Container ports play an important role in international maritime trade. However, the rapid growth of the port and terminal industry has caused many environmental pollution problems. This paper intends to develop an inverse data envelopment analysis (IDEA) model for measuring container ports’ efficiency and analyzing their resource consumption by considering undesirable outputs. Statistical data from 16 main ports are empirically examined using the proposed method in accordance with the 13th Five-Year Plan in China. The results indicate that the proposed IDEA is a feasible approach for performance evaluation, and provides policymakers with insights into resource optimization of container ports. A comparative study with another DEA model is also discussed.

Group decision making typically requires eliciting and coordinating the preferences of group members. In this study, the authors develop an overall framework of group consensus in context of hesitant fuzzy linguistic preference relations (HFLPRs). A new notion of distance measure between two HFLPRs is defined, based on which a consensual framework consists of two mechanisms, the automatic consensus reaching and the interactive consensus reaching is presented. Accordingly, they, respectively, design two algorithms to implement these mechanisms. Finally, a group decision problem of green supplier selection is provided to exemplify and verify both of the proposed methods, and a comparative analysis with some related approaches is performed.

This article proposes a new data envelopment analysis (DEA)-based approach to deal with mergers and acquisitions (M&As) matching. To derive reliable matching degrees between bidder and target firms, we consider both technical efficiency and scale efficiency. Specifically, an inverse DEA model is developed for measuring the technical efficiency, while a conventional DEA model is employed to identify the return of scale of the merged decision-making units (DMUs). Then, an optimization model is formulated to generate matching results to improve DMUs’ performance. An empirical study of M&As matching Turkish energy firms is examined to illustrate the proposed approach. This study shows that both technical efficiency and scale efficiency have impacts on M&As matching practices.