전기 마이크로 모빌리티 공유 시스템에서의 배터리 교체와 재배치 작업 최적화

학위논문 (석사) -- 서울대학교 대학원 : 공과대학 산업공학과, 2021. 2. 박건수.In this thesis, we consider a battery swapping and mobility inventory rebalancing problem arising in electric micro-mobility sharing systems. Vehicles are equipped with swappable batteries and they are managed by staffs' visiting each vehicle and changing depleted batteries. With the free-floating property of the system, vehicles can locate anywhere in a service area without designated stations, which increases the difficulty to visit and collect every single vehicle. In order to successfully meet user demand during the daytime, operators have to redistribute the vehicles with the right number in the right place and swap batteries with insufficient levels into fully charged ones overnight. Therefore, it is essential that operators take battery charging(swapping), staff routing, rebalancing problem all together into consideration. We aim to satisfy demand as much as possible and at the same time minimize routing and swapping costs. We formulate this problem in a mixed integer linear programming. Target inventory level for rebalancing, an important parameter used in the system, is suggested by analyzing a stochastic process that incorporates demand changes. Being a special case of vehicle routing problem with pickup and delivery, it shares the difficulty and complexity of VRP in practically large size. So as to give efficient solutions in large size problems, we develop a Cluster-first Route-second heuristic where a set partitioning problem considers inventory imbalances and approximates routing distances. We benchmark our heuristic approach on a pure MLIP formulation. The experimental result confirms that the heuristic is good at decomposing a large problem and gives efficient solutions even in practically large instances.본 연구는 교체형 배터리를 이용하는 전기 마이크로 모빌리티 공유 시스템에서의 배터리 교체 및 차량 재배치를 효율적으로 수행하는 방법을 제시하고자 한다. 수요를 성공적으로 충족시키기 위해선 모빌리티의 공급과 이용자의 수요를 맞춰주기 위한 차량 재고 차원에서의 재배치 작업과 배터리 수준을 유지시켜주는 배터리 관리 차원에서의 교체 작업이 필수적이다. 또한 충전소로 차량을 옮길 필요 없이 바로 교체할 수 있으므로 담당 직원이 산발적으로 위치한 각 모빌리티들을 순회하며 위 작업들을 진행해야 한다. 이동하며 작업하는 비용과 시간이 대부분이기 때문에 이동 순서를 함께 최적화하는 것이 비용 개선에 필수적이다. 따라서 작업 결정과 경로 결정을 동시에 고려하는 충전 및 재배치 모형을 제시한다. 이때 free-floating 모빌리티 공유시스템의 이용 수요를 효과적으로 반영하고자 수요를 stochastic process로 모델링하고 이를 이용하여 재배치 목표 수량을 구한다. 문제의 크기가 큰 경우 효율적으로 본 충전 및 재배치 모형의 좋은 해를 얻기 위한 방법으로, 해당 서비스지역의 각 구역들을 클러스터링하고 그 뒤에 스태프들의 경로와 작업을 결정하는 휴리스틱을 제안한다. 여러 스태프를 순회시키는 복잡한 형태를 클러스터링으로써 작은 크기의 문제들로 분해하여 빠르게 문제를 풀고자 한다. 이를 위해 각 클러스터에는 한 명의 스태프가 배정되고, 한 클러스터 내에서 소속된 구역들이 필요로 하는 작업들을 한 명의 스태프가 모두 진행하도록 구성한다. 최소걸침나무 근사법을 적용한 set partitioning 문제를 풀어 클러스터링을 진행한다. 계산실험 결과, 고안된 휴리스틱은 차량의 수가 많아 크기가 큰 상황에서도 빠른 시간내에 더 좋은 해를 냈다.Chapter 1. Introduction 1 1.1 Background 1 1.2 Related literature 6 1.2.1 Rebalancing in bike sharing systems 6 1.2.2 Charging and rebalancing in free-floating electric vehicle(FFEV) sharing 9 1.2.3 Charging of electric micro-mobility with swappable batteries 10 1.3 Motivation and contributions 12 1.4 Organization of the thesis 14 Chapter 2. Mathematical formulations 15 2.1 Basic assumptions and problem description 15 2.2 Demand Modeling and Target Inventory 18 2.3 Mixed integer linear programming formulation 23 Chapter 3. Heuristic approach 30 3.1 Cluster-first route-second approach 31 3.2 Clustering problem with routing cost approximation 33 3.2.1 Minimum spanning tree approximation 33 3.2.2 Clustering problem 35 3.2.3 Cluster-first Route-second heuristic 41 Chapter 4. Computational experiments 42 4.1 Design of experiment 42 4.2 Comparative Analysis 47 Chapter 5. Conclusion 52Maste

