A Fuzzy-Based Decision-Making Model for Improving the Carrying Capacity of Ship Locks: A Three Gorges Dam Case

Abstract

This paper develops a decision-making model to assist the improvement of the carrying capacity of ship locks by combing fuzzy logic, the analytic hierarchy process (AHP) method, and the technique for order preference by similarity to an ideal solution (TOPSIS). A three-level hierarchical structure is constructed to identify the key factors influencing the carrying capacity of ship locks from the aspects of ship locks, vessels, environment, and administration. On this basis, a series of targeted strategies have been put forward to improve the carrying capacity of ship locks, and the TOPSIS method is applied to rank these strategies in terms of their performance. A case study of the five-stage dual-track ship lock of the Three Gorges Dam in China has been conducted to demonstrate the feasibility and rationality of the proposed model, and correlation analysis is conducted to verify the identified influencing factors in order to eliminate potential bias which may be generated from using AHP. The results obtained from the proposed methods are consistent with the real-life situation to a certain extent, indicating that the proposed method can provide a useful reference for improving the carrying capacity of ship locks.

Research and Application of Critical Failure Paths Identification Method for Dam Risk Analysis

“…Analytic hierarchy process (AHP) [1,2] is one of the most widely adopted methods for quantitative analysis of qualitative problems. Because of its simplicity and effectiveness, AHP is widely used in many complex decisionmaking fields [3][4][5][6], including the risk analysis on dams. Choi et al [7] studied the quantitative evaluation index and evaluation method of dam safety restoration using the AHP method.…”

Quantitative Assessment on Navigation Impact of the Controlled Waterway in the Upper Yangtze River Using Queuing Model

“…Several scholars have proven that queuing models are an effective tool for analyzing traffic congestion problems (6,(19)(20)(21) and channel carrying capacity (22,23). In previous studies of traffic problems, ship or vehicle arrivals obeyed Poisson distributions (24)(25)(26)(27), and service times mostly obeyed exponential distributions (28,29), which, being the assumptions of the M/M/1 model, resulted in its frequent use.…”