Reduce Delivery Time Research Articles

Validating a Pneumatic Tube System for Blood Product Delivery

Abstract Introduction/Objective Timely delivery of blood products can be of utmost importance to providing quality care. We aimed to validate the use of a high-speed pneumatic tube system (PTS) that was already installed at our hospital. By reducing delivery times, patient resuscitation and survivability can be increased. Methods/Case Report We followed the Association for the Advancement of Blood & Biotherapies (AABB) guidelines to validate a PTS in a large tertiary academic medical center. The requirements include: verifying product suitability, ensuring product integrity, temperature monitoring, transportation time measurements, and proper packaging and tracking of product receipt. We validated the transfer of packed red blood cells (PRBC), plasma, platelets, and cryoprecipitate from the central blood transfusion services lab (BTS) to nursing stations in a separate building up to 1500 feet (>1/4 mile) away using a Swisslog Translogic® PTS Inclusion with foam inside the PTS carriers helped reduce bumps during transit and insulated products from heat. Results (if a Case Study enter NA) The mean temperature of the PRBC was 6.6 degrees Celsius and none exceeded the AABB maximum of 10 degrees Celsius. The mean time for blood products transit to the nursing stations was 3 minutes (m) and 33 seconds (s). The median time was 3m 31s, and it ranged from 2m 18s to 5m 38s. The PTS reduced delivery times of blood products by up to 10-12 minutes compared to manual porters. Conclusion Transporting blood products via PTS is an efficient method that reduces the required manpower and leads to better utilization of resources. However, it is essential to validate the PTS system before implementing it operationally.

AGV Scheduling for Optimizing Irregular Air Cargo Containers Handling at Airport Transshipment Centers

Airport transshipment centers play a pivotal role in global logistics networks, enabling the swift and efficient transfer of cargo, which is essential for maintaining supply-chain continuity and reducing delivery times. The handling of irregularly shaped air cargo containers presents new constraints for automated guided vehicles (AGVs), as these shapes can complicate loading and unloading processes, directly impacting overall operational efficiency, turnaround times, and the reliability of cargo handling. This study focuses on optimizing the scheduling of AGVs to enhance cargo-handling efficiency at these hubs, particularly for managing irregular air cargo containers. A mixed-integer linear programming (MILP) model is developed, validated for feasibility with the Gurobi solver, and designed to handle large-scale operations. It incorporates a novel approach by integrating a simulated annealing optimized genetic algorithm (GA). The experimental results demonstrate that the designed algorithm can solve models of considerable size within 8 s, offering superior time efficiency compared to the solver, and an average solution quality improvement of 12.62% over the genetic algorithm, significantly enhancing both the model’s efficiency and scalability. The enhanced AGV scheduling not only boosts operational efficiency but also ensures better integration within the global logistics framework. This research provides a robust foundation for future advancements in logistics technology, offering both theoretical and practical insights into optimizing complex transportation networks.

A First Extension of the Robust Satellite Technique RST-FLOOD to Sentinel-2 Data for the Mapping of Flooded Areas: The Case of the Emilia Romagna (Italy) 2023 Event

Extreme meteorological events hit our planet with increasing frequency, resulting in an ever-increasing number of natural disasters. Flash floods generated by intense and violent rains are among the most dangerous natural disasters that compromise crops and cause serious damage to infrastructure and human lives. In the case of such a kind of disastrous events, timely and accurate information about the location and extent of the affected areas can be crucial to better plan and implement recovery and containment interventions. Satellite systems may efficiently provide such information at different spatial/temporal resolutions. Several authors have developed satellite techniques to detect and map inundated areas using both Synthetic Aperture Radar (SAR) and a new generation of high-resolution optical data but with some accuracy limits, mostly due to the use of fixed thresholds to discriminate between the inundated and unaffected areas. In this paper, the RST-FLOOD fully automatic technique, which does not suffer from the aforementioned limitation, has been exported for the first time to the mid–high-spatial resolution (20 m) optical data provided by the Copernicus Sentinel-2 Multi-Spectral Instrument (MSI). The technique was originally designed for and successfully applied to Advanced Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS) satellite data at a mid–low spatial resolution (from 1000 to 375 m). The processing chain was implemented in a completely automatic mode within the Google Earth Engine (GEE) platform to study the recent strong flood event that occurred in May 2023 in Emilia Romagna (Italy). The outgoing results were compared with those obtained through the implementation of an existing independent optical-based technique and the products provided by the official Copernicus Emergency Management Service (CEMS), which is responsible for releasing information during crisis events. The comparisons carried out show that RST-FLOOD is a simple implementation technique able to retrieve more sensitive and effective information than the other optical-based methodology analyzed here and with an accuracy better than the one offered by the CEMS products with a significantly reduced delivery time.

