Facility Location Research Articles

Medicare billing and utilization trends in vascular surgery

ObjectiveWithin the past decade, Medicare Part B reimbursements for various surgical procedures have been declining, whereas health care expenses continue to increase. As a result, hospitals may increase service charges to offset losses in revenue, which may disproportionately affect underinsured patients. Our analysis aimed to characterize Medicare billing and utilization trends across common vascular surgical procedures. MethodsThe 2017 to 2021 Medicare Physician and Other Practitioners by Provider and Service dataset was queried for Current Procedural Terminology (CPT) codes for common vascular surgery procedures. The average charges, reimbursements, charge-to-reimbursement ratios, and service counts were calculated for the most common interventions performed by vascular surgeons. Data was stratified by care setting, facility (inpatient and outpatient hospital) vs non-facility locations. All monetary values were adjusted to the 2021 United States dollars to account for inflation. ResultsFor facility settings, the mean charge billed to Medicare Part B increased from $3708 to $3952 (6.6%) from 2017 to 2021, with the average charge-to-reimbursement ratio increasing from 7.2 to 8.6. There were 17 of the 19 facility procedures that had a decline in reimbursements, decreasing from an average of $558 to $499 (−10.4%). Stab phlebectomy had the largest individual decrease in facility reimbursement (−53.5%), followed by above-knee amputation (−11.3%) and below-knee amputation (−11.0%). Both non-facility charges (−10.8%) and reimbursements (−12.2%) declined over the study period. Procedural utilization remained stable from 2017 to 2019. Tibial and femoral-popliteal atherectomy had increases of 45.9% and 33.7%, respectively, in overall procedural utilization when performed in non-facility settings from 2017 to 2019. ConclusionsOur analysis of vascular surgery procedures billed to Medicare Part B from 2017 to 2021 demonstrates an increase in charges, a decline in reimbursements, and a resultant increase in charge-to-reimbursement ratios for facility care settings. In contrast, non-facility charges have decreased in the face of declining reimbursements. These markups in submitted charges in facility locations may serve as an additional barrier to accessing care for patients who are underinsured.

Double resolvability parameters of fosmidomycin anti-malaria drug and exchange property

The practical and theoretical significance of the resolvability parameter makes it an important factor, particularly in the context of network analysis. Its significance is seen in various applications and consequences: Network security, efficient routing, social network analysis, facility location, and site selection. This article finds the double resolvability parameters of the fosmidomycin anti-malaria drug. Resolvability parameters like double metric, double edge metric, and double mixed metric dimensions of fosmidomycin anti-malaria drug also hold exchange properties in the molecular graph of fosmidomycin. We convert the molecular structures of fosmidomycin into molecular graphs and then find some resolvability parameters.

A hybrid multi-criteria decision-making model for waste facilities location considering system resilience

The location of waste treatment facilities is a crucial strategic decision in urban management. Most existing studies focus on mathematical modeling and algorithm solving for multi-objective models aimed at minimizing cost or pollution. They lack consideration for the resilience of decision solutions in the face of uncertain and unpredictable disruptive incidents. This paper aims to bridge the research gap by proposing a top-down hybrid multi-criteria decision-making model that incorporates cost, pollution, social discontent, and system resilience simultaneously. The proposed model integrates various methods such as integer programming modeling, simulation modeling, and catastrophe analysis. It addresses the challenge that disruptive incidents cannot be precisely expressed. Additionally, it quantitatively analyzes the factors that impact system resilience. Finally, the proposed model is validated by applying it to a waste management network based on the real case of Shenzhen city. The results demonstrate notable differences in the resilience indexes among the solutions in the Pareto-optimal set, which confirms the significance of system resilience from another aspect. Further catastrophe analysis reveals several valuable insights for urban waste management. This paper offers a new perspective to cope with various risks related to waste management and provides a method reference and decision support for the construction of urban resilience.

