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The smart supply chain innovation (SSCI) has become the key way for enterprises to enhance their competitiveness. Therefore, it is very important for the current supply chain enterprises to choose a reasonable innovation path. Through the literature review method, this paper summarizes the four main evaluation indicators of path selection for smart supply chain innovation, which are technical indicators, organizational environment indicators, operational efficiency indicators, risk prevention and control indicators. According to the characteristics of evaluation index, the improved Fuzzy Entropy-Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method is proposed. This method considers the situation that the original data contains both interval value and fixed value data. Firstly, the standardized decision matrix is constructed, and then the optimal index weight is established by Fuzzy Entropy method. Secondly, this paper calculates the weighted decision matrix by using the method of sub item weighting of hierarchical indicators. Finally, the improved TOPSIS method is used to determine the relative closeness of each scheme. According to the weighted decision matrix, the innovation path index of the smart supply chain (SSC) is further calculated. We use the actual case of a company to analyze the evaluation method for three different SSCI paths. This paper provides a reference for the path selection of SSCI from the aspects of theory and practice. [ABSTRACT FROM AUTHOR]

Nowadays, supply chain (SC) decentralised decision making is the most usual situation in SC operations planning. In this context, different companies can collaboratively plan to achieve a certain level of individual and SC performance. However in many cases, there is reluctance to collaborate because it is not known a priori which benefits will be reported. This paper aims to develop a mathematical programming-based methodology for the evaluation of different supply chain collaborative planning scenarios (MPM-SC-CP). It is assumed that different SC decision centres (DCs) make decisions based on mixed and integer linear programming models. Two main inputs feed the proposed MPM-SC-CP, a framework and associated methodology that support the integrated conceptual and analytical modeling of the SC-CP process in which several DCs make decisions according to spatio-temporal integration. Finally, an application to a real ceramic SC was conducted. [ABSTRACT FROM AUTHOR]

This paper, employing the multicriteria method of decision support MACBETH, proposes a model for the evaluation of interdisciplinary graduate programs, based on indicators already used in the current model of evaluation of Brazilian graduate studies by Coordination for the Improvement of Higher Education Personnel (CAPES). In the present work, ten Computational Modeling Programs were considered, and six criteria were selected to verify the performance of these programs. There is presently in Brazil a steady growth of requests for accreditation of interdisciplinary programs and a quest to evaluate them differently, given their peculiarities. The main contribution of this study is to propose an alternative methodology for the evaluation of graduate programs, respecting the characteristics of the area of knowledge in which it is inserted, which is able to rank them based on qualitative judgments and to verify how much each course can progress, according to the score established for each program in a set of criteria considered. A comparison was made with the results of the evaluation methodology used by CAPES in the year 2017, being possible to verify some similarities in the evaluations of the graduate programs by the two methods. Finally, a sensitivity analysis was performed. [ABSTRACT FROM AUTHOR]

Existing evaluation methods of green innovation ability have strong subjectivity and can not deal with the problem of information overlapping among the indexes. To overcome these shortcomings, this paper combine analytic hierarchy process (AHP) and osculating value process (OVP) to construct the AHP–OVP evaluation model. Then this model is introduced into the green innovation ability evaluation of manufacturing enterprises, and the validity of the evaluation method is verified by a case. The results show that AHP–OVP evaluation model can effectively evaluate green innovation ability of manufacturing enterprises. And this model also fully solve the problems of green innovation ability evaluation methods of manufacturing enterprises built by previous scholars. [ABSTRACT FROM AUTHOR]

The linkage among customer satisfaction, employee evaluation, and business performance data is very important in modern business organizations. Several previous research efforts have studied this linkage, focusing mainly on the financial or business performance in order to analyze the efficiency of an organization. However, recent studies have tried to consider other important performance indicators, which are able to affect business operations and future growth (e.g., external and internal customer satisfaction). In the case of the banking industry, studying the relations among the aforementioned variables is able to give insight in the performance evaluation of bank branches and the viability analysis of the banking organization. This paper presents a real-world study for measuring the relative efficiency of a set of bank branches using a Data Envelopment Analysis (DEA) approach. In particular, a multistage DEA network model is proposed, using a set of performance indicators that combine customer satisfaction, employee evaluation, and business performance indices. The main aim of the presented study is to evaluate the relative efficiency of each customer service delivery step, in the environment of a bank branch. The results are also able to estimate the contribution of the assessed performance indicators to the branch's overall efficiency, and to determine potential improvement actions. [ABSTRACT FROM AUTHOR]

With the sustainable development of remote sensing satellite observation technology, the effectiveness of the remote sensing satellite observation scheme has drawn wide attention in recent years. A suitable evaluation model can provide a credible improvement basis and guide observation technology's continuous development. In this study, we present an original method based on granular computing to evaluate the remote sensing satellite observation schemes by employing intuitionistic linguistic preference relation, which is ideal for allowing evaluation team members to depict their actual opinions. The granulation of the intuitionistic linguistic preference relation is regarded as an optimization problem, solved by using the particle swarm optimization. The optimization criterion comprised of consensus and consistency is maximized by a suitable mapping of the intuitionistic linguistic preference relation on information granules. Once the granulation of the intuitionistic linguistic preference relation is completed, the solution to the evaluation problem is constructed by the selection process in virtue of the dominance levels of observation schemes and the prioritization relationship of evaluation team members. Finally, an experimental example about the evaluation of remote sensing satellite observation schemes is reported to support the feasibility and practicality of the proposed evaluation method. Furthermore, comparative analysis with other methods is also analyzed to further demonstrate the performance of the designed method. [ABSTRACT FROM AUTHOR]

Data envelopment analysis (DEA) is an important method of efficiency evaluation. Cross-efficiency evaluation is one of the main aspects of research in the field of DEA that has been applied in various fields. In the traditional cross-efficiency evaluation model, the variable data of decision-making units is exact. Dynamic information is frequently unable to reflect the whole characteristic when determining the exact data. In this study, we select interval data to represent the dynamic information of some variables in the evaluation process. We then build a solution method based on interval efficiency and DEA cross-efficiency. This method retains the reflection of interval data on uncertain variable properties. Finally, the stochastic multi-criteria acceptability analysis 2 (SMAA2) is introduced to solve the whole sequence problem of interval efficiency. We present a case study from a set of 19 reservoir dams suffered from Wenchuan earthquake in Luojiang County, Sichuan Province to demonstrate the applicability of the proposed model. [ABSTRACT FROM AUTHOR]