Decision models for fast-fashion supply and stocking problems in internet fulfillment warehouses

Internet technology is being widely used to transform all aspects of the modern supply chain. Specifically, accelerated product flows and wide spread information sharing across the supply chain have generated new sets of decision problems. This research addresses two such problems. The first focuses on fast fashion supply chains in which inventory and price are managed in real time to maximize retail cycle revenue. The second is concerned with explosive storage policies in Internet Fulfillment Warehouses (IFW). Fashion products are characterized by short product life cycles and market success uncertainty. An unsuccessful product will often require multiple price discounts to clear the inventory. The first topic proposes a switching solution for fast-fashion retailers who have preordered an initial or block inventory, and plan to use channel switching as opposed to multiple discounting steps. The FFS Multi-Channel Switching (MCS) problem then is to monitor real-time demand and store inventory, such that at the optimal period the remaining store inventory is sold at clearance, and the warehouse inventory is switched to the outlet channel. The objective is to maximize the total revenue. With a linear projection of the moving average demand trend, an estimation of the remaining cycle revenue at any time in the cycle is shown to be a concave function of the switching time. Using a set of conditions the objective is further simplified into cases. The Linear Moving Average Trend (LMAT) heuristic then prescribes whether a channel switch should be made in the next period. The LMAT is compared with the optimal policy and the No-Switch and Beta-Switch rules. The LMAT performs very well and the majority of test problems provide a solution within 0.4% of the optimal. This confirms that LMAT can readily and effectively be applied to real time decision making in a FFS. An IFW is a facility built and operated exclusively for online retail, and a key differentiator is the explosive storage policy. Breaking the single stocking location tradition, in an IFW small batches of the same stock keeping unit (SKU) are dispersed across the warehouse. Order fulfillment time performance is then closely related to the storage location decision, that is, for every incoming bulk, what is the specific storage location for each batch. Faster fulfillment is possible when SKUs are clustered such that narrow band picklists can be efficiently generated. Stock location decisions are therefore a function of the demand arrival behavior and correlations with other SKUs. Faster fulfillment is possible when SKUs are clustered such that narrow band picklists can be efficiently generated. Stock location decisions are therefore a function of the demand behavior and correlations with other SKUs. A Joint Item Correlation and Density Oriented (JICDO) Stocking Algorithm is developed and tested. JICDO is formulated to increase the probability that M pick able order items are stocked in a δ band of storage locations. It scans the current inventory dispersion to identify location bands with low SKU density and combines the storage affinity with correlated items. In small problem testing against a MIP formulation and large scale testing in a simulator the JICDO performance is confirmed

Using Omnichannel Sales Data Analytics to Decide Between Store and Distribution Center Fulfillment Options

A brick-and-mortar retailer can fulfill online customer orders in two ways (i) Buy Online Fulfill from Store (BOFS) - Picked from store inventory, and (ii) Fulfill from Distribution Center (FDC) - Picked from DC or warehouse inventory. The fulfillment decision is made in real time for each order, with the primary goal of maximizing the revenue value of the store inventory. Analysis of sales data in both online and store channels is used to forecast the value of the dispersed inventory, and then develop a prescriptive model for making a fulfillment decision

SheetCopilot: Bringing Software Productivity to the Next Level through Large Language Models

Computer end users have spent billions of hours completing daily tasks like tabular data processing and project timeline scheduling. Most of these tasks are repetitive and error-prone, yet most end users lack the skill to automate these burdensome works. With the advent of large language models (LLMs), directing software with natural language user requests become a reachable goal. In this work, we propose a SheetCopilot agent that takes natural language task and control spreadsheet to fulfill the requirements. We propose a set of atomic actions as an abstraction of spreadsheet software functionalities. We further design a state machine-based task planning framework for LLMs to robustly interact with spreadsheets. We curate a representative dataset containing 221 spreadsheet control tasks and establish a fully automated evaluation pipeline for rigorously benchmarking the ability of LLMs in software control tasks. Our SheetCopilot correctly completes 44.3\% of tasks for a single generation, outperforming the strong code generation baseline by a wide margin. Our project page:https://sheetcopilot.github.io/.Comment: Accepted to NeurIPS 202

Association between urinary arsenic species and vitamin D deficiency: a cross-sectional study in Chinese pregnant women

BackgroundAn increasing number of studies suggest that environmental pollution may increase the risk of vitamin D deficiency (VDD). However, less is known about arsenic (As) exposure and VDD, particularly in Chinese pregnant women.ObjectivesThis study examines the correlations of different urinary As species with serum 25 (OH) D and VDD prevalence.MethodsWe measured urinary arsenite (As3+), arsenate (As5+), monomethylarsonic acid (MMA), and dimethylarsinic acid (DMA) levels and serum 25(OH)D2, 25(OH)D3, 25(OH) D levels in 391 pregnant women in Tianjin, China. The diagnosis of VDD was based on 25(OH) D serum levels. Linear relationship, Logistic regression, and Bayesian kernel machine regression (BKMR) were used to examine the associations between urinary As species and VDD.ResultsOf the 391 pregnant women, 60 received a diagnosis of VDD. Baseline information showed significant differences in As3+, DMA, and tAs distribution between pregnant women with and without VDD. Logistic regression showed that As3+ was significantly and positively correlated with VDD (OR: 4.65, 95% CI: 1.79, 13.32). Meanwhile, there was a marginally significant positive correlation between tAs and VDD (OR: 4.27, 95% CI: 1.01, 19.59). BKMR revealed positive correlations between As3+, MMA and VDD. However, negative correlations were found between As5+, DMA and VDD.ConclusionAccording to our study, there were positive correlations between iAs, especially As3+, MMA and VDD, but negative correlations between other As species and VDD. Further studies are needed to determine the mechanisms that exist between different As species and VDD

