Effectiveness in warehouse design and operations is crucial for the industrial companies to be competitive in the market arena by reducing the response time and inbound costs, increasing their global ...service level. Storage assignment deals with the definition of effective strategies to organise items into industrial warehouses to achieve high performances. This paper enhances the conventional approaches on storage assignment proposing a time and energy bi-objective model for single-deep rack automatic warehouses served by aisle captive automated storage/retrieval systems (AS/RSs). The model is based on the joint minimisation of the travel time and the energy required by the cranes to S/R the unit-loads. The analytic models to compute the expected single-command cycle time and energy are integrated into a bi-objective model, optimising the load assignment. An application, taken from the beverage industry, is, finally, discussed. The different perspectives of adopting time and energy to drive the load assignment are stressed proposing a practical trade-off rule. Results highlight the possibility to balance the time cycle and the energy intensity. Within the case application, an energy saving of 12.66% respect to the time model occurs with a time efficiency decrease of about 2.52%.
To provide high-quality data for coronavirus disease 2019 (COVID-19) research, we validated derived COVID-19 clinical indicators and 22 associated machine learning phenotypes, in the Mass General ...Brigham (MGB) COVID-19 Data Mart.
Fifteen reviewers performed a retrospective manual chart review for 150 COVID-19-positive patients in the data mart. To support rapid chart review for a wide range of target data, we offered a natural language processing (NLP)-based chart review tool, the Digital Analytic Patient Reviewer (DAPR). For this work, we designed a dedicated patient summary view and developed new 127 NLP logics to extract COVID-19 relevant medical concepts and target phenotypes. Moreover, we transformed DAPR for research purposes so that patient information is used for an approved research purpose only and enabled fast access to the integrated patient information. Lastly, we performed a survey to evaluate the validation difficulty and usefulness of the DAPR.
The concepts for COVID-19-positive cohort, COVID-19 index date, COVID-19-related admission, and the admission date were shown to have high values in all evaluation metrics. However, three phenotypes showed notable performance degradation than the positive predictive value in the prepandemic population. Based on these results, we removed the three phenotypes from our data mart. In the survey about using the tool, participants expressed positive attitudes toward using DAPR for chart review. They assessed that the validation was easy and DAPR helped find relevant information. Some validation difficulties were also discussed.
Use of NLP technology in the chart review helped to cope with the challenges of the COVID-19 data validation task and accelerated the process. As a result, we could provide more reliable research data promptly and respond to the COVID-19 crisis. DAPR's benefit can be expanded to other domains. We plan to operationalize it for wider research groups.
This article applies value stream mapping (VSM) in a distribution warehouse after identifying and categorizing different warehousing wastes. The study suggests solutions for the reduction of each ...type of waste and employs lead time as the metric to understand the overall effectiveness of the suggested remedies. The distribution warehouse faced severe stock-out situations and high lead time for all deliveries. Current state and future state maps were utilized for mapping the current and revamped system, respectively. While existing studies on lean warehousing have utilized VSM to study a specific type of waste, this study extends it to include all types of warehousing waste, classifies them into seven types and provides a real case study along with evidence for improvement schemes for each category. This research, employing a case study, suggests an integrated lean warehousing method for design and operation of distribution warehouses.
Dilemma/research question/purpose: Can the warehouse avoid stock-outs and decrease the lead time by identifying and reducing or eliminating the seven types of wastes in the warehouse operations?
Theory: Lean management principle applied to a warehouse using value stream mapping to eliminate wastes.
Type of the case: A problem-solving using lean tools carried out in a warehouse.
Protagonist: Not needed.
Options: Allow the current state to continue causing stock-outs and high lead times, or identify and reduce the wastes in the warehouse operations and avoid stock-outs, decrease lead times, and improve the overall efficiency of the warehouse.
Discussions and case questions: Is it possible for the firm to go one step further than what the future state map has shown in the study? What other lean tools do you see fit to reduce or eliminate the types of waste outlined in this article?
Objective
To measure hospital quality based on routine data available in many health care systems including the United States, Germany, the United Kingdom, Scandinavia, and Switzerland.
