Graph-based Algorithms for Smart Mobility Planning and Large-scale Network Discovery

Graph theory has become a hot topic in the past two decades as evidenced by the increasing number of citations in research. Its applications are found in many fields, e.g. database, clustering, routing, etc. In this thesis, two novel graph-based algorithms are presented. The first algorithm finds itself in the thriving carsharing service, while the second algorithm is about large graph discovery to unearth the unknown graph before any analyses can be performed. In the first scenario, the automatisation of the fleet planning process in carsharing is proposed. The proposed work enhances the accuracy of the planning to the next level by taking an advantage of the open data movement such as street networks, building footprints, and demographic data. By using the street network (based on graph), it solves the questionable aspect in many previous works, feasibility as they tended to use rasterisation to simplify the map, but that comes with the price of accuracy and feasibility. A benchmark suite for further research in this problem is also provided. Along with it, two optimisation models with different sets of objectives and contexts are proposed. Through a series of experiment, a novel hybrid metaheuristic algorithm is proposed. The algorithm is called NGAP, which is based on Reference Point based Non-dominated Sorting genetic Algorithm (NSGA-III) and Pareto Local Search (PLS) and a novel problem specific local search operator designed for the fleet placement problem in carsharing called Extensible Neighbourhood Search (ENS). The designed local search operator exploits the graph structure of the street network and utilises the local knowledge to improve the exploration capability. The results show that the proposed hybrid algorithm outperforms the original NSGA-III in convergence under the same execution time. The work in smart mobility is done on city scale graphs which are considered to be medium size. However, the scale of the graphs in other fields in the real-world can be much larger than that which is why the large graph discovery algorithm is proposed as the second algorithm. To elaborate on the definition of large, some examples are required. The internet graph has over 30 billion nodes. Another one is a human brain network contains around 1011 nodes. Apart of the size, there is another aspect in real-world graph and that is the unknown. With the dynamic nature of the real-world graphs, it is almost impossible to have a complete knowledge of the graph to perform an analysis that is why graph traversal is crucial as the preparation process. I propose a novel memoryless chaos-based graph traversal algorithm called Chaotic Traversal (CHAT). CHAT is the first graph traversal algorithm that utilises the chaotic attractor directly. An experiment with two well-known chaotic attractors, Lozi map and Rössler system is conducted. The proposed algorithm is compared against the memoryless state-of-the-art algorithm, Random Walk. The results demonstrate the superior performance in coverage rate over Random Walk on five tested topologies; ring, small world, random, grid and power-law. In summary, the contribution of this research is twofold. Firstly, it contributes to the research society by introducing new study problems and novel approaches to propel the advance of the current state-of-the-art. And Secondly, it demonstrates a strong case for the conversion of research to the industrial sector to solve a real-world problem

Demand Forecasting and Location Optimization of Recharging Stations for Electric Vehicles in Carsharing Industries

Carsharing is an alternative to private car usage. Using electric-vehicles as a substitute to fuel vehicles is a wiser option which leads to lower fuel emissions, more energy savings and decreased oil dependency. However, there are some barriers in using electric vehicles at large scale in carsharing companies. Battery power limitation and lack of sufficient infrastructures are some of them. Accurate demand forecasting is a must for this purpose. In the first part of this thesis, we investigate the demand forecasting problem for carsharing industries and apply four techniques namely simple linear regression, seasonally adjusted forecast, Winter's Model and artificial neural networks to decide the right number of vehicles to be made available at each station to meet the customer requests. The results on randomly generated test datasets show that artificial neural networks perform better over the other three. In the second part, we investigate the location planning problem of recharging stations for electric vehicles. The base model used for this study is the mathematical optimization model proposed by Wang & Lin (2013). Firstly, we improve their MIP model and solve it using AIMMS (Advanced Interactive Multidimensional Modeling System). Secondly, we propose Genetic Algorithm for the same problem and implement it in Matlab. The obtained results are compared with previous work done by Wang and Lin (2013). The comparisons show better performance of the proposed methods

Advances in Public Transport Platform for the Development of Sustainability Cities

Modern societies demand high and varied mobility, which in turn requires a complex transport system adapted to social needs that guarantees the movement of people and goods in an economically efficient and safe way, but all are subject to a new environmental rationality and the new logic of the paradigm of sustainability. From this perspective, an efficient and flexible transport system that provides intelligent and sustainable mobility patterns is essential to our economy and our quality of life. The current transport system poses growing and significant challenges for the environment, human health, and sustainability, while current mobility schemes have focused much more on the private vehicle that has conditioned both the lifestyles of citizens and cities, as well as urban and territorial sustainability. Transport has a very considerable weight in the framework of sustainable development due to environmental pressures, associated social and economic effects, and interrelations with other sectors. The continuous growth that this sector has experienced over the last few years and its foreseeable increase, even considering the change in trends due to the current situation of generalized crisis, make the challenge of sustainable transport a strategic priority at local, national, European, and global levels. This Special Issue will pay attention to all those research approaches focused on the relationship between evolution in the area of transport with a high incidence in the environment from the perspective of efficiency

Development of a Sharing Concept for Industrial Compost Turners Using Model-Based Systems Engineering, under Consideration of Technical and Logistical Aspects

The trend of sharing concepts is constantly increasing, whether this may be for economic or environmental reasons. Consequently, numerous scientific research works have addressed the subject of sharing concepts. Many of these works have dealt with questions on the topic of sharing concepts itself, however, much less research has been dedicated to the question of how the sharing concept can be developed in the very first place. Thus, the purpose of this work was to systematically use systems engineering methods to develop a sharing concept for heavy-duty agricultural vehicles, while having a strong focus on technical and logistical aspects. Due to the multidisciplinary complexity of the sharing concept, a method from the field of model-based systems engineering, ARCADIA, was chosen. On ARCADIA’s top level, operational analysis was carried out to identify the key stakeholders. The next level, systems analysis, showed that the sharing model can be divided into three main processes: (1) data acquisition and preparation; (2) location planning; (3) and route planning. For these main processes, corresponding methods, algorithms and models were sought and compared against each other in the last level, logical analysis. It can be concluded that the ARCADIA method has provided a framework for evaluating the correlations and interrelationships between methods, algorithms and models at different levels to develop a sharing concept for compost turners from a technical perspective

Location of charging stations in electric car sharing systems

Electric vehicles are prime candidates for use within urban car sharing systems, both from economic and environmental perspectives. However, their relatively short range necessitates frequent and rather time-consuming recharging throughout the day. Thus, charging stations must be built throughout the system's operational area where cars can be charged between uses. In this work, we introduce and study an optimization problem that models the task of finding optimal locations and sizes for charging stations, using the number of expected trips that can be accepted (or their resulting revenue) as a gauge of quality. Integer linear programming formulations and construction heuristics are introduced, and the resulting algorithms are tested on grid-graph-based instances, as well as on real-world instances from Vienna. The results of our computational study show that the best-performing exact algorithm solves most of the benchmark instances to optimality and usually provides small optimality gaps for the remaining ones, whereas our heuristics provide high-quality solutions very quickly. Our algorithms also provide better solutions than a sequential approach that considers strategic and operational decisions separately. A cross-validation study analyzes the algorithms' performance in cases where demand is uncertain and shows the advantage of combining individual solutions into a single consensus solution, and a simulation study investigates their behavior in car sharing systems that provide their customers with more flexibility regarding vehicle selection

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC