Green Vehicle Routing Optimization Based on Carbon Emission and Multiobjective Hybrid Quantum Immune Algorithm

© 2018 Xiao-Hong Liu et al. Green Vehicle Routing Optimization Problem (GVROP) is currently a scientific research problem that takes into account the environmental impact and resource efficiency. Therefore, the optimal allocation of resources and the carbon emission in GVROP are becoming more and more important. In order to improve the delivery efficiency and reduce the cost of distribution requirements through intelligent optimization method, a novel multiobjective hybrid quantum immune algorithm based on cloud model (C-HQIA) is put forward. Simultaneously, the computational results have proved that the C-HQIA is an efficient algorithm for the GVROP. We also found that the parameter optimization of the C-HQIA is related to the types of artificial intelligence algorithms. Consequently, the GVROP and the C-HQIA have important theoretical and practical significance

Complex Systems: Nonlinearity and Structural Complexity in spatially extended and discrete systems

Resumen Esta Tesis doctoral aborda el estudio de sistemas de muchos elementos (sistemas discretos) interactuantes. La fenomenología presente en estos sistemas esta dada por la presencia de dos ingredientes fundamentales: (i) Complejidad dinámica: Las ecuaciones del movimiento que rigen la evolución de los constituyentes son no lineales de manera que raramente podremos encontrar soluciones analíticas. En el espacio de fases de estos sistemas pueden coexistir diferentes tipos de trayectorias dinámicas (multiestabilidad) y su topología puede variar enormemente dependiendo de dos parámetros usados en las ecuaciones. La conjunción de dinámica no lineal y sistemas de muchos grados de libertad (como los que aquí se estudian) da lugar a propiedades emergentes como la existencia de soluciones localizadas en el espacio, sincronización, caos espacio-temporal, formación de patrones, etc... (ii) Complejidad estructural: Se refiere a la existencia de un alto grado de aleatoriedad en el patrón de las interacciones entre los componentes. En la mayoría de los sistemas estudiados esta aleatoriedad se presenta de forma que la descripción de la influencia del entorno sobre un único elemento del sistema no puede describirse mediante una aproximación de campo medio. El estudio de estos dos ingredientes en sistemas extendidos se realizará de forma separada (Partes I y II de esta Tesis) y conjunta (Parte III). Si bien en los dos primeros casos la fenomenología introducida por cada fuente de complejidad viene siendo objeto de amplios estudios independientes a lo largo de los últimos años, la conjunción de ambas da lugar a un campo abierto y enormemente prometedor, donde la interdisciplinariedad concerniente a los campos de aplicación implica un amplio esfuerzo de diversas comunidades científicas. En particular, este es el caso del estudio de la dinámica en sistemas biológicos cuyo análisis es difícil de abordar con técnicas exclusivas de la Bioquímica, la Física Estadística o la Física Matemática. En definitiva, el objetivo marcado en esta Tesis es estudiar por separado dos fuentes de complejidad inherentes a muchos sistemas de interés para, finalmente, estar en disposición de atacar con nuevas perspectivas problemas relevantes para la Física de procesos celulares, la Neurociencia, Dinámica Evolutiva, etc..

Transportation DistancesReductionat Surabaya Distribution Center Using Anylogistix Software

The delivery method of finished goods by the company Surabaya Distribution Center is to deliver to one customer location (single drop), and the truck size used will be adjusted based on the demand. The company considers the single drop inefficient since the usage of small trucks is significantly higher than that of larger trucks. Therefore, it is essential to change finished goods previously delivered using small modes of transportation to large ones. Since customer demand cannot be controlled, increasing the truck size could be achieved by merging several destinations in a truck (multidrop), reducingtransportation distance. The problem of determining the optimal route to reduce the delivery route distance by considering the vehicle capacity is included in the Capacitated Vehicle Routing Problem with Time Windows(CVRPTW), which can be solved using Anylogistix. Anylogistix software was selected as a software tool for applying the simulation modelingapproach based on the functionality, accessibility, simplicity, and convenience of use, as well as the degree of suitability of the models to the conditions of reality. The simulation begins with selecting the appropriate type of simulation, inputting data, applying research assumptions, and verifying and validating until a verified and validated simulation is obtained. Through a systematic approach, the Anylogistix simulationcan reduce the distance of the routes, initially 280,258.7 km to 203,905.93 km (27.2% distance reduction). In addition, the results showed that the delivery of goods from small modes of transportation allocated to large modes of transportation was 181 shipments with optimal utilization of >70

