Concepção e planeamento de armazéns

Uma cadeia de abastecimento pode ser considerada como uma rede de entidades individuais que, colectivamente são responsáveis pela gestão de fluxos de materiais e de informação desde os fornecedores até ao consumidor final. Assim, a eficiência e eficácia destas redes é fortemente condicionada pelas operações em cada uma das entidades que a integram. Neste sentido, os armazéns proporcionam uma importante ligação entre fornecedores, distribuidores e consumidores pelo que podem ser considerados uma entidade essencial na gestão da cadeia de abastecimento. A concepção e planeamento de um armazém envolve decisões complexas para as quais são diminutas ferramentas de apoio às decisões envolvidas. A complexidade das decisões envolvidas inclui: enorme quantidade de informação a ser processada; elevado número de possíveis alternativas; a existência de vários objectivos que por vezes são conflituosos bem como a incerteza associada ao fluxo de materiais dentro e fora do armazém. São diminutos os modelos de apoio à decisão que integrem várias decisões envolvidas na concepção e planeamento de armazéns. Esta escassez de modelos integradores que traduzam a complexidade destes sistemas é devida à dificuldade de análise e ao complexo tratamento analítico que lhes é inerente. Nesta comunicação será discutido um modelo que integra algumas das decisões envolvidas na concepção e planeamento de armazéns tais como: gestão de inventários, a atribuição de produtos a diferentes zonas de armazenagem e a alocação dos produtos dentro das áreas de armazenamento. O objectivo é mostrar as potencialidades e as fraquezas do modelo quando aplicado a uma variedade de problemas bem como identificar novas oportunidades de investigação

An optimisation model for the warehouse design and planning problem

In spite of the importance of warehouses in the field of the supply chain management, there is not a single decision model that integrates all the decisions that concerns the warehouse design and planning problem. In this paper we discuss a mathematical programming model aiming to support some warehouse management and inventory decisions. Our aim is to address the complexity related to the modeling of the warehouse design and planning problem. In particular an optimisation model is presented to capture the trade-offs among both inventory and warehouse costs in order to achieve global optimal design satisfying throughput requirements

An optimisation model for the warehouse design and product assignment and allocation problem

Warehouse design and planning is a great challenge in the field of Supply Chain Management. In this paper we discuss an optimisation model aiming to support some warehouse management decisions. In particular a mixed-integer programming model (MILP) is presented to determine product assignment and allocation to the functional areas, as well as the size of each area. Our aim is to capture the trade-offs among the different warehouse costs in order to achieve global optimal design satisfying throughput requirements

Integrated approaches to warehouse planning and operations

In this talk we discuss a tactical model recently available in warehouse literature. The model integrates the replenishment decision in inventory management, the allocation of products to warehousing systems and the assignment of products to storage locations in warehousing management. Our aim is to show the models’ potentialities and weaknesses when applied to a wide variety of problems and to identify challenging research opportunities for developing global warehouse decision support models that fill the gap between researchers and warehouse practitioners

An integrated model for warehouse design and planning

Warehouse design is a field of interest for both practitioners and researchers that have attracted a lot of research attention in the last years. Nevertheless it remains a complex task with very few general models that capture the existing and often conflicting trade-offs of a warehouse system. Literature surveys show that most research efforts have been devoted to solve limited and well-defined problems rather than integrated ones. This is not surprising since developing an integrated model is more difficult to analyse and treat analytically. In this talk we present and discuss a high-level model that integrates some decisions involved in warehouse design and planning. Our aim is to analyse the value of integrating warehouse decisions showing that additional savings can be achieved

GNSS/LiDAR-Based Navigation of an Aerial Robot in Sparse Forests

Autonomous navigation of unmanned vehicles in forests is a challenging task. In such environments, due to the canopies of the trees, information from Global Navigation Satellite Systems (GNSS) can be degraded or even unavailable. Also, because of the large number of obstacles, a previous detailed map of the environment is not practical. In this paper, we solve the complete navigation problem of an aerial robot in a sparse forest, where there is enough space for the flight and the GNSS signals can be sporadically detected. For localization, we propose a state estimator that merges information from GNSS, Attitude and Heading Reference Systems (AHRS), and odometry based on Light Detection and Ranging (LiDAR) sensors. In our LiDAR-based odometry solution, the trunks of the trees are used in a feature-based scan matching algorithm to estimate the relative movement of the vehicle. Our method employs a robust adaptive fusion algorithm based on the unscented Kalman filter. For motion control, we adopt a strategy that integrates a vector field, used to impose the main direction of the movement for the robot, with an optimal probabilistic planner, which is responsible for obstacle avoidance. Experiments with a quadrotor equipped with a planar LiDAR in an actual forest environment is used to illustrate the effectiveness of our approach

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge, it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost