3,042 research outputs found

    Optimal logistics scheduling with dynamic information in emergency response: case studies for humanitarian objectives

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    The mathematical model of infectious disease is a typical problem in mathematical modeling, and the common infectious disease models include the susceptible-infected (SI) model, the susceptible-infected-recovered model (SIR), the susceptible-infected-recovered-susceptible model (SIRS) and the susceptible-exposed-infected-recovered (SEIR) model. These models can be used to predict the impact of regional return to work after the epidemic. In this paper, we use the SEIR model to solve the dynamic medicine demand information in humanitarian relief phase. A multistage mixed integer programming model for the humanitarian logistics and transport resource is proposed. The objective functions of the model include delay cost and minimum running time in the time-space network. The model describes that how to distribute and deliver medicine resources from supply locations to demand locations with an efficient and lower-cost way through a transportation network. The linear programming problem is solved by the proposed Benders decomposition algorithm. Finally, we use two cases to calculate model and algorithm. The results of the case prove the validity of the model and algorithm

    On green routing and scheduling problem

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    The vehicle routing and scheduling problem has been studied with much interest within the last four decades. In this paper, some of the existing literature dealing with routing and scheduling problems with environmental issues is reviewed, and a description is provided of the problems that have been investigated and how they are treated using combinatorial optimization tools

    Finding the best tour for travelling salesman problem using artificial ecosystem optimization

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    This paper presents a new method based on the artificial ecosystem optimization (AEO) algorithm for finding the shortest tour of the travelling salesman problem (TSP). Wherein, AEO is a newly developed algorithm based on the idea of the energy flow of living organisms in the ecosystem consisting of production, consumption and decomposition mechanisms. In order to improve the efficiency of the AEO for the TSP problem, the 2-opt movement technique is equipped to enhance the quality of the solutions created by the AEO. The effectiveness of AEO for the TSP problem has been verified on four TSP instances consisting of the 14, 30, 48 and 52 cities. Based on the calculated results and the compared results with the previous methods, the proposed AEO method is one of the effective approaches for solving the TSP problem

    The design and applications of the african buffalo algorithm for general optimization problems

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    Optimization, basically, is the economics of science. It is concerned with the need to maximize profit and minimize cost in terms of time and resources needed to execute a given project in any field of human endeavor. There have been several scientific investigations in the past several decades on discovering effective and efficient algorithms to providing solutions to the optimization needs of mankind leading to the development of deterministic algorithms that provide exact solutions to optimization problems. In the past five decades, however, the attention of scientists has shifted from the deterministic algorithms to the stochastic ones since the latter have proven to be more robust and efficient, even though they do not guarantee exact solutions. Some of the successfully designed stochastic algorithms include Simulated Annealing, Genetic Algorithm, Ant Colony Optimization, Particle Swarm Optimization, Bee Colony Optimization, Artificial Bee Colony Optimization, Firefly Optimization etc. A critical look at these ‘efficient’ stochastic algorithms reveals the need for improvements in the areas of effectiveness, the number of several parameters used, premature convergence, ability to search diverse landscapes and complex implementation strategies. The African Buffalo Optimization (ABO), which is inspired by the herd management, communication and successful grazing cultures of the African buffalos, is designed to attempt solutions to the observed shortcomings of the existing stochastic optimization algorithms. Through several experimental procedures, the ABO was used to successfully solve benchmark optimization problems in mono-modal and multimodal, constrained and unconstrained, separable and non-separable search landscapes with competitive outcomes. Moreover, the ABO algorithm was applied to solve over 100 out of the 118 benchmark symmetric and all the asymmetric travelling salesman’s problems available in TSPLIB95. Based on the successful experimentation with the novel algorithm, it is safe to conclude that the ABO is a worthy contribution to the scientific literature

    Approach to C2F2C (customer to factory to customer) strategy: a case study of the Fanqing Furniture Company

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    Hotel furniture manufacturers, as key components of modern service industry, have become leading service companies concerning China's economic development. Consumer-driven business model and mass customization are becoming important direction of hotel furniture manufacturers’ transformation and upgrade. In a context of fragmented competition and individualized customers’ demand, it is difficult to take advantage of the low cost and high efficiency of mass production, while meeting the customers’ individualized needs. Internet led business increases the difficulty of balancing the offer of large production and customization, because companies face a high cost (Customer to Factory), while the customers’ satisfaction is low (Factory to Customer). Finding a solution to this trade-off is not only a major challenge in the process of company model transformation, but also an important topic that has not yet been studied in depth. Based on Fanqing Hotel Furniture Company’s empirical case of solving the contradiction between individualized demand and mass production, this thesis studies the evolution of hotel furniture manufacturers’ (HFM) innovation ecosystem and the value co-creation mode. The C2F2C (Customer to Factory to Customer) strategy of Fanqing was constructed based on both company innovation ecosystem theory and customer value co-creation theory. By implementing the C2F2C strategy, Fanqing has realized standardization, informatization and lean production, and also fulfilled customers’ needs and improved their satisfaction. The C2F2C strategy also helps to reduce costs and achieve value co-creation between the company and customers. This thesis explores an effective way to improve technological innovation ability and international competitiveness of HFM in China.As empresas de móveis para hotéis constituem um sector importante no desenvolvimento da indústria de serviços modernos, liderando já a indústria no desenvolvimento económico da China. Seguir um modelo de negócio de personalização em larga escala e orientação para o consumidor aponta ser uma direção significativa a tomar para a transformação e inovação das empresas de serviços. Face à concorrência individualizada e fragmentada na procura de clientes da indústria hoteleira, é difícil oferecer ao cliente uma personalização em larga escala, que permita atingir as vantagens de baixo custo e alta eficiência de produção em volume, atendendo simultaneamente à personalização das necessidades de cada cliente. Na comercialização pela internet é mais difícil equilibrar a oferta de uma produção em larga escala e personalizada, porque é elevado o custo em C2F, mas em contrapartida baixo o nível de satisfação do cliente em F2C. Como suporte empírico, esta tese analisou o caso da empresa de Móveis para Hotéis Fanqing, que resolveu a contradição entre procura individualizada e produção de massa em grande escala, permitindo estudar a evolução para um ecossistema inovador e de criação conjunta de valor entre empresas de móveis e clientes nesta industria de mobiliário para hóteis (HFM). A estratégia da relação cliente para fabricante e deste para cliente (C2F2C) da Fanqing foi desenvolvida com base nas teorias da inovação do ecossistema e da criação de valor conjunta. Ao implementar a estratégia de C2F2C, a Fanqing operou tanto a standardização, a informatização e a produção lean, como a satisfação do cliente preenchendo as suas necessidades. A estratégia C2F2C permite reduzir custos e potencia a criação conjunta de valor entre fabricantes e clientes, explorando uma maneira eficaz de melhorar a capacidade de inovação tecnológica e a competitividade internacional das empresas de móveis para hotéis da China