복잡계 네트워크에서 확산 현상의 예측 및 제어

학위논문(박사) -- 서울대학교대학원 : 자연과학대학 물리·천문학부(물리학전공), 2023. 8. 백용주.지난 20년간 복잡계 네트워크의 창발현상에 대해 많은 연구가 이루어져왔다. 이런 현상의 예측과 제어는 복잡계 과학에서 중요한 주제이다. 복잡계 네트워크의 그래프 표현은 이런 주제를 효과적으로 다룬다. 복잡계 중에서는 두개 이상의 개체들이 동시에 상호작용하는 경우가 있다. 예를 들어 두명 이상의 연구자가 동시에 협업을 할 수 있다. 네트워크의 엣지를 통한 전파는 단순한 전파라 불린다. 단순한 전파과정으로 나타낼 수 없는 많은 현상들이 있다. 그 예로는 하이퍼그래프 전파과정, 양자 전파과정, 그리고 사회기반시설에서의 연쇄파멸현상이 있다. 구체적으로 이 학위논문에서는 복잡한 전파과정의 예측과 제어를 다룬다. 하이퍼그래프에서의 전염병 전파 모델인 simplicial SIS 모델의 상전이와 제어 전략을 다룬다. 또한 불균일한 치명률을 가진 인구분포에서 최적 백신 전략의 상전이에 대해서도 다룬다. 추가로 degree 분포가 균일한 네트워크와 불균일한 네트워크에서의 양자 상전이에 대해서도 연구한다. 마지막으로 기계학습을 적용하여 전염병 전파와 연쇄파멸현상을 예측하고 제어한 연구에 대해 소개한다.In past decades, extensive research has been done on emerging phenomena in complex systems. An important issue for such emerging phenomena is their prediction and control. Complex networks represented by graphs enable researchers to study such issues successfully. In complex systems, however, interactions among constituents can be more complex than pairwise. For instance, more than two people can collaborate on a team. Contagion through an edge in a network is called simple contagion. There are contagion processes that cannot be reduced to simple contagions. Examples are hypergraph epidemic processes, quantum spreading processes, and cascading failures in infrastructure networks. In this dissertation, we study the prediction and control of these complex contagion processes. We study the phase transition and control strategy of the simplicial SIS model, which is an epidemic model in hypergraphs. We then study the transition of vaccination strategy in a population with heterogeneous fatality rates. Moreover, we study the phase transition of quantum spreading processes in homogeneous and heterogeneous networks. Lastly, we employ machine learning for the prediction and control of epidemic spreading and cascading failures in infrastructure networks.Abstract i Contents ii List of Figures v List of Tables xxiii 1 Introduction 1 1.1 Complex Network 1 1.2 Complex contagion 2 1.3 Overview of dissertation 3 2 Higher-order epidemics 5 2.1 Phase transition and critical phenomena of the simplicial susceptibleinfected- susceptible (s-SIS) model 5 2.2 Static model of uniform hypergraph 7 2.3 Simplicial SIS model 9 2.4 Heterogeneous mean-field theory (annealed approximation) 11 2.4.1 Self-consistency equation 11 2.5 Phase transition and critical behavior 14 2.5.1 Order parameter 14 2.5.2 Susceptibility 18 2.5.3 Correlation size 19 2.6 Numerical simulations 22 2.6.1 Numerical methods 22 2.6.2 Numerical results 24 2.7 Degree distribution of static model 30 2.8 Asymptotic behavior of G′(Θ) 30 2.9 Susceptibility 31 2.10 Containment strategy for simplicial SIS model 32 2.10.1 Hypergraph popularity-similarity optimization (h-PSO) model 35 2.10.2 Individual- and pair-based mean-field theories 37 2.10.3 Immunization strategies 41 2.10.4 Numerical Results 45 2.11 Summary and conclusion 50 3 Phase transition in vaccination strategy 53 3.1 Introduction 53 3.2 Susceptible-infected-recovered-dead (SIRD) model 55 3.3 Results 56 3.3.1 Fatality- and contact-based strategies 56 3.3.2 Transition and path-dependency of the optimal vaccination strategy 59 3.3.3 Real-world epidemic diseases 63 3.3.4 Complex epidemic stages, vaccine breakthrough infection, and reinfection 66 3.4 Conclusion 68 4 Application of graph neural network (GNN) on spreading processes 70 4.1 Introduction 70 4.2 Prediction and mitigation of avalanche dynamics in power grids using graph neural network 70 4.2.1 Avalanche dynamics 72 4.2.2 Avalanche mitigation strategy 75 4.2.3 Graph neural network (GNN) 78 4.2.4 Conclusion 84 4.3 Epidemic control using graph neural network ansatz 86 4.3.1 Model 88 4.3.2 Vaccination strategy 91 4.3.3 Results 97 4.3.4 Conclusion 103 5 Quantum spreading processes in complex networks 105 5.1 Introduction 105 5.2 Permutational symmetry 109 5.3 Quantum contact process 111 5.4 Dissipative Transverse Ising model 114 5.4.1 Transverse Ising model 114 5.4.2 Dissipative transverse Ising model 117 5.5 Comparison with quantum jump Monte Carlo simulation 127 5.6 Quantum contact process in scale-free networks 129 5.6.1 Annealed approximation and self-consistency equation 129 5.6.2 Phase transition and critical behavior 132 5.6.3 Numerical results 138 5.7 Summary and Discussion 141 6 Conclusion 145 Bibliography 146 Abstract in Korean 188박

