791 research outputs found

    Hospital-Wide Inpatient Flow Optimization

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    An ideal that supports quality and delivery of care is to have hospital operations that are coordinated and optimized across all services in real-time. As a step toward this goal, we propose a multistage adaptive robust optimization approach combined with machine learning techniques. Informed by data and predictions, our framework unifies the bed assignment process across the entire hospital and accounts for present and future inpatient flows, discharges as well as bed requests – from the emergency department, scheduled surgeries and admissions, and outside transfers. We evaluate our approach through simulations calibrated on historical data from a large academic medical center. For the 600-bed institution, our optimization model was solved in seconds, reduced off-service placement by 24% on average, and boarding delays in the emergency department and post-anesthesia units by 35% and 18% respectively. We also illustrate the benefit from using adaptive linear decision rules instead of static assignment decisions

    Optimal robust inventory management with volume flexibility: matching capacity and demand with the lookahead peak-shaving policy

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    We study inventory control with volume flexibility: A firm can replenish using period-dependent base capacity at regular sourcing costs and access additional supply at a premium. The optimal replenishment policy is characterized by two period-dependent base-stock levels but determining their values is not trivial, especially for nonstationary and correlated demand. We propose the Lookahead Peak-Shaving policy that anticipates and peak shaves orders from future peak-demand periods to the current period, thereby matching capacity and demand. Peak shaving anticipates future order peaks and partially shifts them forward. This contrasts with conventional smoothing, which recovers the inventory deficit resulting from demand peaks by increasing later orders. Our contribution is threefold. First, we use a novel iterative approach to prove the robust optimality of the Lookahead Peak-Shaving policy. Second, we provide explicit expressions of the period-dependent base-stock levels and analyze the amount of peak shaving. Finally, we demonstrate how our policy outperforms other heuristics in stochastic systems. Most cost savings occur when demand is nonstationary and negatively correlated, and base capacities fluctuate around the mean demand. Our insights apply to several practical settings, including production systems with overtime, sourcing from multiple capacitated suppliers, or transportation planning with a spot market. Applying our model to data from a manufacturer reduces inventory and sourcing costs by 6.7%, compared to the manufacturer's policy without peak shaving.info:eu-repo/semantics/publishedVersio

    An Optimistic-Robust Approach for Dynamic Positioning of Omnichannel Inventories

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    We introduce a new class of data-driven and distribution-free optimistic-robust bimodal inventory optimization (BIO) strategy to effectively allocate inventory across a retail chain to meet time-varying, uncertain omnichannel demand. While prior Robust optimization (RO) methods emphasize the downside, i.e., worst-case adversarial demand, BIO also considers the upside to remain resilient like RO while also reaping the rewards of improved average-case performance by overcoming the presence of endogenous outliers. This bimodal strategy is particularly valuable for balancing the tradeoff between lost sales at the store and the costs of cross-channel e-commerce fulfillment, which is at the core of our inventory optimization model. These factors are asymmetric due to the heterogenous behavior of the channels, with a bias towards the former in terms of lost-sales cost and a dependence on network effects for the latter. We provide structural insights about the BIO solution and how it can be tuned to achieve a preferred tradeoff between robustness and the average-case. Our experiments show that significant benefits can be achieved by rethinking traditional approaches to inventory management, which are siloed by channel and location. Using a real-world dataset from a large American omnichannel retail chain, a business value assessment during a peak period indicates over a 15% profitability gain for BIO over RO and other baselines while also preserving the (practical) worst case performance

    Mitigating Supply Chain Disruptions in Retail Discount Department Stores

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    Supply chain disruptions can have adverse effects on business outcomes. Retail industry supply chain leaders are concerned with supply chain disruptions because supply chain disruptions can lead to dissatisfied customers and loss of profits. Grounded in game theory, the purpose of this multiple case study was to explore strategies four retail industry supply chain leaders in Northern Illinois and Northwest Indiana implemented to mitigate the effects of supply chain disruptions. Data were collected using semistructured virtual interviews with four retail industry supply chain leaders and a review of company documents. Through thematic analysis, four themes emerged: (a) choosing appropriate inventory strategy, (b) determining facility capacities, (c) conducting ongoing evaluation and control of cost, and (d) monitoring customer satisfaction. A key recommendation is for retail industry supply chain leaders to increase safety stock to mitigate the effects of supply chain disruptions. The implications for positive social change include the potential to improve job stability for employees and support economic growth in local communities

