Antecipação na tomada de decisão com múltiplos critérios sob incerteza

Orientador: Fernando José Von ZubenTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: A presença de incerteza em resultados futuros pode levar a indecisões em processos de escolha, especialmente ao elicitar as importâncias relativas de múltiplos critérios de decisão e de desempenhos de curto vs. longo prazo. Algumas decisões, no entanto, devem ser tomadas sob informação incompleta, o que pode resultar em ações precipitadas com consequências imprevisíveis. Quando uma solução deve ser selecionada sob vários pontos de vista conflitantes para operar em ambientes ruidosos e variantes no tempo, implementar alternativas provisórias flexíveis pode ser fundamental para contornar a falta de informação completa, mantendo opções futuras em aberto. A engenharia antecipatória pode então ser considerada como a estratégia de conceber soluções flexíveis as quais permitem aos tomadores de decisão responder de forma robusta a cenários imprevisíveis. Essa estratégia pode, assim, mitigar os riscos de, sem intenção, se comprometer fortemente a alternativas incertas, ao mesmo tempo em que aumenta a adaptabilidade às mudanças futuras. Nesta tese, os papéis da antecipação e da flexibilidade na automação de processos de tomada de decisão sequencial com múltiplos critérios sob incerteza é investigado. O dilema de atribuir importâncias relativas aos critérios de decisão e a recompensas imediatas sob informação incompleta é então tratado pela antecipação autônoma de decisões flexíveis capazes de preservar ao máximo a diversidade de escolhas futuras. Uma metodologia de aprendizagem antecipatória on-line é então proposta para melhorar a variedade e qualidade dos conjuntos futuros de soluções de trade-off. Esse objetivo é alcançado por meio da previsão de conjuntos de máximo hipervolume esperado, para a qual as capacidades de antecipação de metaheurísticas multi-objetivo são incrementadas com rastreamento bayesiano em ambos os espaços de busca e dos objetivos. A metodologia foi aplicada para a obtenção de decisões de investimento, as quais levaram a melhoras significativas do hipervolume futuro de conjuntos de carteiras financeiras de trade-off avaliadas com dados de ações fora da amostra de treino, quando comparada a uma estratégia míope. Além disso, a tomada de decisões flexíveis para o rebalanceamento de carteiras foi confirmada como uma estratégia significativamente melhor do que a de escolher aleatoriamente uma decisão de investimento a partir da fronteira estocástica eficiente evoluída, em todos os mercados artificiais e reais testados. Finalmente, os resultados sugerem que a antecipação de opções flexíveis levou a composições de carteiras que se mostraram significativamente correlacionadas com as melhorias observadas no hipervolume futuro esperado, avaliado com dados fora das amostras de treinoAbstract: The presence of uncertainty in future outcomes can lead to indecision in choice processes, especially when eliciting the relative importances of multiple decision criteria and of long-term vs. near-term performance. Some decisions, however, must be taken under incomplete information, what may result in precipitated actions with unforeseen consequences. When a solution must be selected under multiple conflicting views for operating in time-varying and noisy environments, implementing flexible provisional alternatives can be critical to circumvent the lack of complete information by keeping future options open. Anticipatory engineering can be then regarded as the strategy of designing flexible solutions that enable decision makers to respond robustly to unpredictable scenarios. This strategy can thus mitigate the risks of strong unintended commitments to uncertain alternatives, while increasing adaptability to future changes. In this thesis, the roles of anticipation and of flexibility on automating sequential multiple criteria decision-making processes under uncertainty are investigated. The dilemma of assigning relative importances to decision criteria and to immediate rewards under incomplete information is then handled by autonomously anticipating flexible decisions predicted to maximally preserve diversity of future choices. An online anticipatory learning methodology is then proposed for improving the range and quality of future trade-off solution sets. This goal is achieved by predicting maximal expected hypervolume sets, for which the anticipation capabilities of multi-objective metaheuristics are augmented with Bayesian tracking in both the objective and search spaces. The methodology has been applied for obtaining investment decisions that are shown to significantly improve the future hypervolume of trade-off financial portfolios for out-of-sample stock data, when compared to a myopic strategy. Moreover, implementing flexible portfolio rebalancing decisions was confirmed as a significantly better strategy than to randomly choosing an investment decision from the evolved stochastic efficient frontier in all tested artificial and real-world markets. Finally, the results suggest that anticipating flexible choices has lead to portfolio compositions that are significantly correlated with the observed improvements in out-of-sample future expected hypervolumeDoutoradoEngenharia de ComputaçãoDoutor em Engenharia Elétric

The multi-vehicle stochastic-dynamic inventory routing problem for bike sharing systems

