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The paper proposes a two-level closed-loop supply chain (CLSC) dynamic competitive model based on different competitive cooperation situations, and explores the impact of competitive cooperation methods on the pricing strategies, recycling and remanufacturing strategies and competitive model selection strategies of supply chain firms.

This paper establishes a CLSC game consisting of a manufacturer and two retailers. Firstly, five CLSC models are established in both horizontal and vertical dimensions, each of which competes with one another. Secondly, the recycling and remanufacturing pricing strategies are analyzed under different competition or cooperation models. Finally, the results are verified through numerical analysis.

The overall profitability of the CLSC is highest when the manufacturer–retailer partnership alliance is in place. The relationship between retailers and manufacturers is also found to be the best way to achieve overall optimization of the CLSC.

The paper investigates the relationship between the competitive partnership and the total profit of the CLSC, taking into account how to optimize the overall benefit, and focusing on how to optimize the individual interests of each participating enterprise. The results can provide basis and guidance for managers' pricing decision and competition cooperation.

The purpose of this paper is to explore the determinants of consumer purchase intention (CPI) of cross-border e-commerce (CBEC) in the countries of the Belt and Road Initiative (BRI).

This study proposes a research model of the antecedents of CPI on CBEC in BRI countries. Study participants were consumers with CBEC shopping experience in BRI-associated countries (n = 278). Structural equation modeling was used to test the research model.

Trust has the greatest effect on CPI, while perceived security has the least effect. In addition, in BRI-associated countries, in contrast to the previous study, product presentation was found to have a significant positive influence on CPI in CBEC. Platform simplicity and logistic service have a significant positive influence on CPI.

These findings offer important implications for CBEC. Consumers' trust in product providers has the greatest impact on CPI. Simplicity, timely shipment tracking and the fast delivery speed of the platform will increase CPI. The results suggest a highly successful tactic for enhancing consumers' perceptions of product authenticity and interest. Finally, this study provides insights into BRI.

This study contributes to the literature on CBEC. It explores the multilevel (i.e. product presentation, platform simplicity, logistic service, perceived security, and trust) determinants of CPI on CBEC. The study provides insights into the determinants of CPI in BRI countries.

Selecting suitable and competent partners is an important prerequisite to improve the performance of collaborative product innovation (CPI). The purpose of this paper is to propose an integrated multi-criteria approach and a decision optimization model of partner selection for CPI from the perspective of knowledge collaboration.

First, the criteria for partner selection are presented, considering comprehensively the knowledge matching degree of the candidates, the knowledge collaborative performance among the candidates, and the overall expected revenue of the CPI alliance. Then, a quantitative method based on the vector space model and the synergetic matrix method is proposed to obtain a comprehensive performance of candidates. Furthermore, a multi-objective optimization model is developed to select desirable partners. Considering the model is a NP-hard problem, a non-dominated sorting genetic algorithm II is developed to solve the multi-objective optimization model of partner selection.

A real case is analyzed to verify the feasibility and validity of the proposed model. The findings show that the proposed model can efficiently select excellent partners with the desired comprehensive attributes for the formation of a CPI alliance.

Theoretically, a novel method and approach to partner selection for CPI alliances from a knowledge collaboration perspective is proposed in this study. In practice, this paper also provides companies with a decision support and reference for partner selection in CPI alliances establishment.

The new Silk Road provides cross-border-e-commerce firms with an opportunity to widen their markets. Under this circumstance, the preference recognition of countries and inventory allocation among overseas warehouses both become critical issues to solve. Three Chinese smartphone brands, including HTC, Huawei, and MI, are selected in this chapter for their relatively enormous sales. DHgate and AliExpress websites are chosen as platforms to analyze the sales for data availability. This chapter first depicts key features of the sales and then, based on which, divide countries into several groups according to their preference for phones by cluster analysis. Then, based on the results of cluster analysis, this chapter further models the inventory assignment among the seven major overseas warehouses that were built by AliExpress in 2015. The results show that the HTC seems to be pursuing the “high value with high price” strategy, while the other two companies seem to be pursuing a hybrid strategy of “low-price” strategy and “high value with low price” strategy. This chapter also provides an assignment pattern of inventory among the overseas warehouses based on the real data of sales and costs.

The purpose of this paper is to design an inference engine to measure the level of readiness of each bank before starting the corporate sustainability auditing process. Based on the output of the designed inference engine, the audition team can decide about the audition resources and the auditing process.

In this paper, the hybrid rough and grey set theory are used to design and create a rule model system to measure the sustainability level of banks. First, 16 rule models are extracted using rough set theory (RST), and the cross-validation of each model is done. Then, the grey clustering is used to combine the same condition attributes and improve the validity of the final model. A total of 16 new rule models are extracted based on the decreased condition attributes, and the best model is selected based on the cross-validation results.

By comparing the accuracy of rough-gray’s rule models and as a result of decreasing the condition attributes, a proper increase in the accuracy of all models is obtained. Finally, the Naive/Genetic/object-related reducts model with 95.6% accuracy is selected as an inference engine to measure new banks’ readiness level.

Sustainability measurement of banks based on RST is a new approach in the field of corporate sustainability. Furthermore, using the grey clustering for combining the condition attributes is a novel solution for improving the accuracy of the rule models.