    Judgmental forecasting: Factors affecting lay people's expectations of inflation

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    In this thesis, laypeople’s judgmental forecasting about inflation is reviewed and experimentally explored in six chapters. Inflation is defined as the Consumer Price Index (CPI) across the whole thesis. In Chapter 1, I review work on the formation of inflation expectations, drawing mainly from the economic literature. In Chapter 2, I review research on judgmental forecasting, drawing mainly from the literature in cognitive psychology and management science. In Chapter 3, three experiments are presented that were designed to determine how and when people employ internal information of experienced price changes to form inflation expectations. In Chapter 4, three experiments are used to investigate the effects of providing within-series and across-series historical information (inflation rates, interest rates and unemployment rates) on inflation expectations. In Chapter 5, two experiments are reported that examine how training using simple outcome feedback increases the accuracy of inflation judgments and improves the calibration of confidence in those judgments. Chapter 6 reports experiments designed to examine the effects of using different elicitation methods (point forecasts, interval forecasts and density forecasts) on the accuracy of inflation judgments. Chapter 7 is a concluding chapter that summarises findings from these experiments and suggests avenues for future work

    Hindsight Learning for MDPs with Exogenous Inputs

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    Many resource management problems require sequential decision-making under uncertainty, where the only uncertainty affecting the decision outcomes are exogenous variables outside the control of the decision-maker. We model these problems as Exo-MDPs (Markov Decision Processes with Exogenous Inputs) and design a class of data-efficient algorithms for them termed Hindsight Learning (HL). Our HL algorithms achieve data efficiency by leveraging a key insight: having samples of the exogenous variables, past decisions can be revisited in hindsight to infer counterfactual consequences that can accelerate policy improvements. We compare HL against classic baselines in the multi-secretary and airline revenue management problems. We also scale our algorithms to a business-critical cloud resource management problem -- allocating Virtual Machines (VMs) to physical machines, and simulate their performance with real datasets from a large public cloud provider. We find that HL algorithms outperform domain-specific heuristics, as well as state-of-the-art reinforcement learning methods.Comment: 53 pages, 6 figure

    Parametric Distributionally Robust Optimisation Models for Resource and Inventory Planning Problems

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    Parametric probability distributions are commonly used for modelling uncertain demand and other random elements in stochastic optimisation models. However, when the distribution is not known exactly, it is more common that the distribution is either replaced by an empirical estimate or a non-parametric ambiguity set is built around this estimated distribution. In the latter case, we can then hedge against distributional ambiguity by optimising against the worst-case objective value over all distributions in the ambiguity set. This methodology is referred to as distributionally robust optimisation. When applying this approach, the ambiguity set necessarily contains non-parametric distributions. Therefore, applying this approach often means that any information about the true distribution’s parametric family is lost. This thesis introduces a novel framework for building and solving optimisation models under ambiguous parametric probability distributions. Instead of building an ambiguity set for the true distribution, we build an ambiguity set for its parameters. Every distribution considered by the model is then a member of the same parametric family as the true distribution. We reformulate the model using discretisation of the ambiguity set, which can result in a large, complex problem that is slow to solve. We first develop the parametric distributionally robust optimisation framework for a workforce planning problem under binomial demands. We then study a budgeted, multi-period new svendor model under Poisson and normal demands. In these first two cases, we develop fast heuristic cutting surface algorithms using theoretical properties of the cost function. Finally, we extend the framework into the dynamic decision making space via robust Markov decision processes. We develop a novel projectionbased bisection search algorithm that completely eliminates the need for discretisation of the ambiguity set. In each case, we perform extensive computational experiments to show that our algorithms offer significant reductions in run times with only negligible losses in solution quality

    Circular Economy and Sustainable Development: A Systematic Literature Review

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    Circular Economy put forth as an alternative to traditional linear model of extract-use-dispose along with the concept of Sustainable Development encompassing economic, environmental, and social aspects have garnered tremendous impetus among academics, practitioners and policymakers alike. The UN Sustainable Development Goals embraced by the member nations in 2015 based on the preceding Millenium Development Goals have been placed as the targets to be achieved as a part of holistic human development. In this backdrop, this paper examines the intersection of sustainability and circular economy with a focus on the three aspects of sustainable development, first the economic aspect by examining the relationship between GDP and circular economy, second the social economic aspect within the interaction of Circular Economy with Sustainable development and third the environmental-economical aspect by examining circularity and sustainability in waste management and waste valorisation. This paper achieves its objective through a systematic literature review of 1748 journal articles collected from Web of Science and SCOPUS database following PRISMA standards, network analysis of keywords, and manual review of texts. Four Research Questions are formulated: RQ1: What are the major emergent topics in Circular Economy and Sustainable Development and how are they related? RQ2: What is the relationship among CE and GDP in the CE and Sustainability? RQ3: What are the relationships between CE and Sustainability? RQ4: What are different use cases of valorisation of waste as CE tool, and can valorisation be sustainable? RQ1 is answered by presenting hotspot of research on Circular Economy and Sustainable Development through keywords occurrence network analysis using VosViewer. This study identifies three clusters and seven thematic areas of research, along with 25 most used keywords. RQ2 is attended through review of the relationship between economic growth (Gross Domestic Product) and Circular Economy and proposes based on the review that CE is still at its infancy. The paper also discusses the appropriateness of using GDP as a measure of sustainable development. This paper addresses RQ3 by examining the relationship between Circular Economy and Sustainable Development through review of literatures. The indicators used to measure CE and SD are also discussed and summarised. This review finds that achieving SDGs require greater effort, and that the present status of achievement is a bleak picture. Further, the role of waste management and potentiality of waste valorisation to aid in circular economy and sustainable development is analysed to answer RQ4. Though there are ample potential, however the recycle rate is very minimal to quench the required level of circularity. While CE and SD are related, CE cannot be a universal panacea to global challenges like emissions reduction, energy consumption, climate change, gender equality, poverty, well-being, environmental protection etc. even though the impact of CE to achieve SD can be substantial. The paper recommends avenues for future research and presents the conclusion of the study