Last Mile Distribution of COVID-19 Vaccines: A Cold Chain Logistical Challenge

The COVID-19 pandemic is a global health and humanitarian crisis that has wreaked havoc on economies and industries around the world. This study aims to address the distribution of the COVID-19 vaccines at the last mile by evaluating the vaccine supply chain and how it can be effectively utilized to address the last mile distribution of the COVID-19 vaccines through simulation. The first part includes a systematic literature review and bibliometric study of vaccine supply chain and cold chain logistics studies conducted in the last decade. The second part examines the distribution of COVID-19 vaccines in Norway as a case study. The study develops a two-stage optimization simulation method to analyse and improve the logistical performance of the COVID-19 vaccine distribution in Inland County, Norway. The study analyses the impact of fleet size and the use of heterogeneous vehicles in the last mile distribution network on some key performance indicators. The findings from the study reveal that the service level, transportation costs and environmental performance of the vaccine logistics system are significantly influenced by routing decisions, fleet size, fleet composition and the types of heterogeneous vehicles used. Based on the findings from the study, some managerial insights are outlined to help logistics managers better understand the interactions between the key parameters of a cold chain vaccine distribution system

Optimal Design and Operation of WHO-EPI Vaccine Distribution Chains

Vaccination has been proven to be the most effective method to prevent infectious diseases and in 1974 the World Health Organization (WHO) established the Expanded Programme on Immunization (EPI) to provide universal access to all important vaccines for all children, with a special focus on underserved low- and middle-income countries. However, there are still roughly 20 million infants worldwide who lack access to routine immunization services and remain at risk, and millions of additional deaths could be avoided if global vaccination coverage could improve. The broad goal of this research is to optimize the design and operation of the WHO-EPI vaccine distribution chain in these underserved low- and middle-income countries. We first present a network design problem for a general WHO-EPI vaccine distribution network by developing a mathematical model that formulates the network design problem as a mixed integer program (MIP). We then present three algorithms for typical problems that are too large to be solved using commercial MIP software. We test the algorithms using data derived from four different countries in sub-Saharan Africa and show that with our final algorithm, high-quality solutions are obtained for even the largest problems within a few minutes. We then discuss the problem of outreach to remote population centers when resources are limited and direct clinic service is unavailable. A set of these remote population centers is chosen, and over an appropriate planning period, teams of clinicians and support personnel are sent from a depot to set up mobile clinics at these locations to vaccinate people there and in the immediate surrounding area. We formulate the problem of designing outreach efforts as an MIP that is a combination of a set covering problem and a vehicle routing problem. We then incorporate uncertainty to study the robustness of the worst-case solutions and the related issue of the value of information. Finally, we study a variation of the outreach problem that combines Set Covering and the Traveling Salesmen Problem and provides an MIP formulation to solve the problem. Motivated by applications where the optimal policy needs to be updated on a regular basis and where repetitively solving this via MIP can be computationally expensive, we propose a machine learning approach to effectively deal with this problem by providing an opportunity to learn from historical optimal solutions that are derived from the MIP formulation. We also present a case study on outreach operations and provide numerical results. Our results show that while the novel machine learning based mechanism generates high quality solution repeatedly for problems that resemble instances in the training set, it does not generalize as well on a different set of optimization problems. These mixed results indicate that there are promising research opportunities to use machine learning to achieve tractability and scalability