In this study, a hybrid rough and grey set-based rule model is designed for diagnosis of one type of blood cancer called multiple myeloma (MM). The grey clustering method is used to combine the same condition attributes and to improve the validity of the final model.

Some tools of the rough set theory (RST) and grey incidence analysis (GIA) are used in this research to analyze the serum protein electrophoresis (SPE) test results. An RST-based rule model is extracted based on the laboratory SPE test results of patients. Also, one decision attribute and 15 condition attributes are used to extract the rules. About four rule models are constructed due to the different algorithms of data complement, discretization, reduction and rule generation. In the following phases, the condition attributes are clustered into seven clusters by using a grey clustering method, the value set of the decision attribute is decreased by using manual discretizing and the number of observations is increased in order to improve the accuracy of the model. Cross-validation is used for evaluation of the model results and finally, the best model is chosen with 5,216 rules and 98% accuracy.

In this paper, a new rule model with high accuracy is extracted based on the combination of the grey clustering method and RST modeling for diagnosis of the MM disease. Also, four primary rule models and four improved rule models have been extracted from different decision tables in order to define the result of SPE test of patients. The maximum average accuracy of improved models is equal to 95% and related to the gamma globulins percentage attribute/object-related reducts (GA/ORR) model.

The total number of observations for rule extraction is 115 and the results can be improved by further samples. To make the designed expert system handy in the laboratory, new computer software is under construction to import data automatically from the electrophoresis machine into the resultant rule model system.

The main originality of this paper is to use the RST and GST together to design and create a hybrid rule model to diagnose MM. Although many studies have been carried out on designing expert systems in medicine and cancer diagnosis, no studies have been found in designing systems to diagnose MM. On the other hand, using the grey clustering method for combining the condition attributes is a novel solution for improving the accuracy of the rule model.

Risk management is one of the most influential parts of project management that has a major impact on the success or failure of projects. Due to the increasing use of information technology in all fields and the high failure rate of software development projects, it is essential to predict the risk level of each project effectively before starting. Therefore, the main purpose of this paper is proposing an expert system to infer about the risk of new banking software development project.

In this research, the risk of software developing projects is considered from four dimensions including risk of cost deviation, time deviation, quality deviation and scope deviation, which is examined by rough set theory (RST). The most important variables affecting the cost, time, quality and scope of projects are identified as condition attributes and four initial decision systems are constructed. Grey system theory is used to cluster the condition attributes and after data discretizing, eight rule models for each dimension of risk as a decision attribute are extracted using RST. The most validated model for each decision attribute is selected as an inference engine of the expert system, and finally a simple user interface is designed in order to predict the risk level of any new project by inserting the data of project attributes

In this paper, a high accuracy expert system is designed based on the combination of the grey clustering method and rough set modeling to predict the risks of each project before starting. Cross-validation of different rule models shows that the best model for determining cost deviation is Manual/Jonson/ORR model, and the most validated models for predicting the risk of time, quality and scope of projects are Entropy/Genetic/ORR, Manual/Genetic/FOR and Entropy/Genetic/ORR models; all of which are more than 90% accurate

It is essential to gather data of previous cases to design a validated expert system. Since data documentation in the field of software development projects is not complete enough, grey set theory (GST) and RST are combined to improve the validity of the rule model. The proposed expert system can be used for risk assessment of new banking software projects

The risk assessment of software developing projects based on RST is a new approach in the field of risk management. Furthermore, using the grey clustering for combining the condition attributes is a novel solution for improving the accuracy of the rule models.

Although an important facet of modernist architecture in which function plays a prominent role, building flexibility is not entirely a new concept. Its relevance transcends generations, allowing space and structure to evolve through time. This paper investigates the relationship among main building structures, infill elements, and space by studying examples in ancient Chinese architecture. It reveals the role of building owners, users, and craftsmen from a survey of historical documentation. In studying these examples, it is concluded that craftsmen in ancient China were involved not only during the construction phase but throughout the period of use as well. Thus, in select cases, the relationship between craftsmen and owners or users had been preserved for generations. Finally, this paper suggests potential strategies for the building industry and technology in the move towards sustainable development.

The purpose of this paper is to investigate the nonlinear dynamics of the aircraft assembly lines. An approach for modeling and analyzing the production rate of an aircraft assembly line is introduced using the chaos theory.

First, two key system variables including reliability and learning ability are considered to control the dynamics model. The discrete-time dynamics equation of the production rate is established as a function of the reliability and the learning rate. Then an improved Gauss-learning curve is proposed and applied to aircraft assembling condition. Finally, the bifurcation diagrams and the maximal Lyapunov exponents are used and applied to the experimental study to analyze the dynamic behavior under different combinations of parameters.

On the basis of the experimental study, it is shown that chaotic behavior really exists in the aircraft assembly lines. The reliability and the Gauss-learning curve can nonlinearly affect the production rate.

This paper applied nonlinear dynamics and chaotic theory to the production analyses of the aircraft assembly lines for the first time. The proposed model has been successfully applied to a practical case, and the result justifies its advantage as well as feasibility to both theory and engineering application.