We address the operational management of station-based bike sharing systems (BSSs). In BSSs, users can spontaneously rent and return bikes at any stations in the system. Demand is driven by commuter, shopping, and leisure activities. This demand constitutes a regular pattern of bike usage over the course of the day but also shows a significant short-term uncertainty. Due to the heterogeneity and the uncertainty in demand, stations may run out of bikes or congest during the day. At empty stations, no rental demand can be served. At full stations, no return demand can be served. To avoid unsatisfied demand, providers dynamically relocate bikes between stations in reaction of current shortages or congestion, but also in anticipation of potential future demand. For this real-time decision problem, we present a method that anticipates potential future demands based on historical observations and that coordinates the fleet of vehicles accordingly. We apply our method for two case studies based on real-world data of the BSSs in Minneapolis and San Francisco. We show that our policy outperforms benchmark policies from the literature. Moreover, we analyze how the interplay between anticipation and coordination is essential for the successful operational management of BSSs. Finally, we reveal that the value of coordination and anticipation based on the demand-structure of the BSS under consideration

Dynamic vehicle routing problems: Three decades and counting

Since the late 70s, much research activity has taken place on the class of dynamic vehicle routing problems (DVRP), with the time period after year 2000 witnessing a real explosion in related papers. Our paper sheds more light into work in this area over more than 3 decades by developing a taxonomy of DVRP papers according to 11 criteria. These are (1) type of problem, (2) logistical context, (3) transportation mode, (4) objective function, (5) fleet size, (6) time constraints, (7) vehicle capacity constraints, (8) the ability to reject customers, (9) the nature of the dynamic element, (10) the nature of the stochasticity (if any), and (11) the solution method. We comment on technological vis-à-vis methodological advances for this class of problems and suggest directions for further research. The latter include alternative objective functions, vehicle speed as decision variable, more explicit linkages of methodology to technological advances and analysis of worst case or average case performance of heuristics.© 2015 Wiley Periodicals, Inc

An Approach to Evaluate a Supply Chain Network

Designing the supply chain network (SCN) is the first step to creating a chain sourcing for results. The process identifies the change that will differentiate an organization from its competitors, to contact a customer with a successful value proposition, reduce costs and boost profitability. The most effective way to ensure perfect fluidity is to appoint an employee responsible for supervising the entire process. The manager will inform and coordinate the activities of the heads of different departments, from shipping to sales, focusing on communication and identification of potential problems, as well as correcting faults before they lead to disruption. This paper proposes an evaluation approach for the supply chain network design problems under uncertainty. Existing approaches to this problem are either the deterministic environments or can only address a modest number of scenarios for the uncertain problem parameters. Our solution approach integrates both features; the collective evaluation and the selection of one

Applying Revenue Management to the Reverse Supply Chain

We study the disposition decision for product returns in a closed-loop supply chain. Motivated by the asset recovery process at IBM, we consider two disposition alternatives. Returns may be either refurbished for reselling or dismantled for spare parts. Reselling a refurbished unit typically yields higher unit margins. However, demand is uncertain. A common policy in many firms is to rank disposition alternatives by unit margins. We show that a revenue management approach to the disposition decision which explicitly incorporates demand uncertainty can increase profits significantly. We discuss analogies between the disposition problem and the classical airline revenue management problem. We then develop single period and multi-period stochastic optimization models for the disposition problem. Analyzing these models, we show that the optimal allocation balances expected marginal profits across the disposition alternatives. A detailed numerical study reveals that a revenue management approach to the disposition problem significantly outperforms the current practice of focusing exclusively on high-margin options, and we identify conditions under which this improvement is the highest. We also show that the value recovered from the returned products critically depends on the coordination between forward and reverse supply chain decisions.remanufacturing;revenue management;onderdelen;revenues;spare parts inventory

A unified race algorithm for offline parameter tuning

This paper proposes uRace, a unified race algorithm for efficient offline parameter tuning of deterministic algorithms. We build on the similarity between a stochastic simulation environment and offline tuning of deterministic algorithms, where the stochastic element in the latter is the unknown problem instance given to the algorithm. Inspired by techniques from the simulation optimization literature, uRace enforces fair comparisons among parameter configurations by evaluating their performance on the same training instances. It relies on rapid statistical elimination of inferior parameter configurations and an increasingly localized search of the parameter space to quickly identify good parameter settings. We empirically evaluate uRace by applying it to a parameterized algorithmic framework for loading problems at ORTEC, a global provider of software solutions for complex decision-making problems, and obtain competitive results on a set of practical problem instances from one of the world's largest multinationals in consumer packaged goods

