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The purpose of this paper is to propose an innovative integration method based on decision-theoretic rough set and the extended VlseKriterijuska Optimizacija I Komoromisno Resenje (VIKOR) methods to address the resilient-sustainable supplier selection and order allocation (SS/OA) problem.

Specifically, a two-stage approach is designed in this paper. First, the decision-theoretic rough set is employed to calculate the rough number for coping with the subjective uncertainty of data and assigning the weights for a resilient-sustainable evaluation criterion. On this basis, the supplier resilient-sustainable performance is ranked in combination with the extended VIKOR method. Second, a novel multi-objective optimization model is proposed that applies an improved genetic algorithm to select the resilient-sustainable supplier and allocate the corresponding order quantity under a multi-tier supplier network.

The results reveal that joint consideration of resilience and sustainability is essential in the SS/OA process. The method proposed in this study based on decision-theoretic rough sets and the extended VIKOR method can handle imprecise information flexibly, reduce information loss and obtain acceptable solutions for decision-makers. Numerical cases validate that this integrated approach can combine resilience and sustainability for effective and efficient SS/OA.

This paper provides industry managers with a new perspective on SS/OA from a resilience and sustainability perspective as a basis for best practices for industry resilience and sustainability. The proposed method helps to evaluate the resilient-sustainable performance of potential suppliers, which is applicable to solving real-world SS/OA problems and has important practical implications for the resilient-sustainable development of supply chains.

The two interrelated priorities of resilience and sustainability have emerged as key strategic challenges in SS/OA issues. This paper is the first study of this issue that uses the proposed integrated approach.

The purpose of this paper is to propose a three-way group decision-making approach to address the selection of green supplier, by extending decision-theoretic rough set (DTRS) into hesitant fuzzy linguistic (HFL) environment, considering the flexible evaluation expression format of HFL term set (HFLTS) and the idea of minimum expected risk in DTRS.

Specifically, the authors first present the calculation method of the conditional probability and discuss the loss functions of DTRS with HFL element (HFLE), along with some associated properties being investigated in detail. Further, three-way group decisions rules can be deduced, followed by the cost of every green supplier candidate. Thus, based on these discussions, a novel green supplier selection DTRS model that combines multi-criteria group decision-making (MCGDM) and HFLTS is designed.

A numerical example of green supplier selection, the comparative analysis and associated discussions are conducted to illustrate the applicability and novelty of the presented model.

The selection of green supplier has played a critically strategic role in sustainable enterprise development due to continuous environmental concerns. This paper offers an insight for companies to select green supplier selection from the perspective of three-way group decisions.

This paper uses three-way decisions to address green supplier selection in the HFL context, which is considered as a MCGDM issue.

Previous studies on hospital supply chain performance have attempted to measure the performance of the hospital supply chain either by the measurement of performance indicators or the performance of specific activities. This paper attempts to measure the internal hospital supply chain's performance indicators to find their interdependencies to understand the relationship among them and identify the key performance indicators for each of those aspects of the logistics process toward improvement.

In this research, a systematic assessment and analysis method under vagueness is proposed to assess, analyze and measure the internal health care performance aspects (HCPA). The proposed method combines the group Decision-Making and Trial Evaluation Laboratory (DEMATEL) method and rough set theory.

The study results indicate that the most critical aspects of hospital supply chain performance are completeness of treatment, clinical care process time and no delay in treatment.

The causal relationship from rough-DEMATEL can advise management officials that to improve the completeness of treatment toward patient safety, clinical care process time should be addressed initially and with it, patient safety aspects such as free from error, clinical care productivity, etc. should be improved as well. Improvement of these aspects will improve the other aspects they are related to.

This paper aims to introduce a novel structure for the physical internet (PI)–enabled sustainable supplier selection and inventory management problem under uncertain environments.

To address hybrid uncertainty both in the objective function and constraints, a novel interactive hybrid multi-objective optimization solution approach combining Me-based fuzzy possibilistic programming and interval programming approaches is tailored.

Various numerical experiments are introduced to validate the feasibility of the established model and the proposed solution method.

Due to its interconnectedness, the PI has the opportunity to support firms in addressing sustainability challenges and reducing initial impact. The sustainable supplier selection and inventory management have become critical operational challenges in PI-enabled supply chain problems. This is the first attempt on this issue, which uses the presented novel interactive possibilistic programming method.

We attempt to construct a grey three-way conflict analysis model with constraints to deal with correlated conflict problems with uncertain information.

In order to address these correlated conflict problems with uncertain information, considering the interactive influence and mutual restraints among agents and portraying their attitudes toward the conflict issues, we utilize grey numbers and three-way decisions to propose a grey three-way conflict analysis model with constraints. Firstly, based on the collected information, we introduced grey theory, calculated the degree of conflict between agents and then analyzed the conflict alliance based on the three-way decision theory. Finally, we designed a feedback mechanism to identify key agents and key conflict issues. A case verifies the effectiveness and practicability of the proposed model.

The results show that the proposed model can portray their attitudes toward conflict issues and effectively extract conflict-related information.

By employing this approach, we can provide the answers to Deja’s fundamental questions regarding Pawlak’s conflict analysis: “what are the underlying causes of conflict?” and “how can a viable consensus strategy be identified?”

