Abstract

The Fixed Charge Transportation Problem (FCTP) is a Linear Programming (LP) class, whereby the total shipping cost of a product, from a source to a destination, consists of a unit transportation cost, proportional to the amount of sent items and a fixed charge associated with the opening of the route. The FCTP is NP-hard and has a characteristic in which, as far as the difference between the value of the unit cost and the fixed charge increases, the computational time changes, worsening the performance. This paper purpose a matheuristic approach to the FCTP which results were good when compared to other methods available in the Literature.

 

Abstract

International audience; To sustain the rapidly increasing air traffic demand, the future air traffic management system will rely on a concept, called Trajectory-Based Operations (TBO), that will require aircraft to follow an assigned 4D trajectory (time-constrained trajectory) with high precision. TBO involves separating aircraft via strategic (long-term) trajectory deconfliction rather than the currently-practicing tactical (short-term) conflict resolution. In this context, this chapter presents a strategic trajectory planning approach aiming at minimizing the number of conflicts between aircraft trajectories for a given day. The proposed methodology allocates an alternative departure time, a horizontal flight path, and a flight level to each aircraft at a nation-wide scale.In real-life situations, aircraft may arrive at a given position with some uncertainties on its curvilinear abscissa due to external events. To ensure robustness of the strategic trajectory plan, the aircraft arrival time to any given position will be represented here by a probabilistic distribution over its nominal assigned arrival time.The proposed approach optimizes the 4D trajectory of each aircraft so as to minimize the probability of potential conflicts between trajectories. A hybrid-metaheuristic optimization algorithm has been developed to solve this large-scale mixed-variable optimization problem. The algorithm is implemented and tested with real air traffic data taking into account uncertainty over the French airspace for which a conflict-free and robust 4D trajectory plan is produced.

Abstract

Increasing transport activities in cities are a substantial driver for congestion and pollution, influencing urban populations’ health and quality of life. These effects are consequences of ongoing urbanization in combination with rising individual demand for mobility, goods, and services. With the goal of increased environmental sustainability in urban areas, city authorities and politics aim for reduced traffic and minimized transport emissions. To support more efficient and sustainable urban transport, this cumulative dissertation focuses on alternative transport concepts. For this purpose, scientific methods and models of the interdisciplinary information systems domain combined with elements of operations research, transportation, and logistics are developed and investigated in multiple research contributions. Different transport concepts are examined in terms of optimization and acceptance to provide decision support for relevant stakeholders. In more detail, the overarching topic of urban transport in this dissertation is divided into the complexes urban mobility (part A) in terms of passenger transport and urban logistics (part B) with a focus on the delivery of goods and services. Within part A, approaches to carsharing optimization are presented at various planning levels. Furthermore, the user acceptance of ridepooling is investigated. Part B outlines several optimization models for alternative urban parcel and e-grocery delivery concepts by proposing different network structures and transport vehicles. Conducted surveys on intentional use of urban logistics concepts give valuable hints to providers and decision makers. The introduced approaches with their corresponding results provide target-oriented support to facilitate decision making based on quantitative data. Due to the continuous growth of urban transport, the relevance of decision support in this regard, but also the understanding of the key drivers for people to use certain services will further increase in the future. By providing decision support for urban mobility as well as urban logistics concepts, this dissertation contributes to enhanced economic, social, and environmental sustainability in urban areas.

Abstract

div"In recent years, there has been considerable growth in the adoption and technology development of electric vehicles (EV), autonomous vehicles (AV), and ride sharing (RS). These technologies have the potential to improve transportation sustainability. Many studies have evaluated the environmental impacts of these technologies but the existing literature has three major gaps: (1) the adoption of these three technologies need to be evaluated considering their impact on each other, (2) many existing models do not evaluate systems on a common ground, and (3) the heterogeneous preferences of riders towards these emerging technologies are not fully incorporated. To address these gaps, this work studies and quantifies the environmental and efficiency gains that can be gained through these emerging transportation technologies by developing a Parameterized Preference-based Shared Autonomous Electric Vehicle (PP-SAEV) agent-based model. The model is then applied to a case study of New York City (NYC) taxis to evaluate the system performance with increasing AV, EV, and RS adoption."/div""div""br""/div""div"The outputs from the PP-SAEV model show that replacing taxi cabs in NYC with AVs along with RS potentially can reduce CO\\textsubscript{2} emissions by 866 metric Tones per day and increase average vehicle occupancy from 1.2 to 3 persons in vehicles with passenger seating capacity of 4. A prediction model based on the PP-SAEV output recommends that 6000 vehicles are needed to maintain the current level of service with 100\\% AV and RS adoption using capacity 4 taxis. Taxi fleets with capacity 4 with high RS and low AV adoption are also found to have the least CO\\textsubscript{2} emissions. Because the heterogeneous sharing preferences of riders have shown as the major limiting factor to ride sharing, these heterogeneous sharing preferences are further modelled. The results show that high service levels are achieved when all the riders are open to sharing, and the maximum service level is reached when 30\\% of riders will only accept shared rides and 70\\% of the riders are either indifferent to sharing or prefer to use ride sharing over riding alone. Additionally, the service level and waiting time of riders that are inflexible (will accept only shared or non-shared rides) are greatly impacted by varying mix of riders with different sharing preference. Finally, an optimization model was built to site charging stations in a system with continually increasing EV adoption. Using the best charging station locations, transforming a fleet of autonomous or traditional vehicles to electric vehicles does not significantly change the system service level. The results show that increasing the EV adoption in fleets with 100\\% RS and AV adoption reduced the daily CO\\textsubscript{2} emissions by about 861 Tones and transforming a fleet of traditional taxi cabs to electric taxi cabs reduced the daily CO\\textsubscript{2} emissions by 1100 Tones."/div""div""br""/div""div"In summary, this dissertation evaluates the potential growth of autonomous vehicles, ride sharing, and electric vehicles in systems where riders may have heterogeneous sharing preferences, from a system performance`s perspective and assesses the environmental impacts. The developed model and the insights gained from this study can inform policy makers to develop sustainable transportation systems incorporating the emerging transportation technologies."/div

