Reinforcement learning for Order Acceptance on a shared resource

Order acceptance (OA) is one of the main functions in business control. Basically, OA involves for each order a reject/accept decision. Always accepting an order when capacity is available could disable the system to accept more convenient orders in the future with opportunity losses as a consequence. Another important aspect is the availability of information to the decision-maker. We use the stochastic modeling approach, Markov decision theory and learning methods from artificial intelligence to find decision policies, even under uncertain information. Reinforcement learning (RL) is a quite new approach in OA. It is capable of learning both the decision policy and incomplete information, simultaneously. It is shown here that RL works well compared with heuristics. Finding good heuristics in a complex situation is a delicate art. It is demonstrated that a RL trained agent can be used to support the detection of good heuristics

Reallocating resources to focused factories: a case study in chemotherapy

This study investigates the expected service performance associated with a proposal to reallocate resources from a centralized chemotherapy department to a breast cancer focused factory. Using a slotted queueing model we show that a decrease in performance is expected and calculate the amount of additional resources required to offset these losses. The model relies solely on typical outpatient scheduling system data, making the methodology easy to replicate in other outpatient clinic settings. Finally, the paper highlights important factors to consider when assigning capacity to focused factories. These considerations are generally relevant to other resource allocation decisions

Dry run

Simulating wind and rain around a stadium determines the best design for keeping spectators dry. Results can be used to improve the design of future stadiums as well as to diagnose and correct problems with existing stadiums

Polarization Modeling and Predictions for DKIST Part 2: Application of the Berreman Calculus to Spectral Polarization Fringes of Beamsplitters and Crystal Retarders

We outline polarization fringe predictions derived from a new application of the Berreman calculus for the Daniel K. Inouye Solar Telescope (DKIST) retarder optics. The DKIST retarder baseline design used 6 crystals, single-layer anti-reflection coatings, thick cover windows and oil between all optical interfaces. This new tool estimates polarization fringes and optic Mueller matrices as functions of all optical design choices. The amplitude and period of polarized fringes under design changes, manufacturing errors, tolerances and several physical factors can now be estimated. This tool compares well with observations of fringes for data collected with the SPINOR spectropolarimeter at the Dunn Solar Telescope using bi-crystalline achromatic retarders as well as laboratory tests. With this new tool, we show impacts of design decisions on polarization fringes as impacted by anti-reflection coatings, oil refractive indices, cover window presence and part thicknesses. This tool helped DKIST decide to remove retarder cover windows and also recommends reconsideration of coating strategies for DKIST. We anticipate this tool to be essential in designing future retarders for mitigation of polarization and intensity fringe errors in other high spectral resolution astronomical systems.Comment: Accepted for publication in JATI

Order acceptance with reinforcement learning

Order Acceptance (OA) is one of the main functions in a business control framework. Basically, OA involves for each order a 0/1 (i.e., reject/accept) decision. Always accepting an order when capacity is available could unable the system to accept more convenient orders in the future. Another important aspect is the aV'(tiiability of information to the decisionmaker. We use a stochastic modeling approach using Markov decision theory and learning methods from Artificial Intelligence techniques in order to deal with uncertainty and long-term decisions in Ok Reinforcement Learning (RL) is a quite new approach that already combines this idea of modeling and solution method. Here we report on RL-solutions for some OA models

Myofibrillar Protein Status of the Gastrocnemius in Male Rats: Effect of Mild Undernutrition

The aim of this work was the determination of the myofibrillar protein profiles in the fed and the mildly underfed rat. Sixteen male rats were divided into 2 groups: CR (control) fed ad libitum and MR (mildly undernourished) fed 75% of energetic maintenance needs. The animals were sacrificed at day 23 and the gastrocnemius muscle was taken for myofibrillar protein characterisation. The myofibrillar protein profiles were found to be very similar in the two groups revealing the lack of preferred catabolism of myofibrillar proteins and consequently that the muscle structure is maintained even in situations of mild undernutrition

Assessing excellence in translational cancer research: a consensus based framework

Background: It takes several years on average to translate basic research findings into clinical research and eventually deliver patient benefits. An expert-based excellence assessment can help improve this process by: identifying high performing Comprehensive Cancer Centres; best practices in translational cancer research; improving the quality and efficiency of the translational cancer research process. This can help build networks of excellent Centres by aiding focused partnerships. In this paper we report on a consensus building exercise that was undertaken to construct an excellence assessment framework for translational cancer research in Europe. Methods: We used mixed methods to reach consensus: a systematic review of existing translational research models critically appraised for suitability in performance assessment of Cancer Centres; a survey among European stakeholders (researchers, clinicians, patient representatives and managers) to score a list of potential excellence criteria, a focus group with selected representatives of survey participants to review and rescore the excellence criteria; an expert group meeting to refine the list; an open validation round with stakeholders and a critical review of the emerging framework by an independent body: a committee formed by the European Academy of Cancer Sciences. Results: The resulting excellence assessment framework has 18 criteria categorized in 6 themes. Each criterion has a number of questions/sub-criteria. Stakeholders favoured using qualitative excellence criteria to evaluate the translational research “process” rather than quantitative criteria or judging only the outputs. Examples of criteria include checking if the Centre has mechanisms that can be rated as excellent for: involvement of basic researchers and clinicians in translational research (quality of supervision and incentives provided to clinicians to do a PhD in translational research) and well designed clinical trials based on ground-breaking concepts (innovative patient stratification, substantial fraction of phase I/II trials, investigator-initiated trials). Critically, the framework supports reduced bureaucracy by building on existing European evaluation systems. Conclusions: The excellence framework is the product of an intense stakeholder consensus building exercise. It will be piloted during an expert peer review/site visit of at least three European Comprehensive Cancer Centres. The findings regarding content, governance and implementation can have relevance for other clinical and research fields