Information dissemination in mobile networks

This thesis proposes some solutions to relieve, using Wi-Fi wireless networks, the data consumption of cellular networks using cooperation between nodes, studies how to make a good deployment of access points to optimize the dissemination of contents, analyzes some mechanisms to reduce the nodes' power consumption during data dissemination in opportunistic networks, as well as explores some of the risks that arise in these networks. Among the applications that are being discussed for data off-loading from cellular networks, we can find Information Dissemination in Mobile Networks. In particular, for this thesis, the Mobile Networks will consist of Vehicular Ad-hoc Networks and Pedestrian Ad-Hoc Networks. In both scenarios we will find applications with the purpose of vehicle-to-vehicle or pedestrian-to-pedestrian Information dissemination, as well as vehicle-to-infrastructure or pedestrian-to-infrastructure Information dissemination. We will see how both scenarios (vehicular and pedestrian) share many characteristics, while on the other hand some differences make them unique, and therefore requiring of specific solutions. For example, large car batteries relegate power saving techniques to a second place, while power-saving techniques and its effects to network performance is a really relevant issue in Pedestrian networks. While Cellular Networks offer geographically full-coverage, in opportunistic Wi-Fi wireless solutions the short-range non-fullcoverage paradigm as well as the high mobility of the nodes requires different network abstractions like opportunistic networking, Disruptive/Delay Tolerant Networks (DTN) and Network Coding to analyze them. And as a particular application of Dissemination in Mobile Networks, we will study the malware spread in Mobile Networks. Even though it relies on similar spreading mechanisms, we will see how it entails a different perspective on Dissemination

Sustainable Assessment in Supply Chain and Infrastructure Management

In the competitive business environment or public domain, the sustainability assessment in supply chain and infrastructure management are important for any organization. Organizations are currently striving to improve their sustainable strategies through preparedness, response, and recovery because of increasing competitiveness, community, and regulatory pressure. Thus, it is necessary to develop a meaningful and more focused understanding of sustainability in supply chain management and infrastructure management practices. In the context of a supply chain, sustainability implies that companies identify, assess, and manage impacts and risks in all the echelons of the supply chain, considering downstream and upstream activities. Similarly, the sustainable infrastructure management indicates the ability of infrastructure to meet the requirements of the present without sacrificing the ability of future generations to address their needs. The complexities regarding sustainable supply chain and infrastructure management have driven managers and professionals to seek different solutions. This Special Issue aims to provide readers with the most recent research results on the aforementioned subjects. In addition, it offers some solutions and also raises some questions for further research and development toward sustainable supply chain and infrastructure management