Search Results (272)

Background: The growth of parcel volumes in urban areas, largely driven by e-commerce, has increased the complexity of pickup and delivery operations. To meet demands for cost efficiency, flexibility, and sustainability, CEP (Courier, Express, and Parcel) operators increasingly outsource segments of their last-mile networks. Methods: This study proposes a novel multidimensional cost model for outsourcing, integrating five key variables: transport unit type (parcel/pallet), service phase (pickup/delivery), vehicle category, powertrain type, and delivery point type. The model applies correction coefficients based on internal operational costs, further adjusted for location and service quality using a bonus/malus mechanism. Results: Each cost component is calculated independently, enabling full transparency and route-level cost tracking. A real-world case study was conducted using operational data from a CEP operator in Bosnia and Herzegovina. The model demonstrated improved accuracy and fairness in cost allocation, with measurable savings of up to 7% compared to existing fixed-price models. Conclusions: The proposed model supports data-driven outsourcing decisions, allows tailored cost structuring based on operational realities, and aligns with sustainable last-mile delivery strategies. It offers a scalable and adaptable tool for CEP operators seeking to enhance cost control and service efficiency in complex urban environments. Full article

This paper investigates sustainable decision systems within green E-business models by analyzing how different O2O (online-to-offline) fulfillment structures affect emission-reduction efforts and pricing strategies in a two-tier supply chain consisting of a manufacturer and a new retailer. Three practical sales formats—package self-pickup, nearby delivery, and hybrid—are modeled using Stackelberg game frameworks that incorporate key factors such as inconvenience cost, logistics cost, processing fees, and emission-reduction coefficients. Results show that the manufacturer’s emission-reduction decisions and both parties’ pricing strategies are highly sensitive to cost conditions and consumer preferences. Specifically, higher inconvenience and abatement costs consistently reduce profitability and emission efforts; the hybrid model exhibits threshold-dependent advantages over single-mode strategies in terms of carbon efficiency and economic returns; and consumer green preference and distance sensitivity jointly shape optimal channel configurations. Robustness analysis confirms the model’s stability under varying parameter conditions. These insights provide theoretical and practical guidance for firms seeking to develop adaptive, low-carbon fulfillment strategies that align with sustainability goals and market demands. Full article

Background/Objectives: This study investigates the evolution of hospital capacity and utilization in California between 2003 and 2023, focusing on emergency departments (EDs) and trauma centers (TCs). We seek to document structural changes in the healthcare delivery system with respect to hospital-based emergency and trauma services. Methods: This analysis examines changes in population demographics, hospital resources, and patient utilization patterns across facility types. Given the significant increase in the proportion of the population aged 65+ and the documented higher use of emergency and trauma services by this population, we expected to observe an expansion in ED and trauma service capacity and utilization. Results: Utilizing a comprehensive dataset of California general acute care hospitals over this 20+ year period, our descriptive analysis reveals major shifts in the healthcare delivery system, notably the increased prominence of hospitals with EDs, particularly those designated as trauma centers. Findings indicate that, while the overall number of hospitals and licensed beds has slightly decreased, facilities with EDs, especially trauma centers, have increased their capacity and manage a greater proportion of inpatient admissions and ED visits. Conclusions: The increase in ED visits and inpatient admissions at trauma centers, contrasted with decreases in both capacity and utilization at non-trauma hospitals, indicates a significant restructuring of the health delivery system with significant implications for healthcare policy, financing, operations, and affordability. The high and increasing percentage of inpatient admissions originating from hospital EDs and from hospitals with trauma centers suggests a need for policies that foster integration between ED and inpatient care and the broader healthcare delivery system, while at the same time managing the increase in prices and costs associated with growing emergency services utilization. Further research is needed to explore the implications of these trends, particularly concerning their impact on the affordability of healthcare in the US. Full article

The large-scale integration of renewable energy and the use of high-energy-consuming equipment in agricultural parks have a great influence on the security of rural distribution networks. To ensure reliable power delivery for residential and agricultural activities and sustainable management of distributed energy resources, this paper develops a distributed generation-supported interactive optimization framework coordinating distribution networks and agricultural parks. Specifically, a wind–photovoltaic scenario generation method based on Copula functions is first proposed to characterize the uncertainties of renewable generation. Based on the generated scenario, a bi-level interactive optimization framework consisting of a distribution network and agricultural park is constructed. At the upper level, the distribution network operators ensure the security of the distribution network by reconfiguration, coordinated distributed resource dispatch, and dynamic price compensation mechanisms to guide the agricultural park’s electricity consumption strategy. At the lower level, the agricultural park users maximize their economic benefits by adjusting controllable loads in response to price compensation incentives. Additionally, an improved particle swarm optimization combined with a Gurobi solver is proposed to obtain equilibrium by iterative solving. The simulation analysis demonstrates that the proposed method can reduce the operation costs of the distribution network and improve the satisfaction of users in agricultural parks. Full article