    Strategies for sustainable socio-economic development and mechanisms their implementation in the global dimension

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    The authors of the book have come to the conclusion that it is necessary to effectively use modern approaches to developing and implementation strategies of sustainable socio-economic development in order to increase efficiency and competitiveness of economic entities. Basic research focuses on economic diagnostics of socio-economic potential and financial results of economic entities, transition period in the economy of individual countries and ensuring their competitiveness, assessment of educational processes and knowledge management. The research results have been implemented in the different models and strategies of supply and logistics management, development of non-profit organizations, competitiveness of tourism and transport, financing strategies for small and medium-sized enterprises, cross-border cooperation. The results of the study can be used in decision-making at the level the economic entities in different areas of activity and organizational-legal forms of ownership, ministries and departments that promote of development the economic entities on the basis of models and strategies for sustainable socio-economic development. The results can also be used by students and young scientists in modern concepts and mechanisms for management of sustainable socio-economic development of economic entities in the condition of global economic transformations and challenges

    Multi-stage stochastic optimization and reinforcement learning for forestry epidemic and covid-19 control planning

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    This dissertation focuses on developing new modeling and solution approaches based on multi-stage stochastic programming and reinforcement learning for tackling biological invasions in forests and human populations. Emerald Ash Borer (EAB) is the nemesis of ash trees. This research introduces a multi-stage stochastic mixed-integer programming model to assist forest agencies in managing emerald ash borer insects throughout the U.S. and maximize the public benets of preserving healthy ash trees. This work is then extended to present the first risk-averse multi-stage stochastic mixed-integer program in the invasive species management literature to account for extreme events. Significant computational achievements are obtained using a scenario dominance decomposition and cutting plane algorithm.The results of this work provide crucial insights and decision strategies for optimal resource allocation among surveillance, treatment, and removal of ash trees, leading to a better and healthier environment for future generations. This dissertation also addresses the computational difficulty of solving one of the most difficult classes of combinatorial optimization problems, the Multi-Dimensional Knapsack Problem (MKP). A novel 2-Dimensional (2D) deep reinforcement learning (DRL) framework is developed to represent and solve combinatorial optimization problems focusing on MKP. The DRL framework trains different agents for making sequential decisions and finding the optimal solution while still satisfying the resource constraints of the problem. To our knowledge, this is the first DRL model of its kind where a 2D environment is formulated, and an element of the DRL solution matrix represents an item of the MKP. Our DRL framework shows that it can solve medium-sized and large-sized instances at least 45 and 10 times faster in CPU solution time, respectively, with a maximum solution gap of 0.28% compared to the solution performance of CPLEX. Applying this methodology, yet another recent epidemic problem is tackled, that of COVID-19. This research investigates a reinforcement learning approach tailored with an agent-based simulation model to simulate the disease growth and optimize decision-making during an epidemic. This framework is validated using the COVID-19 data from the Center for Disease Control and Prevention (CDC). Research results provide important insights into government response to COVID-19 and vaccination strategies

    New decision support tools for forest tactical and operational planning

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    Doutoramento em Engenharia Florestal e dos Recursos Florestais - Instituto Superior de AgronomiaThe economic importance of the forest resources and the Portuguese forest-based industries motivated several studies over the last 15 years, particularly on strategic forest planning. This thesis focuses on the forest planning processes at tactical and operational level (FTOP). These problems relate to harvesting, transportation, storing, and delivering the forest products to the mills. Innovative Operation Research methods and Decision Support Systems (DSS) were developed to address some of these problems that are prevalent in Portugal. Specifically, Study I integrates harvest scheduling, pulpwood assortment, and assignment decisions at tactical level. The solution method was based in problem decomposition, combining heuristics and mathematical programming algorithms. Study II presents a solution approach based on Revenue Management principles for the reception of Raw Materials. This operational problem avoids truck congestion during the operation of pulpwood delivery. Study III uses Enterprise Architecture to design a DSS for integrating the operations performed over the pulpwood supply chain. Study IV tests this approach on a toolbox that handled the complexity of the interactions among the agents engaged on forest planning at regional level. Study V proposes an innovative technological framework that combines forest planning with forest operations' control
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