Logística de transporte de valores: uma revisão sistemática da literatura: Cash/valuables-in-transit logistics: a systematic literature review

O transporte de valores envolve a transferência física de notas, moedas, cartões de crédito e itens de valor de um local para outro. Com o contínuo desenvolvimento da economia e o aumento do consumo, a quantidade de dinheiro em circulação desempenha um papel muito importante na movimentação do comércio, e, apesar da existência de diferentes métodos mais modernos de pagamento, é esperado que o uso de numerário ainda se manterá como a principal forma de transação financeira no futuro próximo. Este estudo tem como objetivo identificar e analisar, por meio de uma revisão sistemática da literatura, o estado da arte da pesquisa na área de logística de transporte de valores. Este estudo selecionou 32 artigos para compor a análise. Dados sobre a evolução do uso de dinheiro em espécie no Brasil demonstram um aumento exponencial na sua utilização. A operação logística de transporte de valores apresenta um alto grau de complexidade devido ao fluxo de mão dupla do produto: os fluxos de caixa do Banco Central (BC) para os clientes e dos clientes para o BC. Devido aos riscos físicos envolvidos na operação, que inclui jornadas de trabalho em turnos e um ambiente laboral inseguro, os vigilantes se tornam propensos à riscos de saúde e segurança no trabalho. Apesar de utilizar veículos blindados e equipados, fornecer um transporte seguro e protegido ainda é uma operação desafiadora para as empresas, destacando o papel da logística neste processo. Foi identificado na revisão da literatura que poucos estudos consideram o risco de roubo durante o transporte e propõem abordagens para reduzir este tipo de ameaça.&nbsp

Risk-aware navigation for UAV digital data collection

This thesis studies the navigation task for autonomous UAVs to collect digital data in a risky environment. Three problem formulations are proposed according to different real-world situations. First, we focus on uniform probabilistic risk and assume UAV has unlimited amount of energy. With these assumptions, we provide the graph-based Data-collecting Robot Problem (DRP) model, and propose heuristic planning solutions that consist of a clustering step and a tour building step. Experiments show our methods provide high-quality solutions with high expected reward. Second, we investigate non-uniform probabilistic risk and limited energy capacity of UAV. We present the Data-collection Problem (DCP) to model the task. DCP is a grid-based Markov decision process, and we utilize reinforcement learning with a deep Ensemble Navigation Network (ENN) to tackle the problem. Given four simple navigation algorithms and some additional heuristic information, ENN is able to find improved solutions. Finally, we consider the risk in the form of an opponent and limited energy capacity of UAV, for which we resort to the Data-collection Game (DCG) model. DCG is a grid-based two-player stochastic game where the opponent may have different strategies. We propose opponent modeling to improve data-collection efficiency, design four deep neural networks that model the opponent\u27s behavior at different levels, and empirically prove that explicit opponent modeling with a dedicated network provides superior performance

Municipal solid-waste collection and disposal management using geospatial techniques in Maseru City, Lesotho

The use of geospatial techniques plays a crucial role in solid waste management. Collection and transportation of solid waste must be done in an efficient manner to avoid negative environmental impacts. At the time of study, there are no collection and routing system in Maseru City, leading to haphazard collection and disposal of Municipal Solid Waste (MSW). The aims of the study are: (i) To get an understanding and address the challenges faced by relevant stakeholders in solid waste management for Maseru City, (ii) To minimize adverse environmental impacts due to unscientific location of a disposal site and (iii) To minimize transportation costs and time during collection. The objectives of this study are summarized in the following: assess the current solid waste management, model suitable disposal/dump sites, determine MSW collection points and develop an optimal route for MSW collection and disposal in Maseru City. To assess the current solid waste management, 130 households, 73 community waste pickers, 15 Maseru City Council (MCC) management staff and 3 drivers were interviewed, and relevant data collected. Both primary and secondary data collection methods were used. Primary data collection methods included interviews, questionnaires and observations and creating feature classes in a geo database. Secondary data collection was done from relevant government repositories, digitization, and internet web sites. Simple random, area, cluster, and convenience sampling techniques were applied. Geographical Information Systems (GIS) and Remote sensing techniques were used to carry out suitability and network analysis, and location of MSW collection points. The study found out that the dump site (Ts'osane) was used by MCC and was not suitably located, hence more suitable alternative dump sites have been proposed. However, Ts'osane dump site was adopted in the analysis as it is the one used by MCC at the time of study. The researcher also found out that there were no designated MSW collection points and optimal routes, and that solid waste collection was done by both MCC and CBOs. In this regard, 334 collection points have been determined based on population and generated solid waste per Constituency and were randomly located in the study area. However, due to the policy that within 25m from the road no development could take place, only collection points which fell v within 25m from the road were selected and used in the routing analysis. One truck was used in the analysis, although more trucks could be used as it was at the time of study. For future research, there is a need to research on policy so that criteria for locating solid waste disposal and location of collection points is explicitly specified in the law to be able to conduct scientific analyses. A multi modal network analysis that would include all the vehicles used by MCC and the CBOs to develop a comprehensive network analysis that would also include necessary attributes such as road names, type, class, and length is needed

Risk Assessment and Collaborative Information Awareness for Plan Execution

Joint organizational planning and plan execution in risk-prone environment, has seen renewed research interest given its potential for agility and cost reduction. The participants are often asked to quickly plan and execute tasks in partially known or hostile environments. This requires advanced decision support systems for situational response whereby state-of-the-art technologies can be used to handle issues such as plan risk assessment, appropriate information exchange, asset localization and adaptive planning with risk mitigation. Toward this end, this thesis contributes innovative approaches to address these issues, focusing on logistic support over risk-prone transport network as many organizational plans have key logistic components. Plan risk assessment involves property evaluation for vehicle risk exposure, cost bounds and contingency options assessment. Appropriate information exchange involves participant specific shared information awareness under unreliable communication. Asset localization mandates efficient sensor network management. Adaptive planning with risk mitigation entails limited risk exposure replanning, factoring potential vehicle and cargo loss. In this pursuit, this thesis first investigates risk assessment for asset movement and contingency valuation using probabilistic model-checking and decision trees, followed by elaborating a gossip based protocol for hierarchy-aware shared information awareness, also assessed via probabilistic model-checking. Then, the thesis proposes an evolutionary learning heuristic for efficiently managing sensor networks constrained in terms of sensor range, capacity and energy use. Finally, the thesis presents a learning based heuristic for cost effective adaptive logistic planning with risk mitigation. Instructive case studies are also provided for each contribution along with benchmark results evaluating the performance of the proposed heuristic techniques