Optimization problems in the postal sector

Supply chain optimization is a widely studied field of operations research. Nevertheless, adapting the existing solutions to the specifications of each company is an interesting and stimulating challenge. With this in mind, the project described herein, developed in partnership with CTT, looks to provide the company with precious tools to more efficiently manage the labour allocated to mail delivery and increase the productivity of the workforce as a whole. To achieve these objectives, it follows up on a previous work by Pereira[26], where an extension of the Vehicle Routing Problem (VRP) was proposed to optimize the last-mile delivery step of the mail distribution procedure, but this time giving particular relevance to the adequacy of the model developed to the intricacies imposed by the company and exploring suitable adaptations. One of the requirements, for standardization purposes, is the creation of segments, composed of sets of postal codes that serve as input to the optimization model. Finally, it was necessary to merge this work with the company’s workflow by integrating the model with SISMA, a productivity assessment tool already used by CTT.A otimização de uma cadeia de abastecimento é um campo vastamente estudado no âm- bito da investigação operacional. Contudo, adaptar as soluções existentes aos critérios de cada empresa é um desafio bastante interessante e estimulante. Tendo isto em consi- deração, este projeto, desenvolvido em parceria com os CTT – Correios de Portugal, S.A. (CTT), procura fornecer à empresa ferramentas que permitam uma gestão eficiente da força de trabalho afeta à distribuição de correio. Para atingir este propósito, este trabalho teve como ponto de partida uma proposta de- senvolvida por Pereira[26], onde uma adaptação do Vehicle Routing Problem (VRP) foi desenvolvida para otimizar a etapa last-mile do processo de distribuição. No presente trabalho, dá-se uma atenção redobrada à compatibilidade do modelo desenvolvido com as complexidades impostas pela empresa e explora-se algumas melhorias consideradas apropriadas. Um dos requisitos, para manter alguma estibilidade nos resultados, é a introdução de segmentos, compostos por conjuntos contíguos de códigos postais, que ali- mentam o modelo. Finalmente, para combinar este trabalho com o fluxo de trabalho da empresa, fez-se a integração do modelo de otimização com o SISMA, uma ferramenta de avalição de produtividade já utilizada pelos CTT

Engineering for sustainable communities: Epistemic tools in support of equitable and consequential middle school engineering

This study is focused on engineering for sustainable communities (EfSC) in three middle school classrooms. Three in-depth case studies are presented that explore how two related EfSC epistemic toolsets—(a) community engineering and ethnography tools for defining problems, and (b) integrating perspectives in design specification and optimization through iterative design sketch-up and prototyping—work to support the following: (a) Students' recruitment of multiple epistemologies; (b) Navigation of multiple epistemologies; and (c) students' onto-epistemological developments in engineering. Using a theoretical framework grounded in justice-oriented notions of equity intersecting with multiple epistemologies, we investigated the impact of the related epistemic toolsets on students' engineering engagement. Specifically, the study focused on how the tools worked when they were taken up in particular ways by teacher and students, and how the nature of their iterative engagement with the tools led to outcomes in ways that were equitable and consequential, both to students' engineering experiences and their engineering onto-epistemological developments, and also in responding to the community injustices prototypes were designed to address. Tensions that emerged are discussed with further reflection on what the EfSC epistemic toolsets suggest about the affordances of a productive epistemic space and the concomitant risks related to larger institutional norms, which constrain the extent of students' justice-oriented engineering goals

Urban Public Transportation Planning with Endogenous Passenger Demand

An effective and efficient public transportation system is crucial to people\u27s mobility, economic production, and social activities. The Operations Research community has been studying transit system optimization for the past decades. With disruptions from the private sector, especially the parking operators, ride-sharing platforms, and micro-mobility services, new challenges and opportunities have emerged. This thesis contributes to investigating the interaction of the public transportation systems with significant private sector players considering endogenous passenger choice. To be more specific, this thesis aims to optimize public transportation systems considering the interaction with parking operators, competition and collaboration from ride-sharing platforms and micro-mobility platforms. Optimization models, algorithms and heuristic solution approaches are developed to design the transportation systems. Parking operator plays an important role in determining the passenger travel mode. The capacity and pricing decisions of parking and transit operators are investigated under a game-theoretic framework. A mixed-integer non-linear programming (MINLP) model is formulated to simulate the player\u27s strategy to maximize profits considering endogenous passenger mode choice. A three-step solution heuristic is developed to solve the large-scale MINLP problem. With emerging transportation modes like ride-sharing services and micro-mobility platforms, this thesis aims to co-optimize the integrated transportation system. To improve the mobility for residents in the transit desert regions, we co-optimize the public transit and ride-sharing services to provide a more environment-friendly and equitable system. Similarly, we design an integrated system of public transit and micro-mobility services to provide a more sustainable transportation system in the post-pandemic world

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

Photovoltaic Electric Scooter Charger Dock for the Development of Sustainable Mobility in Urban Environments

[EN] Means and modes of transport in urban environments are changing. The emergence of new means of personal transport, such as e-scooters or e-bikes, combined with new concepts such as `vehicle sharing' are changing urban transport. A greater social awareness of the harmful effects of polluting gases is leading to the adoption of new e-mobility solutions. A sustainable e-scooter recharging dock has been designed, built, and put into operation in a small town north of the city of Valencia (Spain). In the proposed novel solution, a stand-alone PV system is built for the free recharge of e-scooters using an original system that supports new sustainable means of transport. The design of the PV system considers the size limitations of the equipment, where a single PV module must generate the energy needed to recharge the e-scooters. A battery is used to store the energy and adjust power generation and consumption profiles. A commercial electronic converter adjusts the various electrical characteristics of generation, storage, and consumption. As a result of the system analysis, the surplus autonomy provided for the e-scooter recharging dock is calculated. Potential stakeholders in the use of the proposed system and their reasons for adopting this sustainable solution are identified. Experimental results of the first months of operation are included and these demonstrate the correct operation of the proposed system.Martinez-Navarro, A.; Cloquell Ballester, VA.; Segui-Chilet, S. (2020). Photovoltaic Electric Scooter Charger Dock for the Development of Sustainable Mobility in Urban Environments. IEEE Access. 8:169486-169495. https://doi.org/10.1109/ACCESS.2020.3023881S169486169495

Book of abstracts of the 24th Euro Working Group on Transportation Meeting

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