Repair equipment allocation problem for a support-and-repair ship on a deep sea: A hybrid multi-criteria decision making and optimization approach

Zhao, Ruijia; Xie, Xuhui; Yu, W.H.

doi:10.1016/j.eswa.2020.113658

Cited by 12 publications

(4 citation statements)

References 51 publications

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“…Relatively few publications are available related to the multi-objective optimization of the ship's operation and maintenance problems. For instance, a hybrid multi-criteria decision making and optimization approach to the issue of support-and-repair ship allocation on a deep-sea route is presented in [23]. This approach was based on an aggregation of evaluation information of quantitative criteria (i.e.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The analysis carried out here of the issues related to the application of multi-objective optimization methods to the various problems that arise in the operation and design of ships shows that: − this concerns a wide range of maritime industry areas, for instance: optimizing the ship's routes and schedules [1]- [6], design of the ship's arrangement [7], [12]- [17], hull and propeller shape [8]- [12], optimizing the ship's hull and propeller cooperation [18]- [22], and optimizing the maintenance process [23], [24], − these methods are based on analytical models, for example [1], [17], [25] or numerical models, for example [12], [19], [22], and in the majority, they are parametric studies, where data were obtained from the ship's historical logs or specifications of the existing ships, − these methods used various optimization algorithms, for example: exact algorithms in [1], [17], [25], approximation algorithms in [9], and metaheuristic algorithms (evolutionary algorithm [18]; genetic algorithm [13], [14] [15]; particle swarm optimization [16], [20]). Unfortunately, from the perspective of the problem discussed in this paper, i.e., supporting the selection of the commanded outputs of the ship's propulsion system, the literature review performed found no methods based on data collected during the planned sea trials regarding ship fuel consumption and speed prediction.…”

Section: Literature Reviewmentioning

confidence: 99%

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Optimization Model to Manage Ship Fuel Consumption and Navigation Time

Rudzki

Gomulka

Hoang

2022

Polish Maritime Research

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Owners of vessels are interested in the lowest possible operating costs. These costs are mainly related to fuel consumption during navigation. To manage it rationally, the main decision-making problem is selecting the proper parameters of the ship’s propulsion system during navigation. In practice, operators of ships equipped with controllable pitch propellers controlled in manual mode make a selection of the commanded outputs based on their own knowledge, intuition, and all accessible information regarding sea conditions. In many cases, their decisions are unreasonable or incorrect. Therefore, it would be desirable to support their decision-making in selecting the commanded outputs. For this reason, we have decided to develop a decision support system in the form of an expert system. This computer-aided system supports the selection of the commanded outputs of the ship’s propulsion system. The most important component of this system is the two-criteria optimization model, allowing the rational management of the ship fuel consumption and navigation time.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Optimization Model to Manage Ship Fuel Consumption and Navigation Time

Rudzki

Gomulka

Hoang

2022

Polish Maritime Research

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“…In recent years, with the development of reliability theories, people have realized that "absolutely reliable" machines are impossible to be produced and do not exist [2]. More and more scholars have shifted their interest in scientific research to the academic research of product observation, evaluation, diagnosis and prediction [3,4].…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of High-Speed Brushless Permanent-Magnet DC Motor Based on Support Vector Machine Optimized by Modified Grey Wolf Optimization Algorithm

Liu

Zhao³

et al. 2021

Symmetry

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With the development of reliability theory, people realized that “absolutely reliable” machines could not be made. With its incomparable advantages, the high-speed permanent-magnet brushless DC motor is usually used in the symmetrical structure of high-speed operation working systems, which at present are widely used in aerospace and other fields. The structure of the manufacturing process involves a strict processing, but in the process of work failure could still occur. No matter what field the high-speed permanent magnet brushless DC motor is applied to, it is very important to identify states and run fault diagnosis, which is of great significance to maintain the reliability of the motor and its working system. In this study, the fault diagnosis method of a high-speed permanent-magnet brushless DC motor is studied, and a combination model of modified gray wolf optimization algorithm (MGWO) and support vector machine (SVM) have been proposed for the motor fault diagnosis research. Based on the traditional gray wolf optimization (GWO) algorithm, the optimization performance of the algorithm is improved by initializing the population through a tent map and introducing a sine wave dynamic adaptive factor. Then the modified algorithm is used to optimize the internal parameters of SVM to improve the diagnostic accuracy of the model. Through the signal acquisition test, the current signals under different fault states and faultless states were collected, and the current signal data set required for the experiment is obtained. The experimental result showed that, compared with GWO or sailfish optimization (SFO) optimized SVM models, Extreme learning machine and Back Propagation neural network classical classification models, the fault diagnosis accuracy of the proposed model is the highest, proving the excellent classification performance and good robustness of the MGWO-SVM model.

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“…The authors mainly focused on the removal strategy to avoid infeasible solutions 21 . A robust multi‐objective multi‐humanoid robots task allocation (MO‐MHTA) algorithm proposed solving task allocation, multi‐objective, and energy consumption for humanoid‐based robots 22 …”

Section: Introductionmentioning

confidence: 99%

A novel fuzzy and reverse auction‐based algorithm for task allocation with optimal path cost in multi‐robot systems

Baskaran

et al. 2021

Concurrency and Computation

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One of the most frequent issues in multiple robot implementation is task allocation with the lowest path cost. Our study addresses the multi‐robot task allocation challenge with path costs, the lowest computing time, and task distribution. Furthermore, it is usual for a robot's processing capabilities to be restricted to operate in various target environments. As a consequence, adequate processing power consumption would demonstrate the system's efficiency. Task allocation and path planning issues must be addressed regularly to ensure multi‐robot system operation. Task allocation and path planning issues must be addressed regularly to ensure multi‐robot system operation. The above‐mentioned serious challenge gets more complicated when system factors such as robots and tasks multiply. As previously stated, this article solves the issue using a fuzzy‐based optimum path and reverse auction‐based methods. The detailed simulation results indicate that the suggested methods can solve task allocation with the lowest path cost. A comparative study is conducted between the suggested algorithm and two existing commonly used techniques, the auction‐based and the Hungarian algorithms. Finally, the suggested method was run in real‐time on a TurtleBot2 robot. The findings show the suggested algorithm's efficiency and simplicity of implementation.

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Repair equipment allocation problem for a support-and-repair ship on a deep sea: A hybrid multi-criteria decision making and optimization approach

Cited by 12 publications

References 51 publications

Optimization Model to Manage Ship Fuel Consumption and Navigation Time

Optimization Model to Manage Ship Fuel Consumption and Navigation Time

Fault Diagnosis of High-Speed Brushless Permanent-Magnet DC Motor Based on Support Vector Machine Optimized by Modified Grey Wolf Optimization Algorithm

A novel fuzzy and reverse auction‐based algorithm for task allocation with optimal path cost in multi‐robot systems

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