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Based on commercial passenger-carrying airships like LZ129 or R100, a hypothetical electric rigid framed airship including a solar cell covered surface and a lithium-ion battery is designed. The size of the battery and the coverage with solar cells are selected such that long-haul flights are possible. To simulate flight times, weather data from 2019 and time-dependent solar irradiation are used. Travel route and battery use are optimised in order to reduce flight times. For a mid-range and long-haul use case for passenger or freight transport, travel times have been calculated. Building on these results, analysis of CO2 emissions, land-use, and operating costs are carried out to reveal that depending on the use case, CO2 emissions of solar-powered airships could be as low as 1% to 5% of the emissions of a conventional aircraft at an estimated energy consumption in USD per km of 0.5% to 2.5%.

Based on commercial passenger-carrying airships like LZ129 or R100, a hypothetical electric rigid framed airship including a solar cell covered surface and a lithium-ion battery is designed. The size of the battery and the coverage with solar cells are selected such that long-haul flights are possible. To simulate flight times, weather data from 2019 and time-dependent solar irradiation are used. Travel route and battery use are optimised in order to reduce flight times. For a mid-range and long-haul use case for passenger or freight transport, travel times have been calculated. Building on these results, analysis of CO2 emissions, land-use, and operating costs are carried out to reveal that depending on the use case, CO2 emissions of solar-powered airships could be as low as 1% to 5% of the emissions of a conventional aircraft at an estimated energy consumption in USD per km of 0.5% to 2.5%. [ABSTRACT FROM AUTHOR]

The optimal design of a suitable material supply process is important for the continuous, smooth operation of manufacturing processes, which includes the transportation and handling of work pieces, components, tools, equipment, pallets, packaging materials, and measuring instruments. The most common form of implementation of these material handling processes, especially in mass production, is the milkrun supply. In this paper, the author presents a method to optimise the milkrun material supply processes in manufacturing. The author gives a brief overview of research results related to milkrun-based material supply solutions. An Excel Solver-based optimization method is presented that is suitable to support the design of an optimal material supply process in any manufacturing environment by determining optimal material supply routes and optimal number of milkrun trolleys, depending on the supply demand, location of warehouse and manufacturing cells and the capacity of the milkrun trolleys. [ABSTRACT FROM AUTHOR]

Transportation enterprises have started to pay more attention to the vast amount of historical operational data. In this paper, real route information is extracted from the freight car trajectory data and a satisfied route is proposed based on this information. The satisfied route not only can reduce the travel cost but also conform to the actual transportation situation. A solution framework driven by trajectory data is developed to obtain the satisfied route. A case study associated with the Chinese railway is used to verify the model and the solution framework proposed in this paper. [ABSTRACT FROM AUTHOR]

The travelling salesman problem (TSP) is perhaps the most researched problem in the field of Computer Science and Operations. It is a known NP-hard problem and has significant practical applications in a variety of areas, such as logistics, planning, and scheduling. Route optimisation not only improves the overall profitability of a logistic centre but also reduces greenhouse gas emissions by minimising the distance travelled. In this article, we propose a simple and improved heuristic algorithm named 2-Opt++, which solves symmetric TSP problems using an enhanced 2-Opt local search technique, to generate better results. As with 2-Opt, our proposed method can also be applied to the Vehicle Routing Problem (VRP), with minor modifications. We have compared our technique with six existing algorithms, namely ruin and recreate, nearest neighbour, genetic algorithm, simulated annealing, Tabu search, and ant colony optimisation. Furthermore, to allow for the complexity of larger TSP instances, we have used a graph compression/candidate list technique that helps in reducing the computational complexity and time. The comprehensive empirical evaluation carried out for this research work shows the efficacy of the 2-Opt++ algorithm as it outperforms the other well-known algorithms in terms of the error margin, execution time, and time of convergence. [ABSTRACT FROM AUTHOR]

