Fuel Consumption Minimization Procedure of Sail-assisted Motor Vessel based on a Systematic Meshing of the Explored Area

International audienc

Multi-Objective Optimization of Voyage Plans for Ships

In this thesis two methods are investigated to solve a multi-objective optimization problem for voyage planning. The first method, grid search, is a brute force search in a three-dimensional graph while the other uses the Lipschitzian algorithm DIRECT to do a continuous search along a nominal route. The grid search method gives a computation time of 7.6 minutes for a route from Gothenburg to New York. This is obtained partly by parallelizing on 10 cores but also implementing core routines efficiently in compiled programming languages. However, the continuous search method with DIRECT is not suitable for a realistic voyage planning problem. It is more due to the nature of the DIRECT algorithm than the implementation details

Development of a semi-empirical ship operational performance model for voyage optimization

Previously held under moratorium from 1st December 2016 until 1st December 2021.Voyage optimization is the endeavour to select the optimum route and optimum speed along the voyage in order to maximise the ship performance in energy efficiency and the reduction of the Green House Gas emission footprint within fixed voyage duration. For achieving these goals, it is essential to develop an easy-to-use and accurate enough ship operational performance prediction model, which is the main aim of this study. A detailed critical review of the literature regarding the prediction of ship’s added resistance in waves and its operational performance modelling has been carried out. The existing research gap has been identified and addressed herein. The empirical added resistance prediction formulae have been improved based on the actual ship operational performance data and developed as a semi-empirical added resistance prediction method, which estimates the speed loss due to added resistance. Together with the calm water resistance model, propulsion efficiency model, main engine Specific Fuel Oil Consumption (SFOC) diagram, correction factor indicating fouling effect on fuel consumption, and actual ship operational performance data, the novel semi-empirical ship operational performance prediction model for oil tanker and container ship have been developed and validated. The easy-to-use and practical semi-empirical model is able to accurately predict main engine fuel consumption rate at varying speeds and wave angle encountered. This has been tested successfully on an oil tanker and a container ship. A GRIDS system has been developed to indicate the combination of potential routes and the corresponding weather forecast along each route between departure port and destination. By integrating the GRIDS system with the proposed semi-empirical ship operational performance prediction model, a weather routing model and a speed optimization model have been developed for voyage optimization. In this study, weather routing is achieved by optimum route selection. Its objectives include minimum passage time and minimum fuel consumption under fixed main engine output. Speed optimization is achieved by evaluating the predicted main engine fuel consumption with different speed combinations along the voyage, while a fixed Estimated Time of Arrival(ETA) is set as the constraint. Finally, the main findings are discussed and conclusions are drawn with some recommendations for future research.Voyage optimization is the endeavour to select the optimum route and optimum speed along the voyage in order to maximise the ship performance in energy efficiency and the reduction of the Green House Gas emission footprint within fixed voyage duration. For achieving these goals, it is essential to develop an easy-to-use and accurate enough ship operational performance prediction model, which is the main aim of this study. A detailed critical review of the literature regarding the prediction of ship’s added resistance in waves and its operational performance modelling has been carried out. The existing research gap has been identified and addressed herein. The empirical added resistance prediction formulae have been improved based on the actual ship operational performance data and developed as a semi-empirical added resistance prediction method, which estimates the speed loss due to added resistance. Together with the calm water resistance model, propulsion efficiency model, main engine Specific Fuel Oil Consumption (SFOC) diagram, correction factor indicating fouling effect on fuel consumption, and actual ship operational performance data, the novel semi-empirical ship operational performance prediction model for oil tanker and container ship have been developed and validated. The easy-to-use and practical semi-empirical model is able to accurately predict main engine fuel consumption rate at varying speeds and wave angle encountered. This has been tested successfully on an oil tanker and a container ship. A GRIDS system has been developed to indicate the combination of potential routes and the corresponding weather forecast along each route between departure port and destination. By integrating the GRIDS system with the proposed semi-empirical ship operational performance prediction model, a weather routing model and a speed optimization model have been developed for voyage optimization. In this study, weather routing is achieved by optimum route selection. Its objectives include minimum passage time and minimum fuel consumption under fixed main engine output. Speed optimization is achieved by evaluating the predicted main engine fuel consumption with different speed combinations along the voyage, while a fixed Estimated Time of Arrival(ETA) is set as the constraint. Finally, the main findings are discussed and conclusions are drawn with some recommendations for future research

