. Then, the comprehensive weight of each evaluation index is determined using a combination weighting approach based on maximizing deviations and fuzzy set theory. The priority weights and the ranking of the indices are determined using the catastrophe progression method. Finally, the model is applied in a case study of two representative enterprises. The study demonstrates the validity and practical applicability of the proposed model. Also, based on the evaluation results and findings, some improvement suggestions are made for improving the cold chain LSQ of similar kinds of fresh ecommerce companies.

Blockchain technology has subverted traditional supply chain operational models and transformed information interactions along supply chains. This paper examines the impact of blockchain technology on supply chain information collaboration and operating costs. This paper develops a three-level supply chain model based on blockchain technology that incorporates retailer sensitivity to information. First, the manufacturer’s profit function is developed, and the optimal information-sharing quantity and supply chain pricing decisions are analysed. Then, cost models for both the traditional supply chain and the novel supply chain using blockchain technology are developed and the impact of blockchain technology on supply chain operating costs is determined. The results demonstrate that blockchain technology can effectively reduce supply chain operating costs. In addition, this study has an interesting finding that if blockchain adoption is valuable for the supply chain, the quantity of information-sensitive should be moderate. Too many or too few information-sensitive retailers can reduce the value of the blockchain's use. This is because blockchain is implementation will increase the privacy concerns of supply chain companies.

A shortage or backlog of inventory can easily occur due to the backward forecasting method typically used, which will affect the normal flow of funds in pharmacies. This paper proposes a replenishment decision model with back propagation neural network multivariate regression analysis methods. With the regular pattern between sales and individual variables, supplemented with the safety stock empirical formula, an accurate replenishment quantity can be obtained. In the case analysis, this paper takes the sales situation of a pharmacy as an example and tests the accuracy and stability of the model. The results show that the model has good prediction accuracy which can be introduced into the intelligent pharmacy system and used in the replenishment of the intelligent pharmacy to prevent overstocking or a shortage of stock, thus improving the financial situation, reducing the manpower burden of typical retail pharmacy, and helping residents buy medicines.