IMPROVEMENT OF THE TECHNOLOGICAL PROCESS OF PERISHABLE GOODS DELIVERY IN THE CITY OF KHARKIV

The article analyses the existing methods for building perishable goods delivery technologies and identifies strategies to study the prospects for developing perishable goods delivery systems, which allowed the authors to formulate the research objective in this development. The chosen topic is quite relevant since the formation of rational transport and technological schemes for the delivery of perishable goods allows for increasing the efficiency of resource use, decreasing the cost of their delivery, and reducing the delivery time of perishable goods. One of the ways to improve the efficiency of perishable goods delivery and reduce delivery time is to determine rational distribution routes and rational brands of rolling stock. The study established that foreign scientists have proposed solutions related to the construction of transport and technological schemes for the delivery of perishable goods, taking into account the choice of vehicles and routes for the delivery of goods. Most of these solutions focused on determining the appropriate loading capacity of rolling stock and the optimal delivery routes for perishable goods. A result of the conducted research was a proposed methodology for a comprehensive improvement of the perishable goods delivery process, consisting of determining rational distribution routes for a selected set of vehicles with different loadings. The authors implemented the software for the algorithm for choosing routes for vehicles of various capacities with the definition of the core technical and operational and technical and economic indicators of efficiency of the delivery process of perishable goods. The software implementation of the algorithm for selecting routes for vehicles of different capacities with the determination of the core technical and operational and technical and economic indicators of the efficiency of the delivery process of perishable goods allows one to quickly, in real-time, perform calculations and determine the rational parameters of the transport process and choose the appropriate routes and rolling stock. Keywords: perishable goods, cargo delivery, distribution routes, truck capacity, costs.

THE ROLE OF DATA ANALYTICS IN MAKING MANAGEMENT DECISIONS BY THE LOGISTICS INTERMEDIARIES

Data analytics plays a crucial role in increasing the effectiveness of management decisions by the logistics of intermediaries. The aim of the article is to identify the extent to which the quality of data analytics affects the effectiveness of decision-making in the logistics intermediaries, in particular, the speed of delivery of the studied companies.The study employed regression and correlation analysis to identify key influencing factors in terms of data analytics on the effectiveness of management decisions of the logistics intermediaries. The significance of investment in the qualification of analysts (with a coefficient of -1.6754), analytical tools (with a coefficient of -1.2575), and integration of analytics in decision-making processes (with a coefficient of -3.2511) directly affect the reduction of delivery time.It is emphasized that each analytical project contributes to the reduction of delivery time by 0.48 hours. Correlation analysis confirmed the relationship between the efficiency of logistics and the level of qualification of analysts (-0.283617), investment in analytical tools (-0.257322), the number of analytical projects (-0.343792), the level of integration of analytics (-0.712058). The strongest correlation was observed for the integration of analytics in management decision-making.It is recommended to focus on the development of analytical competencies, increase of investment in tools, intensification of projects, and integration of analytics in strategic management. Further research is planned on the use of artificial intelligence to optimize management decisions in logistics as part of ensuring the company’s sustainable development.

Integration of Information Technology and Machine Learning to Improve the Efficiency of IoT-Based Logistics Systems

In today's digital era, efficiency in supply chain management and logistics is the main key to maintaining business competitiveness. This article discusses the integration of Information Technology (IT) and Machine Learning (ML) in Internet of Things (IoT)-based logistics systems to improve operational efficiency. By leveraging IoT sensors for real-time data collection and ML algorithms for predictive analysis, the system is able to optimize inventory management, route planning, and preventive maintenance. The case studies discussed in this article show that the use of ML in IoT-based logistics systems can reduce delivery times, lower operational costs, and increase responsiveness to changes in market demand. The results of this study are expected to provide insight for system developers and logistics managers in implementing advanced technologies to address challenges in the modern logistics industry.