Sustainable Municipal Solid Waste Management Strategies and Practices: A review

Purpose: This paper introduces a multi-period, multi-objective model for creating an integrated, sustainable municipal solid waste management supply chain. The model includes source separation and a reward-penalty mechanism, aiming to minimize total system costs, greenhouse gas emissions, and environmental impacts on residential areas. Many countries strive to develop efficient solid waste management systems (SWMS) that handle and dispose of daily waste cost-effectively while maintaining sustainability. These systems include waste sources, collection stations, landfills, incinerators, recycling plants, and a transportation network. Methodology: A systematic literature review was conducted on existing literature from various studies. Recent studies were used for this systematic review of sustainable municipal solid waste management strategies and practices. Findings: Decision-makers must design or reconfigure a sustainable MSWM to determine the optimal supply chain network for treating and disposing of daily waste effectively. This paper suggests sustainable waste management strategies and practices from various countries, aiming to determine the best number and locations for facilities and optimal waste flow within the system to minimize daily costs. The primary contribution of this review is the theoretical development of sustainable MSWM practices applicable worldwide. Unique contribution to theory, policy and practice: Cooperation between the private and public sectors involved in solid waste management and the circular economy is imperative to ensure the technical, financial, and social sustainability of waste management systems. Large-scale awareness programs on solid waste management and the circular economy should be implemented, focusing on fostering environmentally responsible behaviors and attitudes, promoting sorting, selective collection, and the recovery of materials and energy from waste.

Mechanical properties of soils in internal waste bank of lignite mines and their primary characteristics

The modern development of urban areas is related to, among others, the location of industrial facilities on the outskirts of cities. More and more often, commercial buildings are founded on areas that have not been used for construction so far. Such areas include, among others: reclaimed lignite mine banks in the Konin region. The man-made soil is a chaotic mixture of fragments of glacial tills and Pliocene clays, often exceeding 20 m in thickness, which is naturally consolidated over time. Due to the method of formation of the embankment, despite the fact that banks are made of natural soil, their strength and deformation characteristics clearly differ from those characteristic of lithologically similar soils deposited as a result of geological processes. In this case, the use of a standard test approach may overestimate the strength and stiffness of the soil. Due to the complex structure of the bank in-situ tests were used for geotechnical exploration: CPTU and FVT, as well as laboratory tests in a triaxial apparatus and an oedometer. The results were compared with the results of studies conducted in similar naturally deposited soils. The obtained results provide valuable geotechnical characteristics of the embankment soil, which in its large fragments is built of natural soil clasts. The obtained results indicate a relatively small change in the geotechnical properties of the soils incorporated into the embankment within individual clasts of the natural soil (on a local scale) and a clear deterioration in the scale of the entire embankment, treated as the impact zone of building structures.

Emergency logistics siting-path question for engineering construction projects based on quantum particle swarm algorithm

Emergency material security is the key to post-disaster emergency relief in complex environment engineering construction projects. Aiming at the emergency logistics center site path planning problem, the uncertain parameters of congestion time and maximum rescue time are described using trapezoidal fuzzy numbers, and the emergency logistics site path model of the two-phase engineering construction project is established. A quantum particle swarm optimization model is designed to avoid premature population convergence and maintain population diversity. The particle swarm algorithm uses the quantum spin gate’s rotation angle update to represent the particle velocity update. Taking the engineering construction project in the western mountainous area as an example, the validity and applicability of the model are verified through model comparison and sensitivity analysis. The results of the example show that in the complex environment, the quantum particle swarm algorithm takes 29.35 s in convergence time, and the particle swarm algorithm takes 40.12 s, which is 36.69% higher than the efficiency of PSO. In total cost, the quantum particle swarm algorithm has a total cost of 46,632.40 RMB, and the particle swarm algorithm has a total cost of 48,319.51 RMB, which is 3.62% lower than the cost of PSO. The quantum particle swarm algorithm’s total distance is 550.57 km, and the particle swarm algorithm’s total distance is 579.35 km, 5.23% lower than the total distance of PSO. The model established in this study can scientifically select the location of emergency facilities and formulate the emergency rescue path, reduce the response time of emergency rescue and storage costs, and provide decision support for the scheduling of emergency supplies for engineering construction projects in complex and dangerous areas.

Remote sensing and machine learning approach for zoning of wastewater drainage system

This study aimed to optimize drainage in Tiruchirappalli city using Geographic Information Systems (GIS), machine learning, and remote sensing. The integration of these methods involved multiple steps: remote sensing data was used to gather up-to-date information on land use and land cover (LULC), GIS provided a spatial framework for data integration and visualization, and machine learning, specifically a Random Forest (RF) model, was utilized for detailed LULC classification. Hydrological analysis using a Digital Elevation Model (DEM) helped delineate the watershed and drainage network, determining suitable zones for wastewater systems. Key factors such as ground slope, land use, and proximity to water bodies were considered. Buffer analysis was employed to transform and overlay these data layers. For the site suitability analysis, the Analytic Hierarchy Process (AHP) method was employed within the GIS to integrate various weighted factors, enabling a comprehensive assessment of potential locations for wastewater treatment facilities. Wastewater drainage zones were determined to be appropriate for zones 1, 8, 9, 11, 15, and 20, which correspond to areas of 40.40, 1.11, 1.00, 2.04, 1.69, and 2.46 Sq.km, respectively. This comprehensive approach enabled the identification of optimal wastewater drainage zones and suitable locations for treatment facilities, enhancing urban wastewater management.

