183 research outputs found

    Active repositioning of storage units in Robotic Mobile Fulfillment Systems

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    In our work we focus on Robotic Mobile Fulfillment Systems in e-commerce distribution centers. These systems were designed to increase pick rates by employing mobile robots bringing movable storage units (so-called pods) to pick and replenishment stations as needed, and back to the storage area afterwards. One advantage of this approach is that repositioning of inventory can be done continuously, even during pick and replenishment operations. This is primarily accomplished by bringing a pod to a storage location different than the one it was fetched from, a process we call passive pod repositioning. Additionally, this can be done by explicitly bringing a pod from one storage location to another, a process we call active pod repositioning. In this work we introduce first mechanisms for the latter technique and conduct a simulation-based experiment to give first insights of their effect

    On the Complexity of an Unregulated Traffic Crossing

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    The steady development of motor vehicle technology will enable cars of the near future to assume an ever increasing role in the decision making and control of the vehicle itself. In the foreseeable future, cars will have the ability to communicate with one another in order to better coordinate their motion. This motivates a number of interesting algorithmic problems. One of the most challenging aspects of traffic coordination involves traffic intersections. In this paper we consider two formulations of a simple and fundamental geometric optimization problem involving coordinating the motion of vehicles through an intersection. We are given a set of nn vehicles in the plane, each modeled as a unit length line segment that moves monotonically, either horizontally or vertically, subject to a maximum speed limit. Each vehicle is described by a start and goal position and a start time and deadline. The question is whether, subject to the speed limit, there exists a collision-free motion plan so that each vehicle travels from its start position to its goal position prior to its deadline. We present three results. We begin by showing that this problem is NP-complete with a reduction from 3-SAT. Second, we consider a constrained version in which cars traveling horizontally can alter their speeds while cars traveling vertically cannot. We present a simple algorithm that solves this problem in O(nlog⁑n)O(n \log n) time. Finally, we provide a solution to the discrete version of the problem and prove its asymptotic optimality in terms of the maximum delay of a vehicle

    Clinical management of common presentations of patients diagnosed with BPD during the COVID-19 pandemic: The contribution of the MBT framework

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    The coronavirus (COVID-19) pandemic has both a profound effect on mental health and affects how psychosocial interventions are delivered. In this paper, we outline particular difficulties patients with Borderline Personality Disorder (BPD) may encounter as a result of the pandemic. We also consider changes in the provision of treatment, specifically the transition from face to face encounters to remotely delivered sessions. Building on a mentalization-based developmental framework, we use clinical vignettes to chart some of these challenges for patients, clinicians and teams. We then make practical recommendations for adaptations to work during the pandemic via the phone or video-link with BPD patients and other groups characterized by a vulnerability to unstable and imbalanced mentalizing. We conclude that the response to these challenges benefits from an existing treatment context that aims at fostering mentalizing and resilience, in which practitioners address the hierarchy of patient needs and their individual responses to the experience of remote treatment during the COVID-19 pandemic

    Agent-Based Markov Modeling for Improved COVID-19 Mitigation Policies

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    The year 2020 saw the covid-19 virus lead to one of the worst global pandemics in history. As a result, governments around the world have been faced with the challenge of protecting public health while keeping the economy running to the greatest extent possible. Epidemiological models provide insight into the spread of these types of diseases and predict the e_ects of possible intervention policies. However, to date, even the most data-driven intervention policies rely on heuristics. In this paper, we study how reinforcement learning (RL) and Bayesian inference can be used to optimize mitigation policies that minimize economic impact without overwhelming hospital capacity. Our main contributions are (1) a novel agent-based pandemic simulator which, unlike traditional models, is able to model _ne-grained interactions among people at speci_c locations in a community; (2) an RL- based methodology for optimizing _ne-grained mitigation policies within this simulator; and (3) a Hidden Markov Model for predicting infected individuals based on partial observations regarding test results, presence of symptoms, and past physical contacts

    ИсслСдованиС влияния тСхнологичСского процСсса изготовлСния ΠΎΠ±ΠΌΠΎΡ‚ΠΎΠΊ Π½Π° Π΄Π΅Ρ„Π΅ΠΊΡ‚Π½ΠΎΡΡ‚ΡŒ корпусной изоляции асинхронных Π΄Π²ΠΈΠ³Π°Ρ‚Π΅Π»Π΅ΠΉ