Data Sources ...and Study Setting
We use the Swiss Medical Statistics of Hospitals, an administrative hospital dataset of all inpatient stays in acute care hospitals in Switzerland for the years 2017–2019.
Study Design
We study hospital quality based on quality indicators used by leading agencies in five countries (the United States, the United Kingdom, Germany, Austria, and Switzerland) for two high‐volume elective procedures: inguinal hernia repair and hip replacement surgery. We assess how least absolute shrinkage and selection operator (LASSO), a supervised machine learning technique for variable selection, and Mundlak corrections that account for unobserved heterogeneity between hospitals can be used to improve risk adjustment and correct for imbalances in patient risks across hospitals.
Data Collection/Extraction Methods
The Swiss Federal Statistical Office collects annual data on all acute care inpatient stays including basic socio‐demographic patient attributes and case‐level diagnosis and procedure codes.
Principal Findings
We find that LASSO‐selected and Mundlak‐corrected hospital random effects logit models outperform common practice logistic regression models used for risk adjustment. Besides the more favorable statistical properties, they have superior in‐ and out‐of‐sample explanatory power. Moreover, we find that Mundlak‐corrected logits and the more complex LASSO‐selected models identify the same hospitals as high or low‐quality offering public health authorities a valuable alternative to standard logistic regression models. Our analysis shows that hospitals vary considerably in the quality they provide to patients.
Conclusion
We find that routine hospital data can be used to measure clinically relevant quality indicators that help patients make informed hospital choices.
This paper examines the two-way relationship between land use and transportation from the perspective of warehouses in the Toronto Census Metropolitan Area. A location choice model is first developed ...to reproduce the decision process of firms selecting suitable locations to operate warehousing facilities. The results highlight that transportation plays a prominent role in the location of warehouses with proximity to airport, highway infrastructure, and rail to a lesser extent. However, the proportion of industrial land use provides the largest role in the model suggesting that available land and zoning is the most important factor. This paper also studies the impact of warehouse location on freight transportation trips. The resulting analysis highlights that GPS derived trips arising from warehouses near a major airport in Toronto tend to travel 1.8 times further than trips pertaining to other warehouses in the region. This suggests that trips related to the airport are more likely to be connected to a larger supply chain process.
With the further development of online shopping and the impact of the COVID-19 pandemic, the logistics industry has further increased the demand for unmanned, automated warehousing and logistics ...handling. To realize intelligent warehousing and logistics handling, reliable positioning navigation technology is indispensable. Therefore, this paper designs a Dual-lidar high-precision natural navigation system based on the ROS (Robot Operating System) platform, which can fulfill the basic warehousing and logistics requirements. The natural navigation system uses the Lidar-SLAM method based on graph optimization to construct the 2D environment map, the PF (Particle Filter) algorithm in MRPT (Mobile Robot Programming Toolkit) is used for system positioning, and the real-time correction algorithm is used for motion control. On the built hardware platform, the navigation system completed the fixed-point cruise navigation task, and finally achieved a navigation accuracy of 4 cm and an average repeatable navigation accuracy of 6 mm. The designed navigation system has reference significance for multi-sensor fusion navigation. In reality, it can be applied to the transportation of warehousing and logistics, and it is expected to be mass-produced in the future.
Modern organizations are currently wrestling with strenuous challenges relating to the management of heterogeneous big data, which combines data from various sources and varies in type, format, and ...content. The heterogeneity of the data makes it difficult to analyze and integrate. This paper presents big data warehousing and federation as viable approaches for handling big data complexity. It discusses their respective advantages and disadvantages as strategies for integrating, managing, and analyzing heterogeneous big data. Data integration is crucial for organizations to manipulate organizational data. Organizations have to weigh the benefits and drawbacks of both data integration approaches to identify the one that responds to their organizational needs and objectives. This paper aw well presents an adequate analysis of these two data integration approaches and identifies challenges associated with the selection of either approach. Thorough understanding and awareness of the merits and demits of these two approaches are crucial for practitioners, researchers, and decision-makers to select the approach that enables them to handle complex data, boost their decision-making process, and best align with their needs and expectations.
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