This study develops a cost model covering monetary and environmental damage costs for source-separated biowaste collection. The model provides an improved basis for decision-making by including environmental damage costs compared to the assessment that considers the only monetary cost. The monetary cost calculation integrated route optimisation using existing road networks, while the environmental damage cost was estimated using the life cycle impact assessment method based on the endpoint (LIME) model. The model was tested in the Finnish case where the new law implements the stricter requirement for source-separated biowaste. The costs of collection, transportation and treatment of three different scenarios were assessed: mixed waste under the old law (MW-OL), biowaste under the new law (B-NL) and mixed waste without biowaste under the new law (MW-NL). The results showed the economic and environmental benefits of sourced separated biowaste. The overall cost of collection and transportation (CT) under the old law and new laws were 80.7 € Mg−1 and 81.1 € Mg−1, respectively. Treatment costs were 79 € Mg−1 and 64.8 € Mg−1 under the old and new laws, respectively. The damage costs for CT under the old and new laws were 0.23 € Mg−1 and 0.24 € Mg−1, respectively. At the same time, the damage costs from the treatment stage were 4.9 € Mg−1 and 3.5 € Mg−1 under the old law and new law, respectively. The model supports decision-making when the collection scheme requires a change. Failing to plan an optimised solution and cost will lead to inefficient systems. [ABSTRACT FROM AUTHOR]

This paper describes the architecture of a weather routing system consisting of two key elements: onboard monitoring and route optimiser sub-systems. The former is responsible for collecting various onboard measurements, such as current ship position or ship motion variables. These data, when gathered and processed, are then used for fine-tuning a ship model. The model, together with weather forecasts, is utilised by a multi-objective route optimiser to estimate forecasted ship responses during the voyage. The route optimiser has been developed in a client-server architecture to reallocate all necessary high-tech resources to the server side and keep the client software as simple and light as possible. The system also includes a module responsible for optimising transmission costs, to reduce onboard transmission during the voyage. The entire solution has been deployed onboard the demonstrator ship ‘Monte da Guia’ and tested during its operations at sea.

• A novel method of municipal solid waste (MSW) collection route optimization. • Population-dependent waste generation calculation method using image processing. • The concept of linear MSW density: waste generation divided by road length. • Accurate modelling of the transport road length covered using linear MSW density. • Efficient novel optimization programming approach with less processing requirements. The city of Budapest produces approximately 680–700 000 tonnes of municipal solid waste every year, of which <2/3 is being recycled or used for energetic purposes, the rest ends up in a landfill. To combat this environmental and logistical problem the installation of a second waste incineration plant has been proposed in the south of the city. The only cost associated with fuel consumption in the case of waste to energy powerplants (WtE plants) is the transport cost, as the city council provides economic support for waste disposal. Since the transportation has a huge influence on the cost of opertation, logistical optimisation of the transport routes promises a direct impact on cost savings. In this study the logistical optimisation of the Southern Budapest area was carried out using image processing and logic based algorithm programming. As a result the optimal transport of 230 000 tonnes of municipal solid waste (MSW) was solved resulting in a 4 835.2 km monthly travel distance reduction.This value can be translated to 7 823 €/month cost, 9 459.6 kg/month CO 2 and 45.3 kg/month NO x emissions reduction in the urban areas. [ABSTRACT FROM AUTHOR]

The travelling salesman problem (TSP) is perhaps the most researched problem in the field of Computer Science and Operations. It is a known NP-hard problem and has significant practical applications in a variety of areas, such as logistics, planning, and scheduling. Route optimisation not only improves the overall profitability of a logistic centre but also reduces greenhouse gas emissions by minimising the distance travelled. In this article, we propose a simple and improved heuristic algorithm named 2-Opt++, which solves symmetric TSP problems using an enhanced 2-Opt local search technique, to generate better results. As with 2-Opt, our proposed method can also be applied to the Vehicle Routing Problem (VRP), with minor modifications. We have compared our technique with six existing algorithms, namely ruin and recreate, nearest neighbour, genetic algorithm, simulated annealing, Tabu search, and ant colony optimisation. Furthermore, to allow for the complexity of larger TSP instances, we have used a graph compression/candidate list technique that helps in reducing the computational complexity and time. The comprehensive empirical evaluation carried out for this research work shows the efficacy of the 2-Opt++ algorithm as it outperforms the other well-known algorithms in terms of the error margin, execution time, and time of convergence.