Solving Optimal Electric Vehicle Charger Deployment Problem

Electric vehicles (EVs) have already been acknowledged to be the most viable solution to the climate change that the entire globe has long been combating. Along the same line, it is a salient subject to expand the availability of EV charging infrastructure, which quintessentially necessitates the optimization of the charger’s locations. This paper proposes to formulate the optimal EV charger location problem into a facility location problem (FLP). As an effort to find an efficient method to solve the well-known nonpolynomial deterministic (NP) hard problem, we present a comparative quantification among several representative solving techniques. This paper features two comprehensive case studies representing regions with an average and a high density of EVs. As such, this paper shows that the proposed framework can lead to successful location optimization with adequate refinement of solving techniques.

Meal-on-wheel Service Facility Location Strategy for Longhua District in Shenzhen based on GIS

With the development of Shenzhen, aging population with needs of Meal-on-Wheel (MOW) service is increasing as well, especially Longhua District. This paper analyses the characteristics of the aging population in Longhua District, and evaluates the accessibility and living quality of current meals service location by applying Geographic Information System (GIS). It finds that the meal service system struggles from low demands and quality, highly depends on government subsidies, lacks of participation from social enterprises, and diversity in service levels. Based on the analysis, proper location strategy is made, and proper MOW service locations are allocated by GIS to extend the coverage of MOW services and suggestions are made properly to promote the improvement of the quality of catering services.

Developing approximation algorithm for elastic competitive facility location model

Developing approximation algorithm for elastic competitive facility location model

A machine learning approach to two-stage adaptive robust optimization

We propose an approach based on machine learning to solve two-stage linear adaptive robust optimization (ARO) problems with binary here-and-now variables and polyhedral uncertainty sets. We encode the optimal here-and-now decisions, the worst-case scenarios associated with the optimal here-and-now decisions, and the optimal wait-and-see decisions into what we denote as the strategy. We solve multiple similar ARO instances in advance using the column and constraint generation algorithm and extract the optimal strategies to generate a training set. We train machine learning models that predict high-quality strategies for the here-and-now decisions, the worst-case scenarios associated with the optimal here-and-now decisions, and the wait-and-see decisions. The models can be applied to problems with varying dimensions. We also introduce novel methods to expedite training data generation and reduce the number of different target classes the machine learning algorithm needs to be trained on. We apply the proposed approach to the facility location, the multi-item inventory control and the unit commitment problems. Our approach solves ARO problems drastically faster than the state-of-the-art algorithms with high accuracy.

EXPRESS: Fairness as a Robust Utilitarianism

This article focuses on the social choice problem in which decisions are based on the utility of multiple stakeholder types. The sum of these utilities – Utilitarian welfare - is one of the most important objectives in solving the social choice problem. While it is the most efficient solution, maximizing Utilitarian welfare may lead to unfair outcomes. However, Encouraging a Utilitarian decision-maker to adopt a fair decision is challenging due to the associated efficiency loss. This article takes a novel perspective by motivating a Utilitarian decision-maker to make fair decisions from an uncertainty-averse standpoint. We study the problem where the proportions of stakeholder types are uncertain and propose a distributionally robust optimization (DRO) model that maximizes the worst-case Utilitarian welfare over an ϕ-divergence-based uncertainty set. We provide three aspects of the relationship between fairness and the uncertain-averse Utilitarian welfare maximization. First, we establish that the worst-case Utilitarian welfare adheres to all five axioms of unfairness-averse cardinal welfare functions with two stakeholder types and satisfies four of these when this number exceeds two. Second, we demonstrate that with the maximal extent of uncertainty aversion, the DRO model identifies the Egalitarian welfare maximizer, which prioritizes fairness. Further, given serveral conventional assumptions, the proposed model selects the Nash welfare maximizer, an objective trade-off between efficiency and fairness, with moderate levels of uncertainty aversion. Lastly, we present numerical studies of two specific instances of the social choice problem – resource allocation and facility location – to show that, as uncertainty aversion increases, our model increasingly emphasizes fairness.

