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Northern Norwegian coastline and the Vardø VTS (shown with its call signal NOR VTS). Solid line is the geographical baseline; stapled line is the border of the Norwegian Territorial Waters (NTW); thick pink line is the traffic corridor for the Traffic Separation Scheme (TSS). Adapted from Vardø VTS (2011).
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A fleet of tugs along the northern Norwegian coast must be dynamically positioned to minimise the risk of oil tanker drifting accidents. We have previously presented a receding horizon genetic algorithm (RHGA) for solving this tug fleet optimisation (TFO) problem. Here, we first present an overview of the TFO problem, the basics of the RHGA, and a...
Contexts in source publication
Context 1
... Vardø VTS is located at the northeasternmost point of Norway and is run by the Norwegian Coastal Administration (NCA) (see Figure 1). Among other duties, the VTS constantly monitors ship movements, maintains dialogue with ships, and manages the tug fleet of Norway. ...
Context 2
... tankers are required by law to follow a pre- defined corridor, or lane, parallel to the coastline, de- picted as the pink TSS in Figure 1. In topological space, the corridor constitutes a curve, which is loc- ally homeomorphic to a straight line. ...
Context 3
... the inside of the corridor, a fleet of tugs patrol the coastal waters. Ignoring a rugged coastline with islands, peninsulas and shoals, and by the same topo- logical argument above, we may assume that N p tugs are patrolling along a line of motion y parallel to z, e.g., the geographical baseline depicted in Figure 1. We do appreciate, however, that this approximation is only locally correct, since the curvature of y and z will make the outermost line longer than the innermost line. ...
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... Discussions on the design or composition of a fleet of ships are rare in the literature (Bye and Schaathun, 2015, Chen et al., 2011, Fagerholt, 1999, Perakis, 2013, Zeng and Yang, 2007. Those available seem to focus on ships with a single purpose or cargo, in a network of ports or even a single route. ...
His research is currently focussed on shipping and shipyard management with a special focus on dealing with sustainability and the uncertainty the energy transition creates. He played a leading role in the Design 4 Operations (Des4Ops) project that lead to this publication. Tijmen Veldstra is a former researcher at Delft University of Technology in Delft, The Netherlands. Currently he is a Data Analyst at Portbase where he is optimizing the logistic chains in the Netherlands in order to make the Dutch ports as attractive as possible. Within the Des4Ops project, his primary task was the development of the Des4Ops model. Bart van Oers has a PhD in Ship Design (2011) and a MSc. in Ship Hydrodynamics (2005) from Delft University of Technology. He works for the Maritime Systems Division of the Netherlands Defence Materiel Organisation since 2009. Bart co-chaired the NATO Research Task Group AVT-RTG-238, and was involved in the Maritime Countermeasures Project at the European Defence Agency. As a team leader, he currently is responsible for conducting concept studies for future warships for the Royal Netherlands Navy. Richard Logtmeijer holds the current position of Senior Staff Member Life Cycle Modelling at the Defence Materiel Organisation (DMO) of the NL Ministry of Defence. He is responsible for analysing through modelling and simulation the life cycle effectiveness and cost of new warship concept designs. He is also chairman of the NATO specialist team on total ship systems engineering (ST-TSSE). His previous experience includes three years serving as a naval officer and over twenty years working for the Royal Netherland Navy. Sander Knegt is a Naval Architect at Damen Schelde Naval Shipbuilding in Vlissingen, The Netherlands. Here he designs naval ships for both the Royal Netherlands Navy as well as for the export market. For his master thesis research at Delft University of Technology he made a model that simulates naval fleet behavior for the purpose of optimizing fleet design. The experience gained from this research contributed to the Des4Ops project. Willem-Jan van Driel is Naval Architect at Huisman Equipment. His focus is on developing and designing innovative offshore floaters in the oil & gas and offshore wind industry. He participated in the Des4Ops project as advisor and user of the model. Synopsis Before designing a ship, the requirements for the ship are required. For innovative or complex technical ships, this is not instantly clear. In many cases, the expected operations of the ship need to be investigated first. Currently, this is often done on a ship-by-ship basis. However, a ship seldom operates alone, especially for technically complex ships. The ship will often be part of a fleet executing e.g. complex operations such as windmill installation or complex missions in case of navy ships. It is therefore important to investigate the role of the ship in this fleet and establish the contributions to the expected missions or tasks. Of course, the fleet will change over time and each stepwise change of the fleet could be investigated independently but should be investigated in cohesion. To enable this, an approach and validation model was developed within the DES4Ops project over the past two years. This paper will discuss the development of an optimal fleet design model to support decision making and requirement elucidation. The impact of existing ships on the choice for a new ship (brownfield development) and of a complete fleet redesign (greenfield development) are supported. A simple example case for an arbitrary set of navy missions will be discussed to highlight the potential of this approach. In the end, we conclude that the model allows the user to check the performance of a fleet in different scenario's, but more importantly, it will lead to fleet considerations that lead to better performances overall and over time.