Mobility as a Service (MaaS) is a term coined for the development and implementation of multi-modal trip planner recommendation systems. Multi-modal journeys can include public transport, private transport and hire-able e-scooters which can beneficially augment public transport journeys. This study proposes and tackles a new and more general multi-modal multi-objective journey planning problem than considered previously. Our aim is to generate Pareto sets of multi-modal journeys which minimise the objectives of cost, travel time, inconvenience, CO 2 emissions, and calorie expenditure, as well as find journeys that optimally balance the trade-offs between them. Commuters can then choose between green, cheap, fast, car-free or convenient journeys. This work proposes implicit enumeration and heuristic algorithms which are analysed in terms of their theoretical time complexity, empirically in terms of solution time and optimality gap using a series of Manhattan and random structure transport networks. We show that our algorithms can generate solutions of equal quality to RAPTOR—a prominent existing journey planning algorithm. For this problem we reveal that the number of network nodes is a huge computational bottleneck, leading to the suggestion that future research can benefit from limiting the number of network nodes that are considered as transfer points. While our heuristic can generate good quality Pareto sets quicker than the enumeration procedure for the largest instances considered, we show that the enumeration procedure with pruning rules can still be the most effective strategy for this particular problem. • This work proposes a new multi-modal multi-objective journey planning problem. • Exact and heuristic solution algorithms are proposed with time complexity analyses. • Output Pareto sets include green, cheap, fast, car-free, and convenient journeys. • Ablation studies reveal the optimal and fastest algorithm configurations. • Transfer-point-sample based algorithms are suggested to address a main bottleneck. [ABSTRACT FROM AUTHOR]

The complex and heterogeneous ecosystem of the Internet of Things (IoT) makes it difficult to achieve energy-efficient routing because of the power, memory, and processing constraints of smart motes. Recently, metaheuristic-based routing is preferred by researchers for energy-efficient transmission in IoT. Existing literature divulge their studies to apply metaheuristic directly in IoT by ignoring the principles that account for the overall performance and the global optimum solution. Also, there is no comprehensive study that addresses the issues, principles, and significance of metaheuristic routing in terms of hybridization, objectivity, and applicability in IoT. Being enthused by the aforementioned issues, a detailed taxonomy of metaheuristic-based routing in IoT is presented in this study. A detailed taxonomy is abstracted into an energy-efficient routing framework to provide solutions to the main adversaries encountered during evaluation. The theoretical framework dwells upon two predictive models: i) Metaheuristic-based selection of potential node for energy efficiency in an IoT. ii) Introduction of metaheuristic principles to avoid convergence issues during evaluation. The comprehensive study confers the routing vulnerabilities, energy saving mechanisms, and the pros and cons of metaheuristic in context to IoT. The key research challenges in metaheuristic based routing and future directions to curb with same are thoroughly provided. Further, a smart manufacturing-based case study is demonstrated to generate the fitness criteria for process scheduling problems to gain energy efficiency. The proposed framework provides a benchmarking solution for the ongoing metaheuristic adversaries that have been undercoated in literature to prove the superiority of an algorithm.