Dispersed starting solutions in facility location: The case of the planar [formula omitted]-median problem

There are many planar multiple facilities location problems for which the optimal locations tend to be spread out. The most popular of these is the planar p-median problem. With this in mind, we propose several procedures to generate sparse configurations as starting solutions. The proposed procedures are easy to implement, and can be used as modules combined in different sequences within heuristics such as a recent trajectory-based procedure that we tested in this paper.The procedures are tested experimentally on a set of 24 large problem instances with up to 10,000 demand points and 100 facilities. We are able to demonstrate that the sparse starting solutions generated by the new procedures lead to significant improvements of final p-median solutions.

Survival outcomes associated with treatment modalities in stage 4 gallbladder neuroendocrine tumors: A nationwide analysis.

e16295 Background: Gallbladder neuroendocrine tumors (GNETs) are rare, heterogeneous, and aggressive tumors of the gallbladder. GNETs usually present in the elderly ( > 60 years) and there are no clear clinical guidelines to treat stage 4 GNETs due to rarity. We evaluated therapeutic options for stage 4 GNETs. Methods: We included stage 4 GNETs diagnosed between 2004-2020 in the National Cancer Database. Treatment was stratified into combination therapy (Comb) - surgery (Sx) and chemotherapy (CT), Sx alone and CT alone. Survival outcomes were expressed as Kaplan-Meier curves and Cox proportional hazard model adjusting for age, Charlson-Deyo score (CDS) and sociodemographic variables like sex, race, ethnicity, insurance status, and facility location. We expressed survival outcomes as median overall survival (mOS, 95% CI) and time-dependent survival as hazard ratio (HR, 95% CI). Results: Of 242 patients with stage 4 GNETs, the median age was 64 years (range 29-90+ years), 62.4% were female, 66.9% had CDS of 0, 22.4% had CDS of 1, 10.8% had CDS ≥2, 98.7% had poorly differentiated tumor (grade 3 or 4), 70.3% were White, 85.9% were non-Hispanic, median size of the primary tumor was 10.7 cm (range 0.8-14.1 cm) and 47.8% were treated at academic or research program. 46 (19.0%) patients received Comb, 171 (70.7%) received CT and 25 (10.3%) patients received Sx alone. The mOS was 11.5 months (9.3-18.4), 9.4 months (7.9-11.2), 4.2 months (2.3-6.3) for Comb, CT and Sx, respectively (P < 0.001). Pairwise log-rank revealed no significant differences between Comb and CT groups (P = 0.07). In univariate Cox analysis, CDS ≥2 showed HR of 1.6 (1.0-2.4; p = 0.04), Sx showed HR of 1.7 (1.1-2.7; P = 0.02), and Medicare had HR of 1.35 (1.02-1.78; p = 0.037). However, on multivariate analysis, uninsured patients (HR = 5.16; 95% CI 1.50-17.7; P = 0.009) and those receiving Sx alone (HR = 2.09; 95% CI 1.27-3.46; P = 0.004) had unfavorable outcomes. Conclusions: The combination of primary site surgery with CT improves survival in stage 4 GNETs as compared to resection alone. However, there is no significant difference in survival in combination therapy and CT. We emphasize the need for robust prospective studies comparing the efficacy of chemotherapy, neoadjuvant and adjuvant chemotherapy for stage 4 GNETs.[Table: see text]

Myofibroblastic sarcoma: A study of demographic and socioeconomic factors.

