Computational Methods and Models for Transport

Although Internet of Things (IoT) has got lots of attention especially during recent years, its origin gets back to older times. IoT is all about connecting different entities in systems, equipped with sensors and actuators by means of internet technology. It has various applications in a wide range of industries from healthcare and agriculture to automotive, manufacturing and supply chain. Looking at the huge amount of money, invested by all big players in different industries to provide the necessary technological infrastructure, shows the strategic importance of implementing this game-changing technology. IoT is not a future paradigm; it is all about the present. It has changed the business models, caused new businesses to be introduced and has revolutionized the way that industries perform. This article reviews Internet of Things, its different applications in several industries as well as its opportunities and challenges.

When one thinks of Houston a variety of different economic activities might come to mind: energy, space, and medicine. Typically one does not think first of transportation. However, the transportation enterprise in the Houston area is massive. Houston is one of the few places in the world where the various modes of transportation: rail, highway, maritime, inland waterway, pipeline and air converge. Approximately 90% of the volume and 70% of the value of goods transported worldwide takes place at sea. These general percentages also apply for the Houston region. Our focus will be on the maritime sector in particular the Port of Houston and the Houston Ship Channel.

The environments, in which we all live, are known to be complex and unpredictable. The complete discovery of these environments aiming to take full control over them is a “mission-impossible”, however, still in our common agenda. People intend to make their living spaces smarter utilizing innovations from the Internet of Things and Artificial Intelligence. Unmanned aerial vehicles (UAVs) as very dynamic, autonomous and intelligent things capable to discover and control large areas are becoming important “inhabitants” within existing and future smart cities. Our concern in this paper is to challenge the potential of UAVs in situations, which are evolving fast in a way unseen before, e.g., emergency situations. To address such challenges, UAVs have to be “intelligent” enough to be capable to autonomously and in near real-time evaluate the situation and its dynamics. Then, they have to discover their own missions and set-up suitable own configurations to perform it. This configuration is the result of flexible plans which are created in mutual collaboration. Finally, the UAVs execute the plans and learn from the new experiences for future reuse. However, if to take into account also the Big Data challenge, which is naturally associated with the smart cities, UAVs must be also “wise” in a sense that the process of making autonomous and responsible real-time decisions must include continuous search for a compromise between efficiency (acceptable time frame to get the decision and reasonable resources spent for that) and effectiveness (processing as much of important input information as possible and to improve the quality of the decisions). To address such a “skill” we propose to perform the required computations using Cloud Computing enhanced with Semantic Web technologies and potential tools (“agile” deep learning) for compromising, such as, e.g., focusing, filtering, forgetting, contextualizing, compressing and connecting.

This paper presents a powerful new single-parameter metaheuristic to solve the Fleet Size and Mix Vehicle Routing Problem with Time Windows. The key idea of the new metaheuristic is to perform a random number of random-sized jumps in random order through four well-known local search operators. Computational testing on the 600 large-scale benchmarks of Bräysy et al. (Expert Syst Appl 36(4):8460–8475, 2009) show that the new metaheuristic outperforms previous best approaches, finding 533 new best-known solutions. Despite the significant number of random components, it is demonstrated that the variance of the results is rather low. Moreover, the suggested metaheuristic is shown to scale almost linearly up to 1000 customers.

A lack of methodological tools that can be used to support decision makers in decreasing greenhouse gas emission levels has motivated us to write this paper. This paper investigates the problem of determining the allocation of available green route capacity. The approach for designing a green transit network that offers green vehicles shorter travel times between given origins and destinations is discussed. This approach is extended to minimize greenhouse gas emissions. For this purpose, bi-level programs are formulated to minimize the emission function under competitive and non-competitive scenarios.

The most common approaches to solve the variants of the well-known vehicle routing problem are based on metaheuristic hill-climbing search. The deficiency of these methods is slow local search based hill climbing that often is restricted to limited local neighborhood. In this paper we suggest a novel new two-phase metaheuristic that escapes the local minima with jumps of varying size, instead of step by step local hill climbing. The initial solution is first generated with a powerful ejection pool heuristic. The key idea of the improvement phase is to combine large neighborhood search with standard guided local search metaheuristic in a novel way, allowing improved search diversification and escape from local minima in more efficient way through jumps. The algorithm has been tested on the standard Gehring and Homberger benchmarks for the vehicle routing problem with time windows and the results indicate very competitive performance. We found 12 new and 43 matched best-known solutions and the best overall results for all problem sizes at comparable computation times.