Maintenance models applied to wind turbines. A comprehensive overview

Producción CientíficaWind power generation has been the fastest-growing energy alternative in recent years, however, it still has to compete with cheaper fossil energy sources. This is one of the motivations to constantly improve the efficiency of wind turbines and develop new Operation and Maintenance (O&M) methodologies. The decisions regarding O&M are based on different types of models, which cover a wide range of scenarios and variables and share the same goal, which is to minimize the Cost of Energy (COE) and maximize the profitability of a wind farm (WF). In this context, this review aims to identify and classify, from a comprehensive perspective, the different types of models used at the strategic, tactical, and operational decision levels of wind turbine maintenance, emphasizing mathematical models (MatMs). The investigation allows the conclusion that even though the evolution of the models and methodologies is ongoing, decision making in all the areas of the wind industry is currently based on artificial intelligence and machine learning models

A Computational Approach to Patient Flow Logistics in Hospitals

Scheduling decisions in hospitals are often taken in a decentralized way. This means that different specialized hospital units decide autonomously on e.g. patient admissions and schedules of shared resources. Decision support in such a setting requires methods and techniques that are different from the majority of existing literature in which centralized models are assumed. The design and analysis of such methods and techniques is the focus of this thesis. Specifically, we develop computational models to provide dynamic decision support for hospital resource management, the prediction of future resource occupancy and the application thereof. Hospital resource management targets the efficient deployment of resources like operating rooms and beds. Allocating resources to hospital units is a major managerial issue as the relationship between resources, utilization and patient flow of different patient groups is complex. The issues are further complicated by the fact that patient arrivals are dynamic and treatment processes are stochastic. Our approach to providing decision support combines techniques from multi-agent systems and computational intelligence (CI). This combination of techniques allows to properly consider the dynamics of the problem while reflecting the distributed decision making practice in hospitals. Multi-agent techniques are used to model multiple hospital care units and their decision policies, multiple patient groups with stochastic treatment processes and uncertain resource availability due to overlapping patient treatment processes. The agent-based model closely resembles the real-world situation. Optimization and learning techniques from CI allow for designing and evaluating improved (adaptive) decision policies for the agent-based model, which can then be implemented easily in hospital practice. In order to gain insight into the functioning of this complex and dynamic problem setting, we developed an agent-based model for the hospital care units with their patients. To assess the applicability of this agent-based model, we developed an extensive simulation. Several experiments demonstrate the functionality of the simulation and show that it is an accurate representation of the real world. The simulation is used to study decision support in resource management and patient admission control. To further improve the quality of decision support, we study the prediction of future hospital resource usage. Using prediction, the future impact of taking a certain decision can be taken into account. In the problem setting at hand for instance, predicting the resource utilization resulting from an admission decision is important to prevent future bottlenecks that may cause the blocking of patient flow and increase patient waiting times. The methods we investigate for the task of prediction are forward simulation and supervised learning using neural networks. In an extensive analysis we study the underlying probability distributions of resource occupancy and investigate, by stochastic techniques, how to obtain accurate and precise prediction outcomes. To optimize resource allocation decisions we consider multiple criteria that are important in the hospital problem setting. We use three conflicting objectives in the optimization: maximal patient throughput, minimal resource costs and minimal usage of back-up capacity. All criteria can be taken into account by finding decision policies that have the best trade-off between the criteria. We derived various decision policies that partly allow for adaptive resource allocations. The design of the policies allows the policies to be easily understandable for hospital experts. Moreover, we present a bed exchange mechanism that enables a realistic implementation of these adaptive policies in practice. In our optimization approach, the parameters of the different decision policies are determined using a multiobjective evolutionary algorithm (MOEA). Specifically, the MOEA optimizes the output of the simulation (i.e. the three optimization criteria) as a function of the policy parameters. Our results on resource management show that the benchmark allocations obtained from a case study are considerably improved by the optimized decision policies. Furthermore, our results show that using adaptive policies can lead to better results and that further improvements may be obtained by integrating prediction into a decision policy

Toward a Resilient Holistic Supply Chain Network System: Concept, Review and Future Direction

The recent financial crisis and other major crises have suggested that there are some strong interactions and interdependence between several supply chains and their external environments in various ways. A set of supply chains that are interdependent is called a holistic supply chain network (H-SCN) in this paper. There is a need to focus on building the resilience (in short, the ability of a system to recover from damage or disruption) of an entire H-SCN as it is believed that such a network system is strongly relevant to the recent economic recession that is triggered by financial crises. The objectives of this paper are to provide a classification of different SCNs in literature, leading to the identification of a new type of SCN system, i.e., an H-SCN, and to discuss the state of knowledge on the resilience of SCNs, particularly of an H-SCN. A systematic review approach is applied in this paper. Another contribution of this paper is the provision of a more comprehensive definition and description of resilient systems, including SCN systems. A final contribution of this paper is the proposal of the future directions of research on resilient SCN systems, particularly resilient H-SCN systems.postprin

