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.

The utilization of electrical and electronic equipment (EEEs) in waste recycling has become paramount for various countries. The waste electrical and electronic equipment (WEEE) recyclers own a crucial position in the environmental growth of a country as they help to minimize carbon emissions during the recycling of WEEE in the most eco-friendly way. Therefore, selecting and evaluating suitable WEEE recycling partners has become an important part of the decision-making (DM) system. The primary aim of this research is to establish a new method based on the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and a new artificial intelligence (AI) approach of feed-forward CPyFR neural network for determining WEEE recycling partners with uncertain weight information. For this, a new concept of complex Pythagorean fuzzy rough sets (CPyFRSs) is first defined to handle uncertainty in decision-making (DM) problems. A generalized distance measure is defined for calculating the weights of experts and criteria. The Dombi operators with operational parameters, have excellent flexibility. Since these Dombi operation parameters are very flexible, some Dombi Aggregation Operators (AOps) are provided to establish an optimal DM approach. Additionally, the developed methodology is applied to a real-world scenario to address the selection of e-waste recycling partners. Finally, to demonstrate the efficiency and consistency of the proposed model, a brief comparison of the proposed method with several existing methods is included.

Financial institutions attach great importance to the multiple attribute decision-making (MADM) problems of selecting an effective third-party logistics (3PL) service provider in supply chain finance. A suitable 3PL service provider can not only assist financial institutions in carrying out supply chain finance activity but can also replace financial institutions in supervising the operation of a target financing supply chain, therefore minimizing financial institutions' operating risks. However, there is no sufficient study on the selection of a 3PL service provider. Hence, the objective of this paper is to establish an innovative concept of the “probabilistic complex hesitant fuzzy set” (PCHFS), which is a hybrid structure of the complex hesitant fuzzy set and the probabilistic fuzzy set to manage complex information in real-decision theory. This paper examines the periodicity of probabilistic fuzzy information and extends the range from [$0,1$] to the unit disc in the complex plane to provide more ability to describe the full meaning of the information in a mathematical model. Based on the internal structure of the set and to find the degree of discrimination between the pairs of PCHFSs, the generalized distance measures and modified generalized distance measures are defined. Several properties and their relationships between them are derived in detail. Also, several cases of the proposed measures are exposed, which reduces them to the existing studies. Furthermore, based on these proposed measures, a multiple-attribute decision-making (MADM) approach is established in an uncertain environment, and several numerical examples are given to examine the feasibility and validity of the explored measures. Finally, the modified and parameterized distance measures based on PCHFSs are verified by comparing them with some existing measures.

Rough set and intuitionistic fuzzy set are very vital role in the decision making method for handling the uncertain and imprecise data of decision makers. The technique for order preference by similarity to ideal solution (TOPSIS) is very attractive method for solving the ranking and multi-criteria decision making (MCDM) problem. The primary goal of this paper is to introduce the Extended TOPSIS for industrial robot selection under intuitionistic fuzzy rough (IFR) information, where the weights of both, decision makers (DMs) and criteria are not-known. First, we develop Intuitionistic fuzzy rough (IFR) aggregation operators based on Einstein T-norm and T-conom, For this firstly we give the idea of intuitionistic fuzzy rough Einstein weighted averaging (IFREWA), intuitionistic fuzzy rough Einstein hybrid averaging (IFREHA) and intuitionistic fuzzy rough ordered weighted averaging (IFREOWA) aggregation operators. The fundamental properties of the proposed operators are described in detail. Furthermore to determine the unknown weights, a generalized distance measure are defined for IFRSs based on intuitionistic fuzzy rough entropy measure. Following that, the intuitionistic fuzzy rough information-based decision-making technique for multi-criteria group decision making (MCGDM) is developed, with all computing steps depicted in simplest form. For considering the conflicting attributes, our proposed model is more accurate and effective. Finally, an example of efficient industrial robot selection is presented to illustrate the feasibility of the proposed intuitionistic fuzzy rough decision support approaches, as well as a discussion of comparative outcomes, demonstrating that the results are feasible and reliable.

Branded apps offer both an essential tool for businesses and consumers to interact in real-time and share marketing messages as well as an innovative approach to business that promotes value co-creation. In order to explore the impact of branded apps on customers, this study provides a Three-way decision (TWDs) technique with decision-theoretic rough sets (DTRSs) for selecting branded apps under spherical double hierarchy linguistic term sets (SDHLTSs) information. SDHLTSs is a combination of the first hierarchy linguistic term and the second hierarchy linguistic term that can more flexibly describe ambiguity and uncertainty in decision-making (DM) problems. To aggregate the SDHLTSs, we propose a series of aggregation operators and fundamental operational laws for SDHLTSs. A grey relational analysis is considered to evaluate a conditional probability, which improves decision-making. A step-wise algorithm of the TWDs method based on decision-theoretic rough sets (DTRSs) is given for SDHLTSs. Further, a proposed aggregation operator is used to compute the loss function, and the decision’s results are decided by the minimum-loss principle. Finally, a real-world case study of the brand experience of a branded app is considered to demonstrate the efficiency of the proposed methods.