Crowdshipping establishes a short-term connection between shippers and individual carriers, bridging the service requirements in last-mile logistics. From the perspective of a carrier operating multiple vehicles, this study considers the challenge of maximizing profits by optimizing bid strategies for delivery prices and transportation conditions in the context of bid-based crowdshipping services. We considered two types of bid strategies: a price bid that adjusts the RFQ freight charge and a multi-attribute bid that scores both price and service quality. We formulated the problem as a Markov decision process (MDP) to represent uncertain and sequential decision-making procedures. Furthermore, given the complexity of the newly proposed problem, which involves multiple vehicles, route optimizations, and multiple attributes of bids, we employed a reinforcement learning (RL) approach that learns an optimal bid strategy. Finally, numerical experiments are conducted to illustrate the superiority of the bid strategy learned by RL and to analyze the behavior of the bid strategy. A numerical analysis shows that the bid strategies learned by RL provide more rewards and lower costs than other benchmark strategies. In addition, a comparison of price-based and multi-attribute strategies reveals that the choice of appropriate strategies is situation-dependent. Full article

Transportation planning is a complex process that aims to achieve the maximum level of effectiveness in terms of costs, usage of transport resources, reliability of deliveries, and minimizing the negative impact on the environment. Most traditional models focus on cost minimization at the expense of risk, road dynamics, and emissions constraints. In contrast, the current paper presents a mixed-integer linear programming (MILP) model for scheduling fleets, selecting transportation modes in multiple modes of transportation, and meeting emissions regulation requirements according to dynamic transportation requirements. Risk-aware routing and taking the factor of congestion and CO₂ emission limits proposed by the government into consideration, this model can offer a more efficient and flexible optimization strategy. From the case study, we observe the significant result that the proposed model achieves, a 23% reduction in transport costs, a 25% improvement in fleet use, a 33.3% decrease in the delivery delay, and a 24.6% decrease in CO₂ emissions. The model dynamically delivers shipments utilizing both road and rail transportation and improves mode choice by minimizing idle vehicle time. This is confirmed through sensitivity analysis which addresses factors such as traffic congestion, changing fuel prices, and changing environmental standards. Full article

Background: Oral diseases represent a constant burden for health care and socio-economic systems as they are correlated to other non-communicable diseases. The aim of the proposed intervention is to test the effect of daily tooth brushing and oral health education on the oral health status of kindergarten children. Methods: The protocol will be conducted based on a previous epidemiological survey and conducted over 24 months; it has been developed on different levels. Dental hygienists will receive specific training to deliver oral health promotion to children and nursery educators. Training will focus on tailoring key messages to the specific age at visit; this will be outlined in the care pathway and offer practical preparation for delivering interventions and a toothpaste/toothbrush scheme. It will also, involving involve offering free daily tooth brushing to every 4–6-year-old child attending nursery. Data will be collected in four kindergartens in the capital or metropolitan areas, two kindergartens each in two large cities, and one kindergarten each in four villages from different geographic areas. Procedures used to assess the outcomes of each activity will be tailored to specific outcomes. Daily tooth-brushing activities will be monitored using qualitative research. A cost analysis including the distribution of necessary materials and correct delivery of products that shows price trends and percentage differences over the time span as well as consumer price index evaluation for the given time span will be conducted. Clinical outcomes will be evaluated using the caries incidence rate; this will be calculated for each tooth as the unit of analysis and evaluated using a multi-step approach. Discussion: Downstream oral health prevention interventions, like clinical prevention and oral health promotion, aim to enhance children’s quality of life. The program’s goal is to progress towards upstream interventions for a more significant impact. Full article

Continuous integration and delivery are essential for modern software development, enabling teams to automate testing, streamline deployments, and deliver high-quality software more efficiently. As DevOps adoption grows, selecting the right CI/CD platform is essential for optimizing workflows. Azure DevOps and GitHub, both under Microsoft, are leading solutions with distinct features and target audiences. This paper compares Azure DevOps and GitHub, evaluating their CI/CD capabilities, scalability, security, pricing, and usability. It explores their integration with cloud environments, automation workflows, and suitability for teams of varying sizes. Security features, including access controls, vulnerability scanning, and compliance, are analyzed to assess their suitability for organizational needs. Cost-effectiveness is also examined through licensing models and total ownership costs. This study leverages real-world case studies and industry trends to guide organizations in selecting the right CI/CD tools. Whether seeking a fully managed DevOps suite or a flexible, Git-native platform, understanding the strengths and limitations of Azure DevOps and GitHub is crucial for optimizing development and meeting long-term scalability goals. Full article