Abordagem preditiva e adaptativa de gestão operacional aplicada à cadeia de suprimentos do varejo Omni-channel

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia de Produção, Florianópolis, 2020.A evolução tecnológica e a digitalização possibilitam a comercialização de produtos através de múltiplos canais e plataformas de forma integrada, propiciando a gestão de varejo omnichannel. Esse processo contínuo de integração das tecnologias digitais/virtuais aos processos gerenciais físicos dos diversos canais influencia na interação das organizações com os clientes. O comportamento de consumo dos clientes é influenciado em decorrência do aumento da conveniência, tornando, contudo, a gestão operacional das cadeias de suprimentos do varejo mais complexa. Para a gestão da cadeia de suprimentos de varejo omni-channel a complexidade reside na incerteza, oscilações no volume de vendas e incompatibilidade entre oferta e demanda. Para lidar com essa complexidade é necessária a adoção de abordagens inovadoras relacionadas a tecnologias de informação e métodos de decisão inteligentes, destacados pela indústria 4.0. No entanto, ainda faltam pesquisas sobre a conexão entre os mundos digital e real, principalmente quando se trata de cadeias de suprimentos de varejo omni-channel, que se baseiam na integração de fluxos e atividades multicanais para melhor atender ao consumidor. Neste contexto, esta pesquisa tem como objetivo propor uma abordagem preditiva e adaptativa para a gestão operacional combinando aprendizado de máquina para minimizar a incerteza, e otimização baseada em simulação para lidar com a sincronização entre oferta e demanda, aplicada à cadeia de suprimentos do varejo omni-channel. Para isso foram identificados os métodos de aprendizado de máquina, de simulação e de otimização aplicados à cadeia de suprimentos e a indústria 4.0 com o intuito de apoiar a escolha do método de redes neurais e da otimização baseada em simulação por meio do algoritmo genético. O método de redes neurais e a otimização baseada em simulação foram analisados por meio de aplicação de um caso teste, visando identificar a aplicabilidade do método levantado na literatura, na gestão operacional da cadeia de suprimentos varejista omni-channel. Em seguida, a abordagem preditiva e adaptativa é aplicada a uma empresa varejista brasileira e como resultado um modelo de gerenciamento operacional de demanda e suprimentos é proposto para a cadeia de suprimentos varejista omnichannel. Os resultados da aplicação do modelo evidenciaram uma redução dos custos da cadeia de suprimentos, do tempo de entrega dos produtos e da quantidade de pedidos provenientes da incompatibilidade de oferta-demanda. Dessa forma, a tese possibilitou a redução das incertezas proveniente da previsão de demanda, redução da falta de produtos na cadeia, e consequentemente um melhor gerenciamento da distribuição da cadeia de suprimentos.Abstract: Technological evolution and digitalization enable the commercialization of products through multiple channels and platforms in an integrated way, providing omni-channel retail management. This ongoing process of integrating digital / virtual technologies into the physical management processes of the various channels influences the interaction of organizations with customers. Customer consumption behavior is influenced by the increase in convenience, however, making the operational management of retail supply chains more complex. For the management of the omni-channel retail supply chain the complexity lies in uncertainty, fluctuations in sales volume and incompatibility between supply and demand. To address this complexity, it is necessary to adopt innovative approaches related to information technologies and intelligent decision methods, highlighted by industry 4.0. However, there is still a lack of research on the connection between the digital and real worlds, especially when it comes to omni-channel retail supply chains, which are based on the integration of multi-channel flows and activities to better serve the consumer. In this context, this research aims to propose a predictive and adaptive approach to operational management combining machine learning to minimize uncertainty, and simulation-based optimization to deal with synchronization between supply and demand, applied to the omni-channel retail supply chain. For this, the machine learning, simulation and optimization methods applied to the supply chain and industry 4.0 were identified in order to support the choice of neural networks method and simulation-based optimization through the genetic algorithm. The neural networks method and the simulationbased optimization were analyzed by applying a test case, aiming to identify the applicability of the method raised in the literature, in the operational management of the omni-channel retail supply chain. The predictive and adaptive approach is then applied to a Brazilian retail company and as a result an operational demand and supply management model is proposed for the omnichannel retail supply chain. The results of the model application showed a reduction in the supply chain costs, in the products fulfillment time and in the quantity of orders resulting from the incompatibility of supply and demand. In this way, the thesis allowed reduce uncertainties arising from demand forecasting, reduce product shortages in the chain, and thereby better manage supply chain distribution