The aim of this paper is to propose a method to cluster GPS data corresponding to driving destinations. A new DBSCAN-based algorithm is proposed to group stationary GPS traces, collected prior to end of trips, into destination clusters. While the original DBSCAN clustering algorithm uses a global threshold as a closeness measure in data space, we develop a method to set local thresholds values for data points; this is important because the GPS data proximity strongly depends on the density of the street grid around each point. Specifically, the spread of GPS coordinates in parking lots can vary substantially between narrow (personal parking lot) and wide (parking lot of a shopping mall) depending on the destinations. To characterize the parking lot diversities at each destination, we introduce the concept of using a local threshold value for each data point. The local threshold values are inferred from road graph density using a map database. Moreover, we propose a mutual reachability constraint to preserve the insensitivity of DBSCAN with respect to the ordering of the points. The performance of the proposed clustering algorithm has been evaluated extensively using trips of actual cars in Sweden, and some of the results are presented here.

Vehicle routing systems provide several advantages over manual transportation planning and they are attracting growing attention. However, deployment of these systems can be prohibitively costly, especially for small and medium-sized enterprises: the customization, integration, and migration is laborious and requires operations research expetise. We propose an automated configuration workflow for vehicle routing system and data flow customization, which will provide the necessary basis for more experimental work on the subject. Our preliminary results with learning and adaptive algorithms support the assumption of applicability of the proposed configuration framework. The strategies presented here equip implementers with the methods needed, and give an outline for automating the deployment of these systems. This opens up new directions for research in vehicle routing systems, data exchange, model inference, automatic algorithm configuration, algorithm selection, software customization, and domain-specific languages.

The multi-period fleet size and mix vehicle routing problem with stochastic demands is a new optimization problem that arises from the need to make strategic fleet sizing decisions while taking into consideration tactical planning and operational uncertainty. The setting is a distribution company that delivers goods to a set of customers and where the expected demand for different customers vary from period to period. The actual demand in a given period is stochastic, and is only revealed when visiting the customer. The objective is to minimize total expected costs, consisting of vehicle acquisition costs, routing costs, and the expected cost of route failures. Route failures occur when a vehicle arrives at a customer with an insufficient amount of goods, resulting in the need to refill the vehicle at the depot. The problem is formulated as a mixed integer programming problem. A heuristic for solving the problem is described and implemented, and a computational study is conducted on a set of varied test instances.

Aircraft emission targets worldwide and their climatic effects have put pressure in government agencies, aircraft manufacturers and airlines to reduce water vapour, carbon dioxide (\(CO_{2}\)) and oxides of nitrogen (\(NO_{x}\)) resulting from aircraft emissions. The difficulty of reducing emissions including water vapor, carbon dioxide (\(CO_{2}\)) and oxides of nitrogen (\(NO_{x}\)) is mainly due to the fact that a commercial aircraft is usually designed for a particular optimal cruise altitude but may be requested or required to operate and deviate at different altitudes and speeds to archive a desired or commanded flight plan, resulting in increased emissions. This is a multi- disciplinary problem with multiple trade-offs such as optimizing engine efficiency, minimizing fuel burnt and emissions while maintaining prescribed aircraft trajectories, altitude profiles and air safety. There are possible attempts to solve such problems by designing new wing/aircraft shape, new efficient engine, ATM technology, or modifying the aircraft flight plan. Based on the rough data provided by an air carrier company, who was willing to assess the methodology, this paper will present the coupling of an advanced optimization technique with mathematical models and algorithms for aircraft emission, and fuel burnt reduction through flight plan optimization. Two different approaches are presented; the first one describes a deterministic optimization of the flight plan and altitude profile in order to reduce the fuel consumption while reducing time and distance. The second approach presents the robust design optimization of the previous case considering uncertainties on several parameters. Numerical results will show that the methods are able to capture a set of useful trade-offs solutions between aircraft range and fuel consumption, as well as fuel consumption and flight time.

