Three-way decision based on third-generation prospect theory with Z-numbers

A new three way decision making technique for supplier selection in logistics service value Cocreation under intuitionistic double hierarchy linguistic term set

In today's business world, choosing a logistics supplier is a critical factor for companies to improve operational efficiency and reduce business costs. With the development of market economy, it is very difficult for companies to choose a suitable logistics provider according to specific rules. Therefore, this study proposes a new three-way decision making (TWD) technique for supplier selection in logistics service value creation. For this, we first develop a new concept called intuitionistic double hierarchy linguistic term set (IDHLTSs) that can describe uncertainty and ambiguity in a more flexible way. Some Hamacher aggregation operators for collecting IDHLTSs information and its basic aspect are proposed. The unknown weight vector for decision experts and criteria is determined by using entropy measures. In addition, the conditional probability is determined using TOPSIS which makes the decision making process more rational. And the decision result is conducted according to minimum loss principle. Finally, an example of 3 PL supplier selection in the logistics service value co-creation environment and comparison is given to validate and demonstrate the effectiveness of the developed method.

A three-way decisions approach based on double hierarchy linguistic aggregation operators of strict t-norms and t-conorms

With the massive increase in uncertainty of linguistic information in realistic decision making, there is a great challenge for people to make decisions in the complex linguistic environment. To overcome this challenge, this paper proposes a three-way decisions method based on aggregation operators of strict t-norms and t-conorms under double hierarchy linguistic environment. By mining the double hierarchy linguistic information, strict t-norms and t-conorms are introduced to define the operation rules and their operation examples are also given. Then, the double hierarchy linguistic weighted average (DHLWA) operator and weighted geometric (DHLWG) operator are proposed based on strict t-norms and t-conorms. Besides, some of their important properties are also proved and derived, such as idempotency, boundedness and monotonicity. Next, DHLWA and DHLWG are integrated with three-way decisions to construct our three-way decisions model. Specifically, the double hierarchy linguistic decision theoretic rough set (DHLDTRS) model is constructed by incorporating the computational model of expected loss with DHLWA and DHLWG, which can consider the various decision attitudes from decision makers more adequately. Furthermore, we also propose a novel entropy weight calculation formula to improve the entropy weight method for obtaining the weights more objectively, and integrate grey relational analysis (GRA) method to calculate the conditional probability. Based on the Bayesian minimum-loss decision rules, the solving method of our model is also propounded and the corresponding algorithm is designed. Finally, an illustrative example and experimental analysis are presented, which can validate the rationality, robustness as well as superiority of our method.

A flexible group decision-making method for green supplier selection integrating MABAC and CRITIC method under the linguistic Z-numbers environment

This paper intends to treat the green supplier selection (GSS) problem as a multi-attribute group decision making (MAGDM) problem, adopt the linguistic Z-number that can more flexibly and accurately express the evaluation information, and expand the traditional multi-attribute boundary approximate area comparison (MABAC) method, combine the CRITIC method of standard importance and consider the risk vector to finally determine the optimal solution. More specifically, the linguistic Z-number is used to describe the fuzzy evaluation information of experts on alternatives under attributes, then the expanded CRITIC method is used to obtain the weight of each given attribute, and finally the MABAC method with added risk vector and expanded is used to obtain the ranking of alternatives and obtain the best solution. Finally, taking green supplier selection as an example, and comparing with other methods, the reliability and effectiveness of the constructed method are verified. The results show that this method can express the evaluation information of experts flexibly and completely, and obtain the ranking results of given schemes through fewer steps, which is reliable and effective.

Rice Mapping in a Subtropical Hilly Region Based on Sentinel-1 Time Series Feature Analysis and the Dual Branch BiLSTM Model

Timely and accurate information on rice cultivation makes important contributions to the profound reform of the global food and agricultural system, and promotes the development of global sustainable agriculture. With all-day and all-weather observing ability, synthetic aperture radar (SAR) can monitor the distribution of rice in tropical and subtropical areas. To solve the problem of misclassification of rice with no marked signal during the flooding period in subtropical hilly areas, this paper proposes a new feature combination and dual branch bi-directional long short-term memory (DB-BiLSTM) model to achieve high-precision rice mapping using Sentinel-1 time series data. Based on field investigation data, the backscatter time series curves of the rice area were analyzed, and a characteristic index (VV − VH)/(VV + VH) (VV: vertical emission and vertical receipt of polarization, VH: vertical emission and horizontal receipt of polarization) for small areas of hilly land was proposed to effectively distinguish rice and non-rice crops with no marked flooding period. The DB-BiLSTM model was designed, ensuring the independent learning of multiple features and effectively combining the time series information of both (VV − VH)/(VV + VH) and VH features. The city of Shanwei, Guangdong Province, China, was selected as the study area. Experimental results showed that the overall accuracy of the rice mapping results was 97.29%, and the kappa coefficient reached 0.9424. Compared to other methods, the rice mapping results obtained by the proposed method maintained good integrity and had less misclassification, which demonstrated the proposed method’s practical value in accurate and effective rice mapping tasks.

Collaborative Extraction of Paddy Planting Areas with Multi-Source Information Based on Google Earth Engine: A Case Study of Cambodia

High-precision spatial mapping of paddy planting areas is very important for food security risk assessment and agricultural monitoring. Previous studies have mainly been based on multi-source satellite imagery, the fusion of Synthetic Aperture Radar (SAR) with optical data, and the combined use of multi-scale and multi-source sensors. However, there have been few studies on paddy spatial mapping using collaborative multi-source remote sensing product information, especially in tropical regions such as Southeast Asia. Therefore, based on the Google Earth Engine (GEE) platform, in this study, Cambodia, which is dominated by agriculture, was taken as the study area, and an extraction scheme for paddy planting areas was developed from collaborative multi-source information, including multi-source remote sensing images (Sentinel-1 and Sentinel-2), multi-source remote sensing land cover products (GFSAD30SEACE, GLC_FCS30-2015, FROM_GLC2015, SERVIR MEKONG, and GUF), paddy phenology information, and topographic features. Evaluation and analysis of the extraction results and the SERVIR MEKONG and ESACCI-LC paddy products revealed that the accuracy of the paddy planting areas extracted using the proposed method is the highest, with an overall accuracy of 89.90%. The results of the proposed method are better than those of the other products in terms of the outline of the paddy planting areas and the description of the road information. The results of this study provide a reference for future high-precision paddy mapping.