Interval-Valued Fuzzy Multi-Criteria Decision-Making with Dependent Evaluation Criteria for Evaluating Service Performance of International Container Ports

Abstract

Due to COVID-19 barriers, the needs of international container ports have become more important than in the past. Therefore, it is very critical and essential for the scientific developments of port-logistics. To gain the scientific developments of port logistics, effective and efficient evaluation methods for decision-making are indispensable, especially for assessing service performance of international container ports based on dependent evaluation criteria (DEC). Among numerous decision-making methods, technique for order preference by similarity to ideal solution (TOPSIS) was often expanded under fuzzy environments into fuzzy multi-criteria decision-making (FMCDM) to preserve imprecise messages. The FMCDM was able to be associated with quality function deployment (QFD) into a hybrid method to solve problems with DEC. To gain more messages, QFD and TOPSIS are combined and then expanded under interval-valued fuzzy environment (IVFE) to solve a FMCDM problem with DEC. Practically, evaluating service performance of international container ports in Taiwan and the surrounding sea areas is considered a problem with DEC because the related evaluation criteria are partially connected. By the hybrid method of combining QFD with TOPSIS under IVFE, international container ports with DEC are effectively and efficiently evaluated for service performance, and more insights are gained than the past for establishing essential fundamentals in recent scientific developments of port logistics on account of breaking down COVID-19 barriers.

Research on the Beibu Gulf Port Container Terminal Operation System Construction Performance Evaluation Based on the AISM-ANP

“…Based on [29,30], port operation indicators in the design of the ease of operation, the business realization degree, and other indicators are adopted. Based on [9], the service performance evaluation system and the service efficiency evaluation module are added as management indexes. Integrating the above literature and expert deliberations, this paper divides the management capability indicators into three secondary indicators, i.e., service capability, green security, and system flexibility.…”

“…Its column vector is the CTOS construction performance evaluation index P in i weight vector W . See Equation (9).…”

“…It is difficult to obtain the development status of the whole automated port through a set of index systems covering all information systems. How to scientifically measure and evaluate the scientific developments of port logistics is an urgent problem that needs to be solved in the future smart port development process [9]. Therefore, providing value-added information services and analytics for information systems is increasingly important to maintain a competitive edge and to fulfill regulatory requirements [8].…”

“…Lin used a combination of Bouda counting and fuzzy hierarchical analysis to analyze and compare the variability of indicators in smart port development and studied the reasons for the variability of indicators [14]. Wang relied on DEC evaluation criteria and used a fuzzy multicriteria decision analysis scheme combined with the technique for order of preference by similarity to the ideal solution (TOPSIS) to effectively evaluate the performance of port services [9]. The literature above shows that most of the latest port evaluation articles have adopted a combination of subjective and objective indicator evaluation schemes to derive valid evaluation results, and this paper also draws on such a combination of subjective and objective evaluation schemes.…”

Integrating Structure Time Series Forecasting and Multicriteria Decision Analysis for Adaptive Operational Risk Assessment: An Empirical Study Using Real-Time Data

“…In addition to MCDA methods that inherently address uncertainty like SMAA, ER, and ELECTRE, MCDA approaches that address uncertainty by integrating external frameworks and methodologies are also commonly seen in academic research and industrial applications. Examples include Bayesian network-based MCDA for operational risk management [16,17], interval-valued fuzzy MCDA [18,19], and Dempster-Shafer theory-based MCDA [20,21]. These methodologies implement MCDA with uncertainty in a wide range of contexts, from disaster risk reduction to supply chain management.…”