    Three Experimental Accounting Studies

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    Diese Dissertation umfasst drei Studien. Die erste Studie untersucht Overprecision („Überpräzision“). Ich untersuche, wie Menschen Spannweitenschätzungen vornehmen. Teilnehmende müssen die Größe der Spannweite (Präzision) mit der Wahrscheinlichkeit, dass sie den wahren Wert einschließt (Richtigkeit) balancieren. Die Ergebnisse zeigen, dass Menschen inhärente individuelle Vorlieben für Präzision zu haben scheinen. Gleichzeitig werden vorhersagbar zusätzliche Informationen genutzt, um die Richtigkeit der Schätzungen zu erhöhen. Dafür wird entweder Präzision geopfert, oder die Spannweitenschätzung insgesamt verschoben. Die Richtigkeit der Schätzung wird jedoch nicht maximiert, sondern ein Teil für höhere Präzision aufgegeben. Die zweite Studie untersucht, wie sich die Übersetzung von Finanzberichterstattung auf die Wahrnehmung einer Firma als attraktives Investment auswirkt. Sie beleuchtet drei verschiedene Kanäle: Lesbarkeit, Stimmung, und Präzision der Veröffentlichung. In einem Umfrageexperiment lesen Kleinanleger deutsche und englische Prognoseberichte deutscher Firmen. Die Ergebnisse zeigen, dass die deutschen Berichte als besser lesbar wahrgenommen werden. Im Gegensatz zu vorheriger Literatur ist die Lesbarkeit nicht mit einer höheren Investmentattraktivität korreliert. Allein die Stimmung des Textes zeigt eine Korrelation mit höherer Attraktivität. Die dritte Studie untersucht, wie das Angebot von formativen Onlinetests die Leistungen von Studierenden in der Klausur beeinflusst. Sie untersucht zudem, ob die Leistung sich unterscheidet, je nachdem ob die Studierenden zeitlich begrenzten Zugang zu den Tests haben oder ob sie jederzeit auf die Tests zugreifen können. Ein Experiment, welches es ermöglicht den kausalen Intention-to-treat-Effekt zu bestimmen, zeigt, dass die formativen Onlinetests die Studienleistung erhöhen können, allerdings nur für Studierende, welche sich nicht freiwillig für die Tests gemeldet hatten und in der kontinuierlichen Lerngruppe waren.This dissertation comprises three papers. The first study examines overprecision. I examine how people provide range estimates, a challenging task that requires people to balance the width of the range (i.e., its precision) with the probability of the range covering the true value (i.e., accuracy). I find that people appear to have inherent individual preferences for a certain level of precision. At the same time, they appear to predictably incorporate additional information in order to increase accuracy by either sacrificing precision or shifting their ranges altogether. Still, they do not seem to maximise accuracy, but are willing to expend some of it to provide more precise estimates. The second study examines how the translation of financial disclosures changes investors’ perceptions of firms as an attractive investment. It examines three possible channels: readability, tone, and precision of the underlying disclosure. In a survey experiment, retail investors read forecast reports of German firms, provided in German and English. The findings indicate that the German versions are easier to read. Contrary to prior literature, the easier readability does not translate into higher investment attractiveness. Solely tone appears to be correlated with investment attractiveness. The third study analyses how offering formative online assessments influences student performance in the final exam. It further examines whether students perform differently depending on whether they have time-restricted access to the assessments, or whether they can access the assessments at any time. An experiment which allows for the identification of the causal intention-to-treat effect shows that offering formative online assessments can enhance student performance, but only for students who do not opt for taking the test voluntarily and who are in a continuous learning environment
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