Container-based sanitation (CBS) is increasingly used to provide safely managed sanitation in low-income urban settlements. However, questions remain around the viability of scaling up the technology, partly because it relies on regular emptying and servicing of containers by a CBS provider. This paper investigates mechanisms by which this process can be achieved efficiently. Three separate collection strategies are evaluated for their routing efficiencies as CBS goes to scale. An open-source route optimisation solver determines the constituent driving and walking distances necessary for each strategy and has been applied in areas of Cape Town, Cap-Haïtien, Lima and Nairobi. The results indicate that with fewer users (e.g. 50) transfer station models offer the shortest driving routes. However, these do require users to carry their containers (e.g. up to 170 m when stations are 100 m apart). As the number of users increases (e.g. to 5,000), visiting individual houses from a neighbourhood depot offers increasingly efficient driving distances. Overall, however, the results suggest that economies in collection distances for scaled CBS will be largely conditional on greater vehicle capacity (rather than any particular provision strategy). This highlights the importance of road access throughout low-income urban settlements in providing a viable CBS service at scale. HIGHLIGHTS Container-based sanitation is a form of road-based faecal sludge management.; Efficient routing of collection vehicles is critical for enabling scaled CBS.; This modelling evaluates three existing provision strategies using Google-OR tools.; Transfer stations and depots within neighbourhoods can reduce the required driving.; However, enlarged vehicle capacity offers greatest reductions in collection routing.;

Despite the progress in the field of automatic public transport route optimisation in recent years, there exists a clear gap between the development of optimisation algorithms and their applications in real-world planning processes. In this study, we bridge this gap by developing an interface between the urban transit routing problem (UTRP) and the professional transport modelling software PTV Visum. The interface manages the differences in data requirements between the two worlds of research and allows the optimisation of public transport lines in Visum network models. This is demonstrated with the application of selection hyper-heuristics on two network models representing real-world urban areas. The optimisation objectives include the passengers' average travel time and operators' costs. Furthermore, we show how our approach can be combined with a mode choice model to optimise the use of public transport in relation to other modes. This feature is applied in a special optimisation experiment to reduce the number of private vehicles on a selected set of links in the network. The results demonstrate the successful implementation of our interface and the applied optimisation methods for a multi-modal public transport network. [ABSTRACT FROM AUTHOR]

The majority of academic studies on the optimisation of public transport routes consider passenger trips to be fixed between pairs of stop points. This can lead to barriers in the use of the developed algorithms in real-world planning processes, as these usually utilise a zone-based trip representation. This study demonstrates the adaptation of a node-based optimisation procedure to work with zone-to-zone trips. A core element of this process is a hybrid approach to calculate zone-to-zone journey times through the use of node-based concepts. The resulting algorithm is applied to an input dataset generated from real-world data, with results showing significant improvements over the existing route network. The dataset is made publicly available to serve as a potential benchmark dataset for future research. [ABSTRACT FROM AUTHOR]

Reducing the travel time of emergency vehicles (EVs) is an effective way to improve critical services such as ambulance, fire, and police. Route optimisation and pre-emption are powerful techniques used to reduce EV travel time. This paper presents a systematic literature review of optimisation and pre-emption techniques for routing EVs. A detailed classification of existing techniques is presented along with critical analysis and discussion. The study observes the limitations of existing routing systems and lack of real-world applications of the proposed pre-emption systems, leading to several interesting and important knowledge and implementation gaps that require further investigation. These gaps include optimisations using real-time dynamic traffic data, considering time to travel as a critical parameter within dynamic route planning algorithms, considering advanced algorithms, assessing and minimising the effects of EV routing on other traffic, and addressing safety concerns in traffic networks containing multiple EVs at the same time. [ABSTRACT FROM AUTHOR]

We introduce an adaptive network for public transport route optimisation by scaling down the available street network to a level where optimisation methods such as genetic algorithms can be applied. Our scaling is adapted to preserve the characteristics of the street network. The methodology is applied to the urban area of Nottingham, UK, to generate a new benchmark dataset for bus route optimisation studies. All travel time and demand data as well as information of permitted start and end points of routes, are derived from openly available data. The scaled network is tested with the application of a genetic algorithm adapted for restricted route start and end points. The results are compared with the real-world bus routes. [ABSTRACT FROM AUTHOR]

