Towards smart open dynamic fleets

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-33509-4_32Nowadays, vehicles of modern fleets are endowed with advanced devices that allow the operators of a control center to have global knowledge about fleet status, including existing incidents. Fleet management systems support real-time decision making at the control center so as to maximize fleet perform‐ ance. In this paper, setting out from our experience in dynamic coordination of fleet management systems, we focus on fleets that are open, dynamic and highly autonomous. Furthermore, we propose how to cope with the scalability problem as the number of vehicles grows. We present our proposed architecture for open fleet management systems and use the case of taxi services as example of our proposal.Work partially supported by Spanish Government through the projects iHAS (grant TIN2012-36586-C03) and SURF (grant TIN2015-65515-C4-X-R), the Autonomous Region of Madrid through grant S2013/ICE-3019 (“MOSI-AGIL-CM”, cofunded by EU Structural Funds FSE and FEDER) and URJC-Santander (30VCPIGI15).Billhardt, H.; Fernández, A.; Lujak, M.; Ossowski, S.; Julian Inglada, VJ.; Paz, JFD.; Hernández, JZ. (2016). Towards smart open dynamic fleets. En Multi-Agent Systems and Agreement Technologies. Springer. 410-424. https://doi.org/10.1007/978-3-319-33509-4_32S41042

An Industrial Multi Agent System for real-time distributed collaborative prognostics

Despite increasing interest, real-time prognostics (failure prediction) is still not widespread in industry due to the di fficulties of existing systems to adapt to the dynamic and heterogeneous properties of real asset fleets. In order to address this, we present an Industrial Multi Agent System for real-time distributed collaborative prognostics. Our system fufil ls all six core properties of Advanced Multi Agent Systems: Distribution, Flexibility, Adaptability, Scalability, Leanness, and Resilience. Experimental examples of each are provided for the case of prognostics using the C-MAPPS engine degradation data set, and data from a fleet of industrial gas turbines. Prognostics are performed using the Weibull Time To Event - Recurrent Neural Network algorithm. Collaboration is achieved by sharing information between agents in the system. We conclude that distributed collaborative prognostics is especially pertinent for systems with presence of sensor faults, limited computing capabilities or significant fleet heterogeneity

Smart Sustainable Mobility: Analytics and Algorithms for Next-Generation Mobility Systems

To this date, mobility ecosystems around the world operate on an uncoordinated, inefficient and unsustainable basis. Yet, many technology-enabled solutions that have the potential to remedy these societal negatives are already at our disposal or just around the corner. Innovations in vehicle technology, IoT devices, mobile connectivity and AI-powered information systems are expected to bring about a mobility system that is connected, autonomous, shared and electric (CASE). In order to fully leverage the sustainability opportunities afforded by CASE, system-level coordination and management approaches are needed. This Thesis sets out an agenda for Information Systems research to shape the future of CASE mobility through data, analytics and algorithms (Chapter 1). Drawing on causal inference, (spatial) machine learning, mathematical programming and reinforcement learning, three concrete contributions toward this agenda are developed. Chapter 2 demonstrates the potential of pervasive and inexpensive sensor technology for policy analysis. Connected sensing devices have significantly reduced the cost and complexity of acquiring high-resolution, high-frequency data in the physical world. This affords researchers the opportunity to track temporal and spatial patterns of offline phenomena. Drawing on a case from the bikesharing sector, we demonstrate how geo-tagged IoT data streams can be used for tracing out highly localized causal effects of large-scale mobility policy interventions while offering actionable insights for policy makers and practitioners. Chapter 3 sets out a solution approach to a novel decision problem faced by operators of shared mobility fleets: allocating vehicle inventory optimally across a network when competition is present. The proposed three-stage model combines real-time data analytics, machine learning and mixed integer non-linear programming into an integrated framework. It provides operational decision support for fleet managers in contested shared mobility markets by generating optimal vehicle re-positioning schedules in real time. Chapter 4 proposes a method for leveraging data-driven digital twin (DT) frameworks for large multi-stage stochastic design problems. Such problem classes are notoriously difficult to solve with traditional stochastic optimization. Drawing on the case of Electric Vehicle Charging Hubs (EVCHs), we show how high-fidelity, data-driven DT simulation environments fused with reinforcement learning (DT-RL) can achieve (close-to) arbitrary scalability and high modeling flexibility. In benchmark experiments we demonstrate that DT-RL-derived designs result in superior cost and service-level performance under real-world operating conditions

An Inquiry into Supply Chain Strategy Implications of the Sharing Economy for Last Mile Logistics

As the prevalence of e-commerce and subsequent importance of effective and efficient omnichannel logistics strategies continues to rise, retail firms are exploring the viability of sourcing logistics capabilities from the sharing economy. Questions arise such as, “how can crowdbased logistics solutions such as crowdsourced logistics (CSL), crowdshipping, and pickup point networks (PPN) be leveraged to increase performance?” In this dissertation, empirical and analytical research is conducted that increases understanding of how firms can leverage the sharing economy to increase logistics and supply chain performance. Essay 1 explores crowdsourced logistics (CSL) by employing a stochastic discrete event simulation set in New York City in which a retail firm sources drivers from the crowd to perform same day deliveries under dynamic market conditions. Essay 2 employs a design science paradigm to develop a typology of crowdbased logistics strategies using two qualitative methodologies: web content analysis and Delphi surveys. A service-dominant logic theoretical perspective guides this essay and explains how firms co-create value with the crowd and consumer markets while presenting a generic design for integrating crowdbased models into logistics strategy. In Essay 3, a crowdsourced logistics strategy for home delivery is modeled in an empirically grounded simulation optimization to explore the logistics cost and responsiveness implications of sharing economy solutions on omnichannel fulfillment strategies