This paper aims to utilize machine learning and soft computing to propose a new method of rough sets using deep learning architecture for many real-world applications.

The objective of this work is to propose a model for deep rough set theory that uses more than decision table and approximating these tables to a classification system, i.e. the paper propose a novel framework of deep learning based on multi-decision tables.

The paper tries to coordinate the local properties of individual decision table to provide an appropriate global decision from the system.

The rough set learning assumes the existence of a single decision table, whereas real-world decision problem implies several decisions with several different decision tables. The new proposed model can handle multi-decision tables.

The proposed classification model is implemented on social networks with preferred features which are freely distribute as social entities with accuracy around 91 per cent.

The deep learning using rough sets theory simulate the way of brain thinking and can solve the problem of existence of different information about same problem in different decision systems

This paper utilizes machine learning and soft computing to propose a new method of rough sets using deep learning architecture for many real-world applications.

Influence diagrams (IDs) have been widely applied as a form of knowledge expression and a decision analysis tool in the management and engineering fields. Relationship measurements and expectation values are computed depending on probability distributions in traditional IDs, however, most information systems in the real world are nondeterministic, and data in information tables can be interval valued, multiple valued and even incomplete. Consequently, conventional numeric models of IDs are not suitable for information processing with respect to imprecise data whose boundaries are uncertain. The paper aims to discuss these issues.

The grey system theory and rough sets have proved to be effective tools in the data processing of uncertain information systems, approximate knowledge acquisition and representation are also the objectives in intelligent reasoning and decision analysis. Hence, this study proposes a new mathematical model by combining grey rough sets with IDs, and approximate measurements are used instead of probability distribution, an implicational relationship is utilized instead of an indiscernible relationship, and all of the features of the proposed approach contribute to deal with uncertain problems.

The focus of this paper is to provide a more comprehensive framework for approximate knowledge representation and intelligent decision analysis in uncertain information systems and an example of decision support in product management systems with the new approach is illustrated.

Collaboration of IDs and grey rough sets is first proposed, which provides a new mathematical and graphical tool for approximate reasoning and intelligent decision analysis within interval-valued information systems.

The paper attempts to construct a novel multi-objective grey hierarchical group consensus approach to deal with the group consensus problems consisting of hierarchical relationship and non-cooperative behaviors among decision makers (DMs).

To deal with these group consensus problems consisting of hierarchical relationship and non-cooperative behaviors among DMs non-cooperative behavior in uncertain information systems, considering the influence of coordination cost and the degree of group consensus, based on the idea of grey situation decision-making, the authors establish a multi-objective grey hierarchical group consensus model, and design different invalid decision elimination rules for decision-making groups of different sizes, and use a case verifies the effectiveness and feasibility of the model.

With the continuous improvement of the coordination cost budget, the degree of consensus of all departments and the overall consensus tend to be stable, and will no longer change with the increase of the coordination cost budget. The cost required by each department is basically consistent with the response trend of the cost required to coordinate the overall situation to the pre-set lower limit of group consensus.

The proposed approach can succeed in identifying DMs' information, and mine the DMs' information and help make a relatively more scientific decision.

The aim of this paper is to propose an optimization method for determining the emergency action, in which the compatibility between emergency alternatives and the collaborative relationship between departments are considered.

The individual emergency cost and individual emergency effect of each emergency alternative are calculated. And the collaborative emergency cost and collaborative emergency effect associated with a pair of emergency alternatives are calculated. Then, a bi-objective programming model maximizing the total emergency effect and minimizing the total emergency cost is constructed. A novel nondominated sorting genetic algorithm II (NNSGA II) is designed to solve the constructed model, subsequently. Finally, an example is given to illustrate the use of the proposed method, and the performance of NNSGA II is evaluated through a simulation experiment.

This paper proposes an effective method to manage complex emergency events that requires the coordinations of multiple departments. Also, this paper provides a new algorithm to determine an appropriate emergency action that performs well in managing both the emergency cost and emergency effect.

The findings contribute to the current methods in the field of emergency management. The method is used for dealing with the individual information of emergency alternatives and the collaborative information associated with a pair of alternatives.

Because of the properties of loess, the occurrence of collapse following deformation of a large settlement is a common problem during the excavation of tunnels on loess ground. Hence, risk management for safer loess tunnel construction is of great significance. The purpose of this paper is to explore the influence of factors on collapse risk of loess tunnels and establish a risk assessment model using rough set theory and extension theory.

The surrounding rock level, groundwater conditions, burial depth, excavation method and support close time were selected as the factors and settlement deformation was the verification index for risk assessment. First, using rough set theory, the influence of risk factors on the collapse risk of loess tunnels was calculated by researching engineering data of excavated sections. Then, a collapse risk assessment model was developed based on extension theory. As the final step, the model was applied to practical engineering in the Loess Plateau of China.

The weights of surrounding rock level, groundwater conditions, burial depth, excavation method and support close time obtained using rough set theory were respectively 10.811 per cent, 18.919 per cent, 24.324 per cent, 40.541 per cent and 5.406 per cent. The assessment results obtained using the model were in good agreement with field observations.

This study highlights key points in collapse risk management of loess tunnels, which could be very useful for future construction methods. The model, using easily obtained parameters, helps in predicting the collapse risk level of loess tunnels excavated under different geological conditions and by different construction organizations and provides a reference for future studies.