An effective hybrid meta-heuristic method for the simultaneous batch production and transportation problem in additive manufacturing

One notable advancement in additive manufacturing (AM) is the mobile mini-factory, which uses a truck equipped with an AM machine to produce orders while en-route to customers' locations. This offers potential benefits such as reduced delivery times and storage expenses for companies. This study investigates a simultaneous batch production and transportation problem (denoted by SBPTP) in additive manufacturing. To solve this problem, a mixed integer linear programming (MILP) model is first formulated. Then, to solve large-scale problems, a meta-heuristic method (denoted by SA-CP) combining a simulated annealing (SA) algorithm, an ant colony optimisation algorithm (ACO) and a cutting-plane algorithm is developed, in which the assignment subproblem is dealt with the SA, the simultaneous production and transportation subproblem is dealt with the ACO, and finally the current solution is further improved by the cutting-plane algorithm. Computational experiments are conducted on both randomly generated instances and modified benchmark instances. The results demonstrate that the SA-CP is very effective since it can obtain the solutions with an average relative percentage gap 0.16% on randomly generated instances and −0.09% on modified benchmark instances within less than 180 CPU seconds, compared to those obtained by solving the MILP model directly with CPLEX within 1 h.

Zara Tech Trail: Futuristic Autonomous Robocart for Cutting-Edge Multi-Perspective Delivery System

The autonomous delivery system presented in this project utilizes a RoboCart equipped with GPS navigation to seamlessly transport products from source to destination. The system, powered by electric charging, ensures timely and secure delivery to end customers, featuring a specialized hand gripper for careful product handling. Designed for diverse applications such as commercial purposes, personal use, and industries including hotels, this fully autonomous cart incorporates a password enabled security feature to guarantee user verification. Positioned as a cutting-edge technological solution, this project aims to effectively address the last mile delivery challenge, presenting a potential to significantly reduce delivery times and contribute to societal benefits. The success of this initiative holds the promise of substantial positive impacts on the Society.

Efficient proton arc optimization and delivery through energy layer pre-selection and post-filtering.

Proton arc therapy (PAT) has emerged as a promising approach for improving dose distribution, but also enabling simpler and faster treatment delivery in comparison to conventional proton treatments. However, the delivery speed achievable in proton arc relies on dedicated algorithms, which currently do not generate plans with a clear speed-up and sometimes even result in increased delivery time. This study aims to address the challenge of minimizing delivery time through a hybrid method combining a fast geometry-based energy layer (EL) pre-selection with a dose-based EL filtering, and comparing its performance to a baseline approach withoutfiltering. Three methods of EL filtering were developed: unrestricted, switch-up (SU), and switch-up gap (SU gap) filtering. The unrestricted method filters the lowest weighted EL while the SU gap filtering removes the EL around a new SU to minimize the gantry rotation braking. The SU filtering removes the lowest weighted group of EL that includes a SU. These filters were combined with the RayStation dynamic proton arc optimization framework energy layer selection and spot assignment (ELSA). Four bilateral oropharyngeal and four lung cancer patients' data were used for evaluation. Objective function values, target coverage robustness, organ-at-risk doses and normal tissue complication probability evaluations, as well as comparisons to intensity-modulated proton therapy (IMPT) plans, were used to assess plan quality. The SU gap filtering algorithm performed best in five out of the eight cases, maintaining plan quality within tolerance while reducing beam delivery time, in particular for the oropharyngeal cohort. It achieved up to approximately 22% and 15% reduction in delivery time for oropharyngeal and lung treatment sites, respectively. The unrestricted filtering algorithm followed closely. In contrast, the SU filtering showed limited improvement, suppressing one or two SU without substantial delivery time shortening. Robust target coverage was kept within 1% of variation compared to the PAT baseline plan while organs-at-risk doses slightly decreased or kept about the same for all patients. This study provides insights to accelerate PAT delivery without compromising plan quality. These advancements could enhance treatment efficiency and patientthroughput.