Sustainable urban mobility through the perspective of overcompliance

Being at the frontier with regard to sustainable aspects of manufacturing may serve as competitive advantage due to the increasing trend of consumer awareness. In order to adhere to the consequent pressure from external stakeholders such as customers, investors, competitors, interest groups and local municipals, companies voluntarily overcomply with social and environmental norms. This paper explores the incentives for the industry to embrace overcompliance as a strategic means to gain competitive advantage and take the lead in sustainable manufacturing. Examples from recent industrial trends are used to present the relevance of the combination of overcompliance and sustainability in the field of mobility. Studies of the Collaboration Research Center 1026 are presented as additional examples of strategic overcompliance with emission standards in the field of sustainable urban mobility

Simulation, optimization, and machine learning in sustainable transportation systems: Models and applications

[EN] The need for effective freight and human transportation systems has consistently increased during the last decades, mainly due to factors such as globalization, e-commerce activities, and mobility requirements. Traditionally, transportation systems have been designed with the main goal of reducing their monetary cost while offering a specified quality of service. During the last decade, however, sustainability concepts are also being considered as a critical component of transportation systems, i.e., the environmental and social impact of transportation activities have to be taken into account when managers and policy makers design and operate modern transportation systems, whether these refer to long-distance carriers or to metropolitan areas. This paper reviews the existing work on different scientific methodologies that are being used to promote Sustainable Transportation Systems (STS), including simulation, optimization, machine learning, and fuzzy sets. This paper discusses how each of these methodologies have been employed to design and efficiently operate STS. In addition, the paper also provides a classification of common challenges, best practices, future trends, and open research lines that might be useful for both researchers and practitioners.This work has been partially supported by the Spanish Ministry of Science, Innovation, and Universities (PID2019-111100RB-C21-C22/AEI/10.13039/501100011033, RED2018-102642-T) and the SEPIE Erasmus+ Program (2019-I-ES01-KA103-062602), and the IoF2020-H2020 (731884) project.Torre-Martínez, MRDL.; Corlu, CG.; Faulin, J.; Onggo, BS.; Juan-Pérez, ÁA. (2021). Simulation, optimization, and machine learning in sustainable transportation systems: Models and applications. Sustainability. 13(3):1-21. https://doi.org/10.3390/su1303155112113

Collaboration and Climate Action at the Local Scale

This dissertation encompasses a case study and a Participatory Action Research project. The case study focuses on climate change mitigation activities within King County, Washington and its 39 cities and towns and discusses progress and challenges related to transportation issues, efficiency measures, and sustainability planning. The findings indicate there is a high level of activity in waste reduction, environmental outreach and education, bicycle and pedestrian promotion, tree canopy protection, sustainability policies, and green building. Other categories, such as energy efficiency, electric vehicle infrastructure, and greenhouse gas emission inventories and goal setting are on the rise. Twelve of the cities were found to be highly active with several more initiating new sustainability related policies and programs. The two overall biggest challenges to implementing climate change mitigation efforts in this area are the lack of financial and technical resources and the lower prioritization of these activities. The Participatory Action Research project was developed and conducted in collaboration with King County and nine of its cities in support of regional climate change and sustainability solutions, with the intent to increase climate change mitigation within King County. As a result of the project, the King County Cities Climate Collaboration was created to formalize a working partnership between the cities and the County, encourage and support region-wide emission reduction strategies, and increase efficiency and effectiveness of efforts through bottom-up collaboration and systemic operational integration. This dissertation includes attached video files. The electronic version of this dissertation is accessible at the OhioLINK ETD Center, https://etd.ohiolink.edu

Small Electric Vehicles

This edited open access book gives a comprehensive overview of small and lightweight electric three- and four-wheel vehicles with an international scope. The present status of small electric vehicle (SEV) technologies, the market situation and main hindering factors for market success as well as options to attain a higher market share including new mobility concepts are highlighted. An increased usage of SEVs can have different impacts which are highlighted in the book in regard to sustainable transport, congestion, electric grid and transport-related potentials. To underline the effects these vehicles can have in urban areas or rural areas, several case studies are presented covering outcomes of pilot projects and studies in Europe. A study of the operation and usage in the Global South extends the scope to a global scale. Furthermore, several concept studies and vehicle concepts on the market give a more detailed overview and show the deployment in different applications

Marketing study

The findings are presented of a marketing survey conducted in the lake Victoria region. The research concentrated on consumers, trader /processors serving local markets, industrial processors serving mainly international markets, and fishers. The market for fish from Lake Victoria is traced from the consumer to the producer, including as many components of the chain as possible. The components are dealt with in individual sections which comprise a profile of a typical consumer/trader-processor/industrial processor /fisher, a list of survey sites, a map showing locations, a note on potential biases within the individual survey, a list of hypotheses or study topics for all surveys except for that of industrial processors, detailed analyses and also the pertinent questionnaire

Seeking equity and justice in urban freight: where to look?

Urban freight systems embed and reflect spatial inequities in cities and imbalanced power structures within transport decision-making. These concerns are principal domains of “transportation justice” (TJ) and “mobility justice” (MJ) scholarship that have emerged in the past decade. However, little research exists situating urban freight within these prevailing frameworks, which leaves urban freight research on socio-environmental equity and justice ill-defined, especially compared to passenger or personal mobility discussions. Through the lens that derives from TJ and MJ’s critical dialogue, this study synthesises urban freight literature’s engagement with equity and justice. Namely, the review evaluates: How do researchers identify equitable distributions of urban freight’s costs and benefits? At what scale do researchers evaluate urban freight inequities? And who does research consider entitled to urban freight equity and how are they involved in urban freight governance? The findings help inform researchers who seek to reimagine urban freight management strategies within broader equity and justice discourse