e23545 Background: Myofibroblastic sarcoma is a type of non-rhabdomyosarcoma soft tissue sarcoma originating from spindle cells of mesenchymal origin with myofibroblastic differentiation. First described in 1998, there is little known about the prognostic factors of this rare and relatively new cancer. This typically non-metastasizing tumor is primarily found in the head, neck, or extremities. Treatment focuses on tumor excision, and the 5-year overall survival of low-grade myofibroblastic sarcoma cancer is 72%. This study analyzed data from the National Cancer Database (NCDB) to understand how demographic factors contribute to the treatment and prognosis of myofibroblastic sarcoma. Methods: A retrospective cohort analysis was conducted on demographic factors of patients with confirmed myofibroblastic sarcoma (ICD-8825-3) using the NCDB from 2004-2020. Regression analysis was used to study trends of patient age, sex, race, education, insurance status, distance from facility, and Charles/Deyo score within this population. Results: A total of 111 patients (N = 111) were identified in the database with a confirmed diagnosis of myofibroblastic sarcoma from 2004 to 2020 with an increasing frequency of diagnoses per year (R^2 = 0.6283). The average age of diagnosis was 47.3 (SD = 24.17, range = 0 - 90 years). Although Myofibroblastic sarcoma appeared throughout the body, the most prominent sites were the long bones of the lower limb and associative joints (6%) followed by the uterus (5%). The majority of patients were white (83%), non-Hispanic (86%), and female (63%). A higher percentage of patients lived in metropolitan areas with a population greater than 1 million (58%) compared to smaller metropolitan, urban, or rural counties (42%). Most of the patients were treated at an academic/research program (30%) followed by a comprehensive community cancer program (21%). More facility locations were based in the Pacific (15%) and the Middle Atlantic (11%) regions. The primary payer at diagnosis was private insurance/managed care (44%) followed by Medicare (32%) and Medicaid (19%). Few patients (5%) received palliative care. Conclusions: To the best of our knowledge, this is the first NCDB analysis on Myofibroblastic sarcoma. Based on our dataset, Myofibroblastic sarcoma tends to occur in the long bones of the lower limb and associative joints which differed slightly from what we found in previous reports, which described the head and neck as the two areas of highest prevalence. This is the first time the socioeconomic factors have been described in the literature: these patients are more likely to be non-Hispanic, female individuals, living in smaller towns. This study offers insight on how much further investigation is required to better understand the impact of demographic and socioeconomic factors on early diagnosis and treatments.

Solving Facility Location Problems via FastMap and Locality Sensitive Hashing

Solving Facility Location Problems via FastMap and Locality Sensitive Hashing

Analysis of the impact of urban planning facility locations on multifamily attached housing prices*

Analysis of the impact of urban planning facility locations on multifamily attached housing prices*

The maximal covering location disruption problem

This research sets forth and examines a new sequential, competitive location problem. The maximal covering location disruption problem is a zero-sum Stackelberg game comprised of two stages. A leader denies access to at most q out of n possible facility locations in the first stage and, in the second stage, a follower solves a maximal covering location problem while emplacing at most p facilities. Identifying this problem as both relevant and unaddressed in the current literature, this research examines properties of the bilevel programming formulation to inform heuristic development, subsequently evaluating the efficacy and efficiency of two variants each of an iterative, bounding heuristic (IBH) and a reformulation-based construction heuristic (RCH) over a two sets collectively consisting of 2160 test instances representing a breadth of relative parametric values. Although we illustrate that each heuristic may not identify an optimal solution, computational testing demonstrates the superlative and generally excellent performance of the RCH variants. For the 12.4% of instances for which the RCH does not readily verify the optimality of its solution, lower-bounding procedures characterize solution quality. Both of the RCH variants attain solutions with an average 4.08% relative optimality gap, and they scaled well over different parametric value combinations, solving instances in an average of 98.0 and 123.6 seconds, respectively.

일제강점기 항공시설의 입지 및 비행기 격납고(엄체)의 특성 ― 전남 무안･경남 사천･경북 영천을 중심으로 ―

일제강점기 항공시설의 입지 및 비행기 격납고(엄체)의 특성 ― 전남 무안･경남 사천･경북 영천을 중심으로 ―

Peta Digital Kependudukan Berbasis QGIS: Inovasi Teknologi untuk Visualisasi Data kependudukan dan Populasi di Dusun Mrisi, Kabupaten Bantul, Daerah Istimewa Yogyakarta

Digital mapping has become a valuable tool for managing and visualizing geographical information. In this context, this community service aims to create a digital map using WebGIS in Mrisi Hamlet. Mrisi requires accurate and efficient mapping of population data to optimize data collection methods. This is to make decisions related to regional development, and map resources such as UMKM. This activity method involves collecting spatial and attribute data through field surveys, remote sensing, and relevant secondary data sources. The data obtained is then processed and compiled into a web-based geographical information system. In developing digital maps, WebGIS was chosen because it provides easy accessibility, interactive data visualization, and the ability to share geographical information with other users. This activity results in a digital map that includes various relevant information about Mrisi Hamlet. The map includes area boundaries, infrastructure, land use, public facilities location, and other related information. These digital maps are internet-based access and use decision-making by local governments, communities, and other related parties.