The split-delivery vehicle-routing problem in which delivery to a demand point can be served by any number of vehicles is an important branch of classic VRP. Objective function is used to minimise travel distance while using the lowest number of vehicles. According to the maximum-minimum distance clustering method, a three-stage algorithm is proposed. First, the maximumminimum distance method is employed to cluster customer points into the lowest number of groups. Second, according to the maximum vehicle capacity, the load demand in each group is adjusted to create suitable customer points in each clustering group by adopting 'push-out' and 'pull-in' operations. Third, a tabu search is used and an optimised route for each group is generated to minimise the total travel distance. Numerical experiments, some on the benchmark data set, are presented to verify the feasibility and effectiveness of the proposed algorithm. The computational results show that the performance of the proposed algorithm is better in terms of both optimised travel distance and less computation time when the problem size is less than 75. The results also show that when the customer points are in a cluster distribution around the depot, the algorithm achieves better performance. [ABSTRACT FROM AUTHOR]

We investigate the problem of vehicle route planning in a dynamic environment. In order to better reflect real-life situations, we assume that travel times are not known exactly, but bounded from below and from above, i.e., they are given as interval quantities. Accordingly, we develop algorithms for effective route replanning in a highly dynamic road network environment that combines traffic image processing with interval data for dynamic path optimisation. The developed algorithms are integrated into a larger system for traffic management. The efficiency of the proposed algorithms and their ability to support the dynamics of road traffic is verified using real data. The experimental research was also conducted using the microscopic, time-discrete, space-continuous traffic simulator. [ABSTRACT FROM AUTHOR]

After decades of limited situational awareness for aircraft flying in the mid-North Atlantic, full satellite coverage will soon be available. This opens up the possibility of altering flight routes to exploit the wind field fully. By considering flights between New York and London, from 1 December, 2019 to 29 February, 2020, it is shown how changes to current practice could significantly reduce fuel use and, hence, greenhouse gas emissions. When airspeed and altitude are constant, the fuel flow rate per unit time is constant and the route with the minimum journey time uses the least fuel. Optimal control theory is used to find these minimum time routes through wind fields from a global atmospheric re-analysis dataset. The total fuel burn and, hence, the emissions (including CO _2 ) are proportional to the ‘air distance’ (the product of airspeed and flight time). Minimum-time routes are compared with the actual routes flown through the wind fields. Results show that current flight tracks have air distances that are typically several hundred kilometres longer than the fuel-optimised routes. Potential air distance savings range from 0.7% to 7.8% when flying west and from 0.7% to 16.4% when flying east, depending on airspeed and which of the current daily tracks is used. Thus, substantial reductions in fuel consumption are possible in the short term. This is in contrast to the incremental improvements in fuel-efficiency through technological advances, which are high cost, high risk and take many years to implement.

1. Pollinators, such as bees, face the complex challenge of efficiently exploiting patchily distributed floral resources across large landscapes. 2. In the present study we consider the utility of spatial network statistics to analyse the foraging patterns of bees moving between feeding sites at various spatial and temporal scales. 3. We explain how spatial movement networks can be derived theoretically and experimentally to describe bee foraging decisions. 4. We illustrate this approach by analysing six datasets of bumblebees and honeybees foraging in arrays of artificial flowers, and showing how network metrics change as foragers gain experience with the spatial distribution of feeding sites. 5. We compare network analyses with more conventional statistics used to characterise bee foraging movements and discuss the implications of our novel statistical and modelling approach for pollination ecology. [ABSTRACT FROM AUTHOR]