Multi-Criteria Decision Making in Complex Decision Environments

In the future, many decisions will either be fully automated or supported by autonomous system. Consequently, it is of high importance that we understand how to integrate human preferences correctly. This dissertation dives into the research field of multi-criteria decision making and investigates the satellite image acquisition scheduling problem and the unmanned aerial vehicle routing problem to further the research on a priori preference integration frameworks. The work will aid in the transition towards autonomous decision making in complex decision environments. A discussion on the future of pairwise and setwise preference articulation methods is also undertaken. "Simply put, a direct consequence of the improved decision-making methods is,that bad decisions more clearly will stand out as what they are - bad decisions.

Ant colony optimization based simulation of 3d automatic hose/pipe routing

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This thesis focuses on applying one of the rapidly growing non-deterministic optimization algorithms, the ant colony algorithm, for simulating automatic hose/pipe routing with several conflicting objectives. Within the thesis, methods have been developed and applied to single objective hose routing, multi-objective hose routing and multi-hose routing. The use of simulation and optimization in engineering design has been widely applied in all fields of engineering as the computational capabilities of computers has increased and improved. As a result of this, the application of non-deterministic optimization techniques such as genetic algorithms, simulated annealing algorithms, ant colony algorithms, etc. has increased dramatically resulting in vast improvements in the design process. Initially, two versions of ant colony algorithms have been developed based on, respectively, a random network and a grid network for a single objective (minimizing the length of the hoses) and avoiding obstacles in the CAD model. While applying ant colony algorithms for the simulation of hose routing, two modifications have been proposed for reducing the size of the search space and avoiding the stagnation problem. Hose routing problems often consist of several conflicting or trade-off objectives. In classical approaches, in many cases, multiple objectives are aggregated into one single objective function and optimization is then treated as a single-objective optimization problem. In this thesis two versions of ant colony algorithms are presented for multihose routing with two conflicting objectives: minimizing the total length of the hoses and maximizing the total shared length (bundle length). In this case the two objectives are aggregated into a single objective. The current state-of-the-art approach for handling multi-objective design problems is to employ the concept of Pareto optimality. Within this thesis a new Pareto-based general purpose ant colony algorithm (PSACO) is proposed and applied to a multi-objective hose routing problem that consists of the following objectives: total length of the hoses between the start and the end locations, number of bends, and angles of bends. The proposed method is capable of handling any number of objectives and uses a single pheromone matrix for all the objectives. The domination concept is used for updating the pheromone matrix. Among the currently available multi-objective ant colony optimization (MOACO) algorithms, P-ACO generates very good solutions in the central part of the Pareto front and hence the proposed algorithm is compared with P-ACO. A new term is added to the random proportional rule of both of the algorithms (PSACO and P-ACO) to attract ants towards edges that make angles close to the pre-specified angles of bends. A refinement algorithm is also suggested for searching an acceptable solution after the completion of searching the entire search space. For all of the simulations, the STL format (tessellated format) for the obstacles is used in the algorithm instead of the original shapes of the obstacles. This STL format is passed to the C++ library RAPID for collision detection. As a result of using this format, the algorithms can handle freeform obstacles and the algorithms are not restricted to a particular software package

A Hybrid MCDM Approach to Transshipment Port Selection

Port selection is an intrinsic supply-chain problem that has substantial impact on development of local economies. Shipping business environment developed into complex system where decision making is derived from uncertain and incomplete data. In this study we present a conceptual integrated Multi-Criteria Decision solution to transshipment port selection problem based on Best-Worst MCDM and Artificial Bee Colony Algorithm. Through literature review and expert analysis, 50 relevant criteria have been identified as relevant to the transshipment port selection problem. Decision makers within liner shipping companies evaluate transshipment port selection criteria and establish ranking that is used to determine crisp solution with lowest consistency ratio. ABC based algorithm is used to reduce computational complexity and deliver a single optimal solution by solving both objective and constraint violation functions