... The GA is generally attributed to Holland (1975) [15], with subsequent popularisation by Goldberg (1989) [8], and is still a very popular optimisation tool across many different disciplines. In our Cyber-Physical Systems Laboratory (CPS Lab), 2 we have many years of experience utilizing the GA for a variety of purposes, including adaptive locomotion of a caterpillar-like robot [18], autonomous ships and dynamic positioning of tug vessels along the Norwegian coast (e.g., [6,7,3]), and as a core part of a generic software framework for intelligent computer-automated design (CautoD) [4], which was successfully applied for optimized CautoD, or virtual prototyping, of offshore cranes and winches (e.g., [13,14,5]. ...
The traveling salesman problem is a very popular combinatorial optimization problem in fields such as computer science, operations research, mathematics and optimization theory. Given a list of cities and the distances between any city to another, the objective of the problem is to find the optimal permutation (tour) in the sense of minimum traveled distance when visiting each city only once before returning to the starting city. Because many real-world problems can be modeled to fit this formulation, the traveling salesman problem has applications in challenges related to planning, routing, scheduling, manufacturing, logistics, and other domains. Moreover, the traveling salesman problem serves as a benchmark problem for optimization methods and algorithms, including the genetic algorithm. In this paper, we examine various implementations of the genetic algorithm for solving two examples of the traveling salesman problem. Specifically, we compare commonly employed methods of partially mapped crossover and order crossover with an alternative encoding scheme that allows for single-point, multipoint, and uniform crossovers. In addition, we examine several mutation methods, including Twors mutation, center inverse mutation, reverse sequence mutation, and partial shuffle mutation. We empirically compare the implementations in terms of the chosen crossover and mutation methods to solve two benchmark variations of the traveling salesperson problem. The experimental results show that the genetic algorithm with order crossover and the center inverse mutation method provides the best solution for the two test cases.
... Previous works [5]- [9] consider a one-dimension modeling approach and focus on the minimization of the distances between potentially drifting vessels and the nearest tugboat by means of genetic algorithms and a mixed integer programming (MIP). Their model and algorithms allocate tugboats to oil tankers, but do not give information on the probability of successful hook-up. ...
... Razi and Karatas (2016) on the other hand develop a tactical model for determining the optimal placement of SAR boats, however, their model do not account for uncertainty related to vessel incidents and the dynamic nature of the SAR resources positioning, which are the primary concern of the OTP problem. To determine the optimal positions of tugboats in real time, researchers have developed methods both including genetic algorithms (Bye, 2012;Bye & Schaathun, 2014;Bye & Schaathun, 2015a, 2015b) and an MIP model (Assimizele, Oppen, & Bye, 2013). Their algorithms and models assume oil tankers move along piecewise-linear corridors and approximately parallel to the coastline. ...
An important task of operators in Norwegian vessel traffic services (VTS) centres is to cleverly position tugboats before potential vessel distress calls. Here, we formulate a non-linear binary-integer program, integrated in a receding horizon control algorithm that minimises the expected cost of grounding accidents by positioning tugboats optimally under uncertainty about vessel incidents and environmental conditions. Linearisations of the model lead to easy-to-compute bounds on the optimal value. Numerical experiments with real-world data demonstrate significant reduction in the expected cost, suggesting that the model can be used as a decision-support tool at VTS centres.
... The number of oil tanker transits is expected to rise significantly in coming years [19], therefore, the problem of commanding tugs to suitable positions may become unmanageable for human operators. Motivated by this challenge, researchers at the Software and Intelligent Control Engineering (SoftICE) Laboratory at the Aalesund University College (AAUC) have over the last few years developed, refined, and studied a receding horizon genetic algorithm (RHGA) [6][7][8][9] through the research project Dynamic Corresponding author. This paper is an extended and revised version of a paper presented at the 4th International Conference on Operations Research and Enterprise Systems (ICORES '15) in Lisbon, Portugal, January 2015 [8]. ...