Additive manufacturing, also known as 3D printing, is becoming more and more popular because of its wide range of materials and flexibility in design. Layer by layer, 3D complex structures can be generated by the revolutionary computer-aided process known as 3D bioprinting. It is particularly crucial for youngsters and elderly patients and is a useful tool for tailored pharmaceutical therapy. A lot of research has been carried out recently on the use of polysaccharides as matrices for tissue engineering and medication delivery. Still, there is a great need to create affordable, sustainable bioink materials with high-quality mechanical, viscoelastic, and thermal properties as well as biocompatibility and biodegradability. The primary biological substances (biopolymers) chosen for the bioink formulation are proteins and polysaccharides, among the several resources utilized for the creation of such structures. These naturally occurring biomaterials give macromolecular structure and mechanical qualities (biomimicry), are generally compatible with tissues and cells (biocompatibility), and are harmonious with biological digesting processes (biodegradability). However, the primary difficulty with the cell-laden printing technique (bioprinting) is the rheological characteristics of these natural-based bioinks. Polysaccharides are widely used because they are abundant and reasonably priced natural polymers. Additionally, they serve as excipients in formulations for pharmaceuticals, nutraceuticals, and cosmetics. The remarkable benefits of biological polysaccharides—biocompatibility, biodegradability, safety, non-immunogenicity, and absence of secondary pollution—make them ideal 3D printing substrates. The purpose of this publication is to examine recent developments and challenges related to the 3D printing of stimuli-responsive polysaccharides for site-specific medication administration and tissue engineering. Full article

This study examined the impact of location differentials (LDs) on soybean futures trading in South Africa. This study uses a systematic approach, employing the Autoregressive Moving Average (ARMA) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models to analyze relationships between soybean futures prices and LDs. The results suggest that LDs have caused price stabilization for most of the contract months, while the variability and occasional extremes in the spot price increased. Post-LD analysis showed that the volatility was lower, with a normalization of price structures, but, still, regional disparities were driven by transport costs and logistical issues. LDs also affected silo utilization, and the rates of re-delivery differed among regions, reflecting local market dynamics and operational efficiencies. This, in essence, suggests that LDs act to enhance the predictability of markets and price harmonization; LDs also equally require concerted interventions in regional disparities and optimization of market performances. Future studies need to determine the impact that LDs, over a long period, have on market efficiency, regional trade, and general economy-wide indicators like farmers’ incomes and rural development. Full article

In order to address the diverse and personalized needs of consumers for fresh products, as well as to enhance the efficiency and safety of fresh product delivery, this paper proposes an integer programming model aimed at minimizing total distribution costs. The model takes into account the cold storage multi-temperature joint distribution mode, carbon emission costs, and actual constraints associated with the distribution process of fresh products. To solve this model, an improved salp swarm algorithm (SSA) has been developed. The feasibility and effectiveness of both the proposed model and algorithm are demonstrated using R110 data from the Solomon standard calculation example. Research findings indicate that compared to traditional single-product temperature distribution modes, the multi-temperature joint distribution mode achieves reductions in total distribution costs and vehicle quantities by 45.4% and 72.2%, respectively. Furthermore, it is observed that total distribution costs increase with rising unit carbon tax prices; however, the rate of growth gradually diminishes over time. Additionally, a reduction in vehicle load capacity results in a continuous rise in total delivery costs after reaching a certain turning point. When compared to conventional SSAs and genetic algorithms, the proposed algorithm demonstrates superior performance in generating optimal multi-temperature joint distribution route schemes for fresh products. Full article