Blockchain-enabled platform-as-a-service for production management in off-site construction design using openBIM standards

Integrating building information modeling (BIM) with emerging blockchain technology has gained considerable attention in addressing design collaboration problems like poor data security, weak traceability, and low transparency in off-site construction. However, blockchain implementations in off-site construction design, especially with openBIM standards, currently lack standardized workflow management and methods for ensuring data completeness within the blockchain environment. To bridge these gaps, this paper presents a novel blockchain-enabled platform-as-a-service (PaaS), specifically tailored for off-site construction to enhance interoperability and collaborative design efficiency using openBIM standards. The proposed PaaS offers two significant intellectual contributions: (1) a data contract system that manages openBIM workflow standardization and (2) a data escrow system that ensures the completeness and integrity of openBIM data. The data contract system utilizes an innovative asynchronous smart contract algorithm to streamline workflow checks, while the data escrow system introduces a new blockchain-driven data-checking strategy to ensure openBIM completeness at the data level. Besides, a PaaS architecture is developed to clarify the interaction logic between these two systems. The PaaS is validated and evaluated in openBIM-based design collaborations from a real-world project, with results indicating improvements: a 38% time-saving in design issue coordination and a 32% reduction in design data delivery time. Furthermore, the platform received favorable feedback from project participants, validating the approach and confirming the PaaS as a valuable tool for enhancing design collaboration in off-site construction.

Stereotactic body radiation therapy (SBRT) for prostate cancer: Improving treatment delivery efficiency and accuracy

PurposeIn stereotactic body radiation therapy (SBRT) for prostate cancer, intrafraction motion is an important source of treatment uncertainty as it could not be completely smoothed through fractionation. Herein, we compared different arrangements and beam qualities for extreme hypofractionated treatments to minimize beam delivery time and so intrafractional errors. MethodsA retrospective dataset of 11 patients was used. Three volumetric modulated arc therapy (VMAT) beam arrangements were compared for a prescription dose of 40 Gy/5 fractions: two full arcs, 6 MV flattening filter free (FFF); one full arc, 6 MV FFF; one full arc, 10 MV FFF. A plan quality index was defined to compare achievement of the planning goals. Plan complexity was evaluated with the modulation factor. Dose delivery accuracy and efficiency were measured with patient-specific quality assurance plans. ResultsAll treatment plans fulfilled all dose objectives. No statistical differences were found both in plan quality and complexity. Very accurate dose delivery was achieved with the three arrangements, with mean γ passing rates >96.5 % (2 %/2 mm criteria). Slightly but significantly higher γ passing rates were observed with single-arc 6 MV FFF. Contrariwise, statistically significant reductions of the delivery time were obtained with single-arc geometries: the average delivery times were 1.6 min (−46.1 %) and 1.3 min (−56.2 %) for 6 and 10 MV FFF respectively. ConclusionsThe high-quality, very fast and accurate dose delivery of single-arc plans confirmed the suitability of this arrangement for prostate SBRT. In particular, the significant reduction of delivery time would improve treatment robustness against intrafraction prostate motion.

1860: Inclusion of delivery dynamics into optimization reduces delivery time for liver IMPT treatments

1860: Inclusion of delivery dynamics into optimization reduces delivery time for liver IMPT treatments

Developing an integrated blood supply chain network in disaster conditions considering multi-purpose capabilities

PurposeThis study aims to minimize the cost and time of blood delivery in the whole blood supply chain network (BSCN) in disaster conditions. In other words, integrating all strategic, tactical and operational decisions of three levels of blood collection, processing and distribution leads to satisfying the demand at the right time.Design/methodology/approachThis paper proposes an integrated BSCN in disaster conditions to consider four categories of facilities, including temporary blood collection centers, field hospitals, main blood processing centers and medical centers, to optimize demand response time appropriately. The proposed model applies the location of all permanent and emergency facilities in three levels: blood collection, processing and distribution. Other essential decisions, including multipurpose facilities, emergency transportation, inventory and allocation, were also used in the model. The LP metric method is applied to solve the proposed bi-objective mathematical model for the BSCN.FindingsThe findings show that this model clarifies its efficiency in the total cost and blood delivery time reduction, which results in a low carbon transmission of the blood supply chain.Originality/valueThe researchers proposed an integrated BSCN in disaster conditions to minimize the cost and time of blood delivery. They considered multipurpose capabilities for facilities (e.g. field hospitals are responsible for the three purposes of blood collection, processing and distribution), and so locating permanent and emergency facilities at three levels of blood collection, processing and distribution, support facilities, emergency transportation and traffic on the route with pollution were used to present a new model.