Collection of municipal solid waste is one of the most important elements of municipal waste management and requires maximum fund allocated for waste management. The cost of collection and transportation can be reduced in comparison with the present scenario if the solid waste collection bins are located at suitable places so that the collection routes become minimum. This study presents a suitable solid waste collection bin allocation method at appropriate places with uniform distance and easily accessible location so that the collection vehicle routes become minimum for the city Dhanbad, India. The network analyst tool set available in ArcGIS was used to find the optimised route for solid waste collection considering all the required parameters for solid waste collection efficiently. These parameters include the positions of solid waste collection bins, the road network, the population density, waste collection schedules, truck capacities and their characteristics. The present study also demonstrates the significant cost reductions that can be obtained compared with the current practices in the study area. The vehicle routing problem solver tool of ArcGIS was used to identify the cost-effective scenario for waste collection, to estimate its running costs and to simulate its application considering both travel time and travel distance simultaneously. [ABSTRACT FROM AUTHOR]

In global demand of improvement of electrical and electronic waste management systems, stakeholders look for effective collection systems that generate minimal costs. In this study we propose a novel model for application in mobile collection schemes - on demand that waste be taken back from household residents. This type of the waste equipment collection is comfortable for residents as they can indicate day and time windows for the take-back. Collecting companies are interested in lowering operational costs required for service. This lowering includes selection of a sufficient number of vehicles and employees, and then minimising the routes’ length in order to achieve savings in fuel consumption, and lowering of emissions. In the proposed model we use a genetic algorithm for optimisation of the route length and number of vehicles and fuzzy logic for representation of the household residents’ satisfaction on the take-back service provided by collection companies. Also, modern communication channels like websites or mobile phone applications can be used to send the waste equipment take-back request from the household, so it has the potential to be developed in future applications. The operation of the model has been presented in the case study of a city in southern Poland. The results can be useful for collecting companies and software producers for preparation of new applications to be used in waste collection. [ABSTRACT FROM AUTHOR]

Large container ships can only be berthed in hub ports with deep water, which requires a feeder ship service to transit and transport containers from the hub ports. This paper presents a feeder routing optimisation method for container ships through an intelligent Electronic Chart Display and Information System (ECDIS). ECDIS has been adopted to design routes and calculate the estimated time of arrival in two ports, and a mixed integer programming model is established for container vessel regional transportation where the shortest ship sailing time is designated as the objective function. In this paper, through using heuristic tour-route coding, the solution of the model based on genetic algorithms is presented to select ship capacities and routes simultaneously. Taking the Pearl River in China as an example, for different types of vessel capacity, vessel costs and fuel costs, 100 TEU and 150 TEU ship capacities with six optimal routes are selected to minimise sailing time and operating costs. [ABSTRACT FROM AUTHOR]

The logistic sector raises a number of highly challenging problems. Probably one of the most important ones is the shipping planning, i.e. plan the routes that the shippers have to follow to deliver the goods. In this article, we present an artificial intelligence-based solution that has been designed to help a logistic company to improve its routes planning process. In order to achieve this goal, the solution uses the knowledge acquired by the company drivers to propose optimised routes. Hence, the proposed solution gathers the experience of the drivers, processes it and optimises the delivery process. The solution uses data mining to extract knowledge from the company information systems and prepares it for analysis with a case-based reasoning (CBR) algorithm. The CBR obtains critical business intelligence knowledge from the drivers experience that is needed by the planner. The design of the routes is done by a genetic algorithm that, given the processed information, optimises the routes following several objectives, such as minimise the distance or time. Experimentation shows that the proposed approach is able to find routes that improve, on average, the routes made by the human experts. [ABSTRACT FROM PUBLISHER]