Given the current need to improve the thermal and energy performance of buildings, special attention has been given to the building envelope and materials. Concrete sandwich panels (CSPs) are versatile composite construction elements whose popularity is increasing given their properties, e.g., good thermal and acoustic insulation, durability, and fire resistance. Nevertheless, besides their properties, it is important to evaluate the sustainability of composite panels under development. This work aims to assess the eco-efficiency of six CSPs with distinct insulation materials: lightweight concrete (LWC), cork, glass wool, and expanded polystyrene (EPS). Coupling both life cycle assessment (LCA) and life cycle costing (LCC) analysis, this study derives eco-efficiency indicators to inform decisions regarding CSP environmental and economic performances. The results of the LCA and LCC showed that the high-performance concrete (HPC) layer was the main hotspot of the CSPs in all scenarios. Moreover, the best scenario changed when different environmental impact categories were considered. Thus, using multiple environmental indicators is recommended to avoid problem-shifting. Considering the final cost, the CSP with cork is the most expensive panel to produce, with the other five options having very similar manufacturing prices. On average, raw material inputs, labour, and material delivery account for 62.9%, 18.1%, and 17.1% of the total costs, respectively. Regarding the eco-efficiency results, the most eco-efficient scenario changed with the environmental indicator used. Cork seems to be the best option when considering the carbon footprint of the panels, whereas when considering other environmental indicators, the recycled EPS scenario has the best eco-efficiency and the CSP with cork the worst. Full article

Developing policy instruments related to urban freight, such as congestion pricing, urban consolidation schemes, and off-hours delivery, requires an understanding of the distribution of shipment delivery times. Furthermore, agent-based urban freight simulators use relevant information (shipment delivery time distribution or vehicle tour start time distribution) as input to simulate tour generation. However, studies focusing on shipment delivery time-period selection modeling are very limited. In this study, we propose a method using GPS trajectory data from the Tokyo Metropolitan Area to estimate a shipment delivery time-period selection model based on pseudo-shipment records inferred from GPS data. The results indicate that shipment distance, size, and destination attributes can explain the delivery times of goods. Moreover, we demonstrate the practicality of the model by comparing the simulation result with the observed data for three areas with distinct characteristics, concluding that the model could be applied to urban freight simulation models for accurately reproducing spatial heterogeneity in shipment delivery time periods. This study contributes to promoting smart city development and management by proposing a method to use big data to better understand deliveries and support the development of relevant advanced city logistics solutions. Full article

Background/Objectives: Pulmonary delivered tobramycin (TOB) is a standard treatment for Pseudomonas aeruginosa lung infections, that, along with Staphylococcus aureus, is one of the most common bacteria causing recurring infections in CF patients. However, the only available formulation on the market containing tobramycin, TOBI^®, is sold at a price that makes the access to the treatment difficult. Therefore, this work focuses on the development and characterization of an ionic complex between a polyelectrolyte, hyaluronic acid (HA) and its salt, sodium hyaluronate (NaHA), and TOB to be formulated as an inhalable dry powder. Methods: The solid state complex obtained by spray drying technique was physicochemically characterized by infrared spectroscopy, thermal analysis and X-ray diffraction, confirming an ionic interaction for both complexes. Results: The powder density, geometric size, and morphology along with the aerodynamic performance showed suitable properties for the powder formulations to reach the deep lung. Moisture uptake was found to be low, with the complex HA-TOB remaining physicochemically unchanged, while the NaHA-TOB required significant protection against humidity. The biopharmaceutical in vitro experiments showed a rapid dissolution which can have a positively impact in reducing side effects, while the drug release study demonstrated a reversible polyelectrolyte–drug interaction. Microbiological experiments against P. aeruginosa and S. aureus showed improved bacterial growth inhibition and bactericidal efficacy, as well as better inhibition and eradication of biofilms when compared with to TOB. Conclusions: A simple polyelectrolyte-drug complex technique represents a promising strategy for the development of antimicrobial dry powder formulations for pulmonary delivery in the treatment of cystic fibrosis (CF) lung infections. Full article

The purpose of this paper is to examine the optimization of the HIV drug supply chain, with a dual focus on minimizing freight costs and delivery times. With the help of a dataset containing 10,325 instances of supply chain transactions, key variables, including “Country”, “Vendor INCO Term”, and “Shipment Mode”, were examined in order to develop a predictive model using Artificial Neural Networks (ANN) employing a Multi-Layer Perceptron (MLP) architecture. A set of ANN models were trained to forecast “freight cost” and “delivery time” based on four principal design variables: “Line Item Quantity”, “Pack Price”, “Unit of Measure (Per Pack)”, and “Weight (Kilograms)”. According to performance metrics analysis, these models demonstrated predictive accuracy following training. An optimization algorithm, configured with an “active-set” algorithm, was then used to minimize the combined objective function of freight cost and delivery time. Both freight costs and delivery times were significantly reduced as a result of the optimization. This study illustrates the potent application of machine learning and optimization algorithms to the enhancement of supply chain efficiency. This study provides a blueprint for cost reduction and improved service delivery in critical medication supply chains based on the methodology and outcomes. Full article