The bleaching limits of IRSL signals at various stimulation temperatures and their potential inference of the pre-burial light exposure duration

Infrared Stimulated Luminescence (IRSL) techniques are being increasingly used for dating sedimentary feldspars in the middle to late Quaternary. By employing several subsequent stimulations at increasing temperatures, a series of post-IR IRSL (pIRIR) signals with different characteristics (stability and bleachability) can be obtained for an individual sample. It has been experimentally demonstrated that higher-temperature pIRIR signals are more stable, but they tend to exhibit larger residual doses up to few tens of Gy, potentially causing severe age overestimation in young samples. In this study we conducted comprehensive bleaching experiments of IRSL and pIRIR signals using a loess sample from China, and demonstrated that non-bleachable components in the IR (and possibly pIRIR) signals do exist. The level of such non-bleachable signal shows clearly positive correlation with preheat/stimulation temperature, which further supports the notion that lower temperature pIRIR are advantageous to date young samples and sediments especially from difficult-to-bleach environments. These results display a potential in constrain the pre-burial light exposure history of sediment utilizing multiple feldspar post-IR IRSL (pIRIR) signals. For the studied loess sample, we infer that prior to its last burial, the sample has received an equivalent of >264 h exposure to the SOL2 simulator (more than 2,000 h of natural daylight)

What has affected the governance effect of the whole population coverage of medical insurance in China in the past decade? Lessons for other countries

ObjectiveThis study aimed to explore the current state of governance of full population coverage of health insurance in China and its influencing factors to provide empirical references for countries with similar social backgrounds as China.MethodsA cross-sectional quantitative study was conducted nationwide between 22 January 2020 and 26 January 2020, with descriptive statistics, analysis of variance, and logistic regression models via SPSS 25.0 to analyze the effectiveness and influencing factors of the governance of full population coverage of health insurance in China.ResultsThe effectiveness of the governance relating to the total population coverage of health insurance was rated as good by 59% of the survey respondents. According to the statistical results, the governance of the public's ability to participate in insurance (OR = 1.516), the degree of information construction in the medical insurance sector (OR = 2.345), the government's governance capacity (OR = 4.284), and completeness of the government's governance tools (OR = 1.370) were all positively correlated (p < 0.05) on the governance effect of the whole population coverage of health insurance.ConclusionsThe governance of Chinese health insurance relating to the total population coverage is effective. To effectively improve the effectiveness of the governance relating to the total population coverage of health insurance, health insurance information construction, governance capacity, and governance tools should be the focus of governance to further improve the accurate expansion of and increase the coverage of health insurance

A semi-automatic deep learning model based on biparametric MRI scanning strategy to predict bone metastases in newly diagnosed prostate cancer patients

ObjectiveTo develop a semi-automatic model integrating radiomics, deep learning, and clinical features for Bone Metastasis (BM) prediction in prostate cancer (PCa) patients using Biparametric MRI (bpMRI) images.MethodsA retrospective study included 414 PCa patients (BM, n=136; NO-BM, n=278) from two institutions (Center 1, n=318; Center 2, n=96) between January 2016 and December 2022. MRI scans were confirmed with BM status via PET-CT or ECT pre-treatment. Tumor areas on bpMRI images were delineated as tumor’s region of interest (ROI) using auto-delineation tumor models, evaluated with Dice similarity coefficient (DSC). Samples were auto-sketched, refined, and used to train the ResNet BM prediction model. Clinical, radiomics, and deep learning data were synthesized into the ResNet-C model, evaluated using receiver operating characteristic (ROC).ResultsThe auto-segmentation model achieved a DSC of 0.607. Clinical BM prediction’s internal validation had an accuracy (ACC) of 0.650 and area under the curve (AUC) of 0.713; external cohort had an ACC of 0.668 and AUC of 0.757. The deep learning model yielded an ACC of 0.875 and AUC of 0.907 for the internal, and ACC of 0.833 and AUC of 0.862 for the external cohort. The Radiomics model registered an ACC of 0.819 and AUC of 0.852 internally, and ACC of 0.885 and AUC of 0.903 externally. ResNet-C demonstrated the highest ACC of 0.902 and AUC of 0.934 for the internal, and ACC of 0.885 and AUC of 0.903 for the external cohort.ConclusionThe ResNet-C model, utilizing bpMRI scanning strategy, accurately assesses bone metastasis (BM) status in newly diagnosed prostate cancer (PCa) patients, facilitating precise treatment planning and improving patient prognoses