The Australian urban construction electricity sector has witnessed a transformational effect in the use of small-scale solar photovoltaic (PV) systems in the past decade. Currently, Australia has one of the highest rates of rooftop solar PV users with over 20% of households connected. This will see a rapid growth in the volume of PV waste in the coming years when these PV systems come to their end-of-life or require replacement. The collection and transportation involved in solar PV waste treatment has a significant impact on the environmental sustainability of Australian cities while designing a holistic reverse logistic (RL) network may play an essential role in the reduction of the associated cost and environmental impacts. In this study, the Weibull distribution model is employed to forecast the PV waste in the next three decades in South Australia. The study further estimates the pollutant emission associated with the collection and transportation of the waste for recycling and recovery using hotspot analysis, location allocation modelling and vehicle routing problem. Generation of pollutants - Particulate Matter (PM), Carbon Monoxide (CO), Carbon dioxide (CO 2) and Nitrogen Oxides (NOx) associated with transport and energy consumption are estimated through three routing scenarios. Results indicate that, there will be 109,007 tons of PV waste generated in urban and suburban context in South Australia by 2050. Among the three routing scenarios generated, the third scenario with optimised transfer stations and an additional recycling facility showed more than 34% reduction in pollutant emission. Such additional PV waste management facilities require policy support and regulations to effectively manage solar PV waste treatment and logistics. • The Weibull distribution model is employed to forecast the loss of PV waste. • A total of over 109,007 tons of PV waste will be generated by 2050. • Pollutant emission associated with transportation of PV waste is estimated. • Pollutants-PM, CO, CO 2 and NOx are estimated through three routing scenarios. • The third scenario optimised showed up to over 34% reduction in pollutant emission. [ABSTRACT FROM AUTHOR]

Abstract: Food products are among the most frequently delivered items to retail shops in city centres and also need special attention owing to their perishable nature and quality requirements. The main objective of this study was to map out the segments of food distribution systems and determine the constraints and possibilities in developing a co-ordinated and optimised food distribution system in and around Uppsala city, to promote efficiency and environmental sustainability. The study was conducted by arranging a series of seminars, carrying out field measurements, optimisation analysis and emission estimation. Data on eight companies that distribute food in and around Uppsala city, distribute were gathered and analysed. Different tools were used successfully i.e. Global Positioning System (GPS) for field measurement, RouteLogiX for route optimisation analysis and MODTRANS (a Matlab based package) for emission estimation. Optimising the individual routes reduced travel distance by 39% and time by 40% while total optimisation reduced the number of routes by 58%, the number of vehicles by 42%, and the total distance by 39%. Consequently, optimisation could reduce emissions generated by vehicles by 48%. [Copyright &y& Elsevier]

Sophisticated routing of ships is increasingly recognised as an important contribution to safe, reliable and economical ship operation. The more reliable the weather forecasts and the performance simulation of ships in a seaway become, the better they serve to identify the best possible route in terms of criteria like estimated time of arrival (ETA), fuel consumption (FUEL), safety (of ship, crew, passengers and cargo) and comfort. This establishes a multi-objective, non-linear and constrained optimisation problem in which a suitable compromise has to be found between opposing targets. For its solution, a new optimisation approach to select the most advantageous route on the basis of hydrodynamic simulations and sophisticated weather forecasts is posed. Transfer functions are employed to assess the operating behaviour of a ship in waves. Probabilistic ensemble forecasts are applied to account for the stochastic behaviour of weather. A multi-objective genetic algorithm turns out to be a suitable optimisation method to identify Pareto-optimum routes for a sustainable support of a conscious decision-making process. [ABSTRACT FROM AUTHOR]

Abstract: The paper presents a compact method to calculate a suitable route for a sailboat in order to reach any specified target. The calculation is based on the optimisation of the time derivative of the distance between boat and target and features a hysteresis condition, which is of particular importance for beating to windward. The algorithm provides an answer to the perennial question when to tack on upwind courses. Further, it immediately adapts to varying wind conditions. The resulting routes for different conditions are analysed on the basis of a simulation featuring a mathematical boat model. Experiments have been carried out using an unmanned and autonomously controlled sailboat. The navigated route agrees well with the simulation results. [Copyright &y& Elsevier]