Abstract

20% of Europe's GHG emissions come from road transportation. Freight transport energy consumption, and thus emission levels, is tightly coupled to GDP. So if Europe seeks to dramatically reduce emissions without sacrificing wealth, it is crucial to address the issue of energy efficiency within the road transportation sector. Today, empty journeys comprise 25%-40% of all road haulage. To address that issue the goal of this project is to describe and solve an optimization program that utilizes the opportunity for trailer-haulers to exchange trailers mid-way. If successful, this will allow haulers to reduce their amount of empty journeys, benefiting the environment.

Abstract

Long term projections of cargo throughput are indispensable for port development plans. Although commodity flow projections are useful for governments, port authorities, terminal operating companies and port users, scientific research on commodity flow projections for ports is limited. Existing studies generally use econometric models that assume stable relationships between growth of port throughput and such variables as GDP growth and trade growth. This paper presents a method that was developed as part of the Port Vision 2030 project of Port of Rotterdam Authority. The method combines a model with expert judgement and commodity specific research. This combination enables incorporating disruptions of past growth patterns. The contribution of this paper is the description of this method, its application to the volumes in 2030 of all major commodities handled in the Hamburg – Le Havre range, with four different scenarios, while most studies deal with a few commodities in one port, and often for a shorter period and with less scenarios. The results show that in all scenarios, total throughput is expected to rise, although in three scenarios not as fast as in the previous two decades. Furthermore, intermediates and container flows are expected to continue to grow, while throughput of raw materials (iron ore, crude oil) may decline.

Abstract

Part 3: Computational Intelligence and Algorithms; International audience; Computational technologies under the domain of intelligent systems are expected to help the rapidly increasing traffic congestion problem in recent traffic management. Traffic management requires efficient and accurate forecasting models to assist real time traffic control systems. Researchers have proposed various computational approaches, especially in short-term traffic flow forecasting, in order to establish reliable traffic patterns models and generate timely prediction results. Forecasting models should have high accuracy and low computational time to be applied in intelligent traffic management. Therefore, this paper aims to evaluate recent computational modeling approaches utilized in short-term traffic flow forecasting. These approaches are evaluated by real-world data collected on the British freeway (M6) from 1st to 30th November in 2014. The results indicate that neural network model outperforms generalized additive model and autoregressive integrated moving average model on the accuracy of freeway traffic forecasting. Document type: Part of book or chapter of book

Abstract

Logistic Service Providers (LSPs) offering hinterland transportation face the trade-off between efficiently using the capacity of long-haul vehicles and minimizing the first and last-mile costs. To achieve the optimal trade-off, freights have to be consolidated considering the variation in the arrival of freight and their characteristics, the applicable transportation restrictions, and the interdependence of decisions over time. We propose the use of a Markov model and an Approximate Dynamic Programming (ADP) algorithm to consolidate the right freights in such transportation settings. Our model incorporates probabilistic knowledge of the arrival of freights and their characteristics, as well as generic definitions of transportation restrictions and costs. Using small test instances, we show that our ADP solution provides accurate approximations to the optimal solution of the Markov model. Using larger problem instances, we show that our modeling approach has significant benefits when compared to common-practice heuristic approaches. Document type: Part of book or chapter of book

Abstract

In order to provide better Quality-of-Service (QoS) in large networks, several congestion pricing proposals have been made in the last decade. Usually, however, those proposals studied optimal strategies and did not focus on implementation issues. Our main contribution in this paper is to address implementation issues for congestion-sensitive pricing over a single domain of the differentiated-services (diff-serv) architecture of the Internet. We propose a new congestion-sensitive pricing framework Distributed Dynamic Capacity Contracting (Distributed-DCC), which is able to provide a range of fairness (e.g. max-min, proportional) in rate allocation by using pricing as a tool. Within the Distributed-DCC framework, we develop an Edge-to-Edge Pricing Scheme (EEP) and present simulation experiments of it. Document type: Part of book or chapter of book