Two-Stage Delivery System for Last Mile Logistics in Rural Areas: Truck–Drone Approach

In rural areas of China, the challenges of efficient and cost-effective distribution are exacerbated by underdeveloped infrastructure and low population density, with last mile logistics distribution posing a significant obstacle. To address the gap in drone application for last mile logistics in rural areas, a truck–drone distribution model was developed based on the specific conditions of rural regions. The improved fuzzy C-means algorithm (FCM) and genetic simulated annealing algorithm (GASA) were employed to tackle real−world cases in rural areas. The focus of the truck–drone system is to optimize the rural logistics distribution process, reduce delivery time, and minimize costs while considering factors such as maximum mileage of trucks and drones as well as customer priority. Compared to traditional methods, this system has demonstrated notable improvements in distribution efficiency and cost reduction, offering valuable insights for practical drone applications in last mile rural logistics.

Impact of Drone Battery Recharging Policy on Overall Carbon Emissions: The Traveling Salesman Problem with Drone

This study investigates the traveling salesman problem with drone (TSP-D) from a sustainability perspective. In this problem, a truck and a drone simultaneously serve customers. Due to the limited battery and load capacity, the drone temporarily launches from and returns to the truck after each customer visit. Previous studies indicate the potential of deploying drones to reduce delivery time and carbon emissions. However, they assume that the drone battery is swapped after each flight. In this study, we analyze the carbon emissions of the TSP-D under the recharging policy and provide a comparative analysis with the swapping policy. In the recharging policy, the drone is recharged simultaneously on top of the truck while the truck travels. A simulated annealing algorithm is proposed to solve this problem. The computational results demonstrate that the recharging policy can provide faster delivery and lower emissions than the swapping policy if the recharging is fast enough.

Applying Tension to the Transcervical Foley Balloon and Delivery Times in Term Nulliparous Women Undergoing Induction of Labor: A Randomized Controlled Trial.

To investigate the effects of applying tension to a transcervical Foley balloon on delivery time in term nulliparous patients undergoing labor induction. This cluster randomized clinical trial included 279 term nulliparous women presenting for labor induction with a plan for cervical ripening through transcervical Foley balloon placement. Participants were assigned to either the tension group (n=138) or the no-tension group (n=141) on the basis of randomized, weekly clusters (26 total clusters). The primary outcome measured was the time from initial Foley balloon insertion to delivery. Secondary outcomes included cesarean delivery rates, peripartum infection, and neonatal intensive care unit (NICU) admission. Our prior data suggested that delivery time in the tension group would be about 1,053 minutes. We estimated a sample size of 260 (130 per group, 26 clusters) on the basis of a 25% difference, power of 80%, and two-sided α of 0.05. A total of 279 term nulliparous patients were included in the analysis. The median time from Foley placement to delivery was 1,596 minutes (range 430-3,438 minutes) for the tension group and 1,621 minutes (range 488-3,323 minutes) for the no-tension group ( P =.8); similar results were noted for time to vaginal delivery. No significant differences were observed in the secondary outcomes, including the rates of cesarean delivery (34.1% vs 29.8%, P =.7), peripartum infection, and NICU admission, between the two groups. Applying tension to a transcervical Foley balloon in term nulliparous women undergoing labor induction did not significantly reduce delivery time or improve secondary outcomes. ClinicalTrials.gov , NCT05404776.