While collaborative logistics has the potential to provide savings to organizations, the individual result of sub-units within an organization might not directly benefit from the collaboration. This cause problems as the sub-units may be their own result or profit centers. We face this problem in the context of an organized network of sawmills. The organization benefits from timbering exchange and joint transports with an external company. The collaboration with this external company, however, implies an increase in the direct cost of supplying some of the sawmills. This occurs because some of the flows which would be used to supply these sawmills in absence of the collaboration, are assigned to the external company in the collaborative solution. In order to make the collaboration profitable for all sawmills, the organization must reallocate the cost among the different members in the network. We address this problem by using concepts of cooperative game theory. We apply these concepts in a case involving a network of 12 Scandinavian sawmills which together cooperate with an external procurement company. The collaboration in this case results in 3.3% savings for the network. A slightly modified version of the equal profit method allows to reallocate the cost of the collaborative solution in such a way that the cost of all sawmills is reduced with respect to their direct cost in absence of collaboration, while also assures the stability of the cooperation.

This paper discusses the dynamical behavior of a randomly-fluctuating heterogeneous continuum model of the ballast. The Young’s modulus is modeled as a random field parameterized by its average, its variance and a correlation model representing non-interpenetrating spheres. A numerical model of the ballast and the surrounding soil is then constructed based on an efficient implementation of an explicit spectral element solver on a large cluster of computers. This model allows to describe numerically the wave field generated in the ballast and soil by the passage of a train, as well as to construct dispersion equations for the ballast-soil model. The influence of heterogeneity is discussed by comparison with a similar model where the ballast is assumed homogeneous. Different values of the soil mechanical parameters are considered and compared. Finally, potential consequences for the design of the ballast are discussed.

This work details how numerical methods can be employed to determine the optimal arrangement of multiple micro vortex generators (MVG) installed on a UAV wing with its flaps deflected/deployed. For very little cost, the use of MVGs can substantially improve the aerodynamic performance of the UAV by increasing the lift and reducing the drag, particularly during critical phases of the flight (takeoff and landing). The optimal position, related to the detachment zone, and height, related to the boundary layer, of a vortex generator is studied numerically by solving the three dimensional Navier-Stokes equations in a simplified configuration. The numerical study includes a reference baseline configuration. A parametric study is then performed to determine, which configuration gives the maximum gain—highest lift/drag ratio. A preliminary numerical study of convergence for different grids to ensure convergency of the solutions is included. Finally, considering these results, a selection of the optimal configurations based on the aerodynamic performance and the manufacturing limitations.

Crosswind flow over high speed trains can pose serious safety concerns for rail transport. Methodologies for evaluating the aerodynamic forces exerted on the train include full-scale measurements, physical modeling using wind-tunnel experiments and numerical modeling using computational fluid mechanics (CFD). Although CFD presents the most cost-effective approach, it faces severe uncertainties in the predicted forces, most of which are related to the turbulence modeling technique employed. In here we investigate the influence of various turbulence modeling approaches on crosswind flow simulations and calculated force coefficients. In particular, we perform URANS, LES and DDES simulations utilizing the DLR Next Generation Train 2 model geometry. Particular emphasis is laid on simulating a wind angle of 30 degrees and Reynolds number of 225,000 for which validation data is provided by wind tunnel measurements. We confirm that a major vortex system on the leeward side of the train develops, which mainly drives the overturning force and moment of the train. The lift force is determined mainly by the underbody flow, which is characterized by unsteady vortex shedding. Due to its dual ability to properly model the roof boundary layer on the one hand and to resolve small-scale turbulent eddies in the underfloor region on the other, the DDES approach is found to give the most accurate force predictions. LES overpredicts the overturning force and moment, while URANS overpredicts the lift force.

This chapter presents an analysis of how the new Russian support policy for renewable energy investments changes the expected profitability of renewable energy investments in Russia. A comparative analysis of investment profitability in the before and after support policy cases is presented for a wind farm investment to illustrate the effect of the policy. This chapter is among the first to comparatively analyze the effect of the Russian renewable energy support mechanism on investment project profitability.