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    Π’ Ρ€Π°Π±ΠΎΡ‚Π΅ ΠΏΡ€ΠΎΠ²Π΅Π΄Π΅Π½ΠΎ исслСдованиС влияния ΠΊΠΎΠ»Π΅Π±Π°Π½ΠΈΠΉ Ρ€Π΅ΠΆΠΈΠΌΠΎΠ² Ρ€Π°Π±ΠΎΡ‚Ρ‹ статорообмоточных станков WST-660 ΠΈ ΠΏΠ°Π·ΠΎΠΈΠ·ΠΎΠ»ΠΈΡ€ΠΎΠ²ΠΎΡ‡Π½Ρ‹Ρ… станков ИПБ-3 Π½Π° Π΄Π΅Ρ„Π΅ΠΊΡ‚Π½ΠΎΡΡ‚ΡŒ корпусной изоляции. ΠŸΠΎΠ»ΡƒΡ‡Π΅Π½Ρ‹ матСматичСскиС ΠΌΠΎΠ΄Π΅Π»ΠΈ дСфСктообразования Π² корпусной изоляции ΠΎΠ±ΠΌΠΎΡ‚ΠΊΠΈ с ΡƒΡ‡Π΅Ρ‚ΠΎΠΌ Ρ€Π΅ΠΆΠΈΠΌΠΎΠ² Ρ€Π°Π±ΠΎΡ‚Ρ‹ тСхнологичСского оборудования ΠΈ качСства ΠΌΠ°Ρ‚Π΅Ρ€ΠΈΠ°Π»Π° корпусной изоляции Π² состоянии поставки. УстановлСно, Ρ‡Ρ‚ΠΎ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ΠΌ Ρ€Π΅ΠΆΠΈΠΌΠΎΠ² Ρ€Π°Π±ΠΎΡ‚Ρ‹ тСхнологичСского оборудования ΠΌΠΎΠΆΠ½ΠΎ Π΄ΠΎΠ±ΠΈΡ‚ΡŒΡΡ Ρ‚Ρ€Π΅Π±ΡƒΠ΅ΠΌΠΎΠ³ΠΎ качСства корпусной изоляции ΠΏΡ€ΠΈ максимальной ΠΏΡ€ΠΎΠΈΠ·Π²ΠΎΠ΄ΠΈΡ‚Π΅Π»ΡŒΠ½ΠΎΡΡ‚ΠΈ оборудования

    Wind Field of a Nonmesocyclone Anticyclonic Tornado Crossing the Hong Kong International Airport

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    A nonmesocyclone tornado traversed the Hong Kong International Airport on September 6, 2004 directly impacting a surface weather station. This allowed for 1-second 10-meter above ground level (AGL) wind observations through the core of the tornado. Integration of these 10-meter AGL wind data with Ground-Based Velocity Track (GBVTD) wind retrievals derived from LIDAR data provided a time history of the three-dimensional wind field of the tornado. These data indicate a progressive decrease in radial inflow with time and little to no radial inflow near the time the tornado crosses the surface weather station. Anemometer observations suggest that the tangential winds approximate a modified-Rankine vortex outside the radius of maximum winds, suggesting that frictionally induced radial inflow was confined below 10 m AGL. The radial-height distribution of angular momentum depicts an increase in low-level angular momentum just prior to the tornado reaching its maximum intensity

    The Use of Software Agents for Autonomous Control of a DC Space Power System

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    In order to enable manned deep-space missions, the spacecraft must be controlled autonomously using on-board algorithms. A control architecture is proposed to enable this autonomous operation for an spacecraft electric power system and then implemented using a highly distributed network of software agents. These agents collaborate and compete with each other in order to implement each of the control functions. A subset of this control architecture is tested against a steadystate power system simulation and found to be able to solve a constrained optimization problem with competing objectives using only local information

    Auction Protocols for Decentralized Scheduling

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    Scheduling is the problem of allocating resources to alternate possible uses over designated periods of time. Several have proposed (and some have tried) market-based approaches to decentralized versions of the problem, where the competing uses are represented by autonomous agents. Market mechanisms use prices derived through distributed bidding protocols to determine an allocation, and thus solve the scheduling problem. To analyze the behavior of market schemes, we formalize decentralized scheduling as a discrete resource allocation problem, and bring to bear some relevant economic concepts. Drawing on results from the literature, we discuss the existence of equilibrium prices for some general classes of scheduling problems, and the quality of equilibrium solutions. To remedy the potential nonexistence of price equilibria due to complementarity in preference, we introduce additional markets in combinations of basic goods. We present some auction mechanisms and bidding protocols corresponding to the two market structures, and analyze their computational and economic properties. Finally, we consider direct revelation mechanisms, and compare to the market-based approach.http://deepblue.lib.umich.edu/bitstream/2027.42/50443/1/gebfinal.pd
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