Multi Objective and Multi-Product Perishable Supply Chain with Vendor-Managed Inventory and IoT-Related Technologies

With the emergence of the fourth industrial revolution, the use of intelligent technologies in supply chains is becoming increasingly common. The aim of this research is to propose an optimal design for an intelligent supply chain of multiple perishable products under a vendor-managed inventory management policy aided by IoT-related technologies to address the challenges associated with traditional supply chains. Various levels of the intelligent supply chain employ technologies such as Wireless Sensor Networks (WSNs), Radio Frequency Identification (RFID), and Blockchain. In this paper, we develop a bi-objective nonlinear integer mathematical programming model for designing a four-level supply chain consisting of suppliers, manufacturers, retailers, and customers. The model determines the optimal network nodes, production level, product distribution and sales, and optimal choice of technology for each level. The objective functions are total cost and delivery times. The GAMS 24.2.1 optimization software is employed to solve the mathematical model in small dimensions. Considering the NP-Hard nature of the problem, the Grey Wolf Optimizer (GWO) algorithm is employed, and its performance is compared with the Multi-Objective Whale Optimization Algorithm (MOWOA) and NSGA-III. The results indicate that the adoption of these technologies in the supply chain can reduce delivery times and total supply chain costs.

Control of Autonomous Aerial Vehicles to Transport a Medical Supplies

Public health surveillance must guarantee the safety of people by limiting human mobility, in cases of isolation, through product deliveries, making it necessary to use drones to guarantee safety because they play a crucial role in several sectors. The literature review highlights the benefits of automation in-home delivery using drones, focusing on time efficiency and competitiveness in various sectors, and provides crucial design parameters to ensure its implementation in urban areas using different control techniques. A contribution was proposed to a solution that aims to realize the honeycomb design, which drones create during flight, controlled by a flight and delivery algorithm in a simulation environment applying an iterative methodology and continuous transport tests. medical burden. The results indicate a qualitative advance in the successful creation of simulated terrain, although the lack of numerical data on takeoffs and landings suggests the need for additional quantitative measurements. The current results support the efficiency of drones in route planning, precise management of medical cargo, and reduction of delivery time is numerical evidence that reinforces the robustness of the solution. In conclusion, this study developed a functional prototype to control drones with a flight planning algorithm and a swarm formation system for the transport of medical supplies in urban environments, although the need for future research to implement artificial intelligence technologies is noted. that improve transportation efficiency.

Using Model-Based Systems Engineering Tools in Software Development

The article discusses the application of model-based systems engineering in software development. MBSE offers a new model-based approach to viewing complex systems, allowing data and information to be effectively shared across different product life cycle stages. The paper examines the model-based systems engineering methodology, its benefits, and its impact on requirements, design, analysis, and verification within the product life cycle. The study highlights the relevance of using model-based systems engineering to modern systems engineering practices, especially in the context of software engineering. The study results demonstrate the benefits of model-based systems engineering and its ability to optimize development processes, improve product quality, and reduce delivery times. The scientific novelty of the article lies in the application and research of the model-based systems engineering in the context of software development, which represents a significant contribution to the development of modern systems engineering practices and development technologies.

The relationship of inspirational leadership and green supply chain management to reinforce the performance of SMEs

The aim of this research is to analyze the relationship between inspirational leadership and performance and the relationship between green supply chain management and performance improvement. This research method is quantitative. Research data was obtained by distributing online questionnaires via social media, the questionnaire was designed using statement items with a Likert scale of 1 to 7. The respondents for this research were 678 SMEs owners in Indonesia who were determined using the simple random sampling method. The data analysis technique for this research uses partial least squares (PLS) structural equation modeling (SEM) analysis with SmartPLS 3.0 software data processing tools. The stages of research analysis are validity testing, reliability testing and hypothesis testing (significance). The results of the research show that inspirational leadership has a positive and significant effect on performance. With the existence of inspirational leadership on HR performance it will have a positive effect if employees get trust and good examples from leaders, and employees get inspiration and motivation so they can create new innovations that make the company more advanced. Green supply chain management has a positive and significant effect on improving performance. The concept in green supply chain management provides the possibility for organizations to improve process efficiency, waste recycling management, the ability to attract new suppliers and consumers, organizations can save costs, reduce delivery times through collaboration with suppliers and consumers which can ultimately improve operational performance.