... Motivated by this challenge, researchers at the Software and Intelligent Control Engineering (SoftICE) Laboratory at the Aalesund University College (AAUC) have over the last few years developed, refined, and studied a receding horizon genetic algorithm (RHGA) [6][7][8][9] through the research project Dynamic Corresponding author. This paper is an extended and revised version of a paper presented at the 4th International Conference on Operations Research and Enterprise Systems (ICORES '15) in Lisbon, Portugal, January 2015 [8]. ...
... Whilst the RHGA was implemented in MATLAB in earlier versions [6,9], we recently rewrote the entire code base in the advanced, purely-functional programming language Haskell 2 [7,8]. ...
Tug fleet optimisation algorithms can be designed to solve the problem of dynamically positioning a fleet of tugs in order to mitigate the risk of oil tanker drifting accidents. In this paper, we define the tug fleet optimisation problem and present the details of a receding horizon genetic algorithm for solving it. The algorithm can be configured with a set of cost functions such that each configur- ation effectively constitute a unique tug fleet optimisation algorithm. To measure the performance, or merit, of such algorithms, we propose two evaluation heur- istics and test them by means of a computational simulation study. Finally, we discuss our findings and some of our related work on parallelisation and a 2D nonlinear mixed integer programming formulation of the problem.
For many years, NTNU in Ålesund (formerly Aalesund University College) has maintained a close relationship with the maritime industrial cluster, centred in the surrounding geographical region, thus acting as a hub for both education, research, and innovation. Of many common relevant research topics, virtual prototyping is currently one of the most important. In this paper, we describe our first complete version of a generic and modular software framework for intelligent computer-automated product design. We present our framework in the context of design of offshore cranes, with easy extensions to other products, be it maritime or not. Funded by the Research Council of Norway and its Programme for Regional R&D and Innovation (VRI), the work we present has been part of two separate but related research projects (grant nos. 241238 and 249171) in close cooperation with two local maritime industrial partners.
We have implemented several software modules that together constitute the framework, of which the most important are a server-side crane prototyping tool (CPT), a client-side web graphical user interface (GUI), and a client-side artificial intelligence for product optimisation (AIPO) module that uses a genetic algorithm (GA) library for optimising design parameters to achieve a crane design with desired performance. Communication between clients and server is achieved by means of the HTTP and WebSocket protocols and JSON as the data format.
To demonstrate the feasibility of the fully functioning complete system, we present a case study where our computer-automated design was able to improve the exist- ing design of a real and delivered 50-tonnes, 2.9 million EUR knuckle boom crane with respect to some chosen desired design criteria.
Our framework being generic and modular, both client- side and server-side modules can easily be extended or replaced. We demonstrate the feasibility of this concept in an accompanying paper submitted concurrently, in which we create a simple product optimisation client in Matlab that uses readily available toolboxes to connect to the CPT and optimise various crane designs by means of a GA. In addition, our research team is currently developing a winch prototyping tool to which our existing AIPO module can connect and optimise winch designs with only small configuration changes. This work will be published in the near future.
In close collaboration with the maritime industry, virtual prototyping with maritime application has been an important research topic for Aalesund University College for some years. In this paper, we describe the development of a computer-automated design tool for intelligent virtual prototyping of offshore cranes. Our work is part of a research project funded by the Research Council of Norway and takes place in close cooperation with two partners from the maritime industry. A literature review of virtual prototyping, computer-automated design, and modelling and simulation of offshore cranes sets the stage for the description of a design tool whose main components consist of a computational model, a simulator, and a genetic algorithm. We show how domain-specific constraints can be accounted for in conjunction with an automated optimisation procedure of design parameters to yield crane specifications that closely match the desired design criteria. Limitations of slewing rings and hydraulic cylinders are of particular importance in offshore crane design and are used as an example of the multitude of design calculations that form the computational model. Being work in progress, we report on completed parts and the work that remains. © ECMS Valeri M. Mladenov, Petia Georgieva, Grisha Spasov, Galidiya Petrova (Editors).
