Average Cost Reduction Research Articles

Tourism Recommendation Algorithm Based on the Mobile Intelligent Connected Vehicle Service Platform

As to the problems in current tourism recommendation, this paper proposes a tourism recommendation algorithm based on the mobile ICV service platform. Firstly, the ICV service system for the Point of Interest (POI) searching and route recommendation is designed. Secondly, the recommendation service model is set up from two aspects, namely the tourism POI clustering algorithm and the tourism POI searching and route recommendation algorithm. In the aspect of symmetrical-based matching features, the clustered POIs are matched with the tourists’ interests, and the POIs in the neighborhood of the ICV dynamic locations are searched. Then, a POI recommendation algorithm based on the tourists’ interests is constructed, and the POIs that best match the symmetrical interests of the tourists within the dynamic buffer zones of ICV are confirmed. Based on the recommended POIs, the ICV guidance route algorithm is constructed. The experiment verifies the advantages of the proposed algorithm on the aspect of the POI matching tourists’ interests, algorithm stability, traveling time cost, traveling distance cost and computational complexity. As to the iterative sum and the iterative sum average of the POI matching function values, the proposed algorithm has a performance improvement of at least 20.2% and a stability improvement of at least 20.5% compared to the randomly selected POIs in matching tourists’ interests. As to the cost of the guidance routes, the proposed algorithm reduces the average cost by 19.6% compared to the other suboptimal routes. Compared with the control group algorithms, the proposed algorithm is superior in terms of route cost, with an average cost reduction of 13.8% for the output routes compared to the control group. Also, the proposed algorithm is superior in terms of route cost compared to the control group recommendation algorithms, with an average cost reduction of 11.2%.

Serverless Computing in the Edge-Cloud Continuum : Challenges, Opportunities, and a Novel Framework

This article explores the integration of serverless computing across the edge-cloud continuum, addressing the growing demand for low-latency, data-intensive applications in the era of the Internet of Things (IoT). We present EdgeServe, a novel framework that extends the serverless paradigm to encompass edge devices and cloud resources, overcoming the limitations of traditional cloud-centric approaches. Through comprehensive simulations and real-world case studies, including a smart city traffic management system, an industrial IoT predictive maintenance application, and a mobile augmented reality gaming platform, we evaluate the performance, scalability, and cost-effectiveness of our proposed framework. Our results demonstrate significant improvements in application response times, with latency reductions of up to 82% in time-critical functions, and an average cost reduction of 43% compared to cloud-only serverless deployments. We also observe a 28% decrease in overall energy consumption in certain scenarios. The article addresses key challenges in resource management, data consistency, adaptive function placement, and security in distributed environments. Our findings have important implications for application architects and cloud service providers, paving the way for a new generation of edge-native serverless platforms. This research contributes to the growing body of knowledge on distributed systems and offers insights into the future evolution of serverless computing in heterogeneous computing environments.

Optimizing substitution of two products model for exponentially increasing demand under inflation and shortages

This study addresses the limited attention given to product substitution in inventory models. Incorporating product substitution is crucial for determining reorder points, and safety stock, enabling businesses to optimize inventory levels, reduce costs, and maintain customer satisfaction. This study introduces an economic order quantity model tailored to an inflationary environment with shortages and one-way substitution between two deteriorating product types. Through comprehensive testing, this study evaluates the model under various substitution scenarios, including partial substitution. Findings highlight the significance of product substitution in inventory management, allowing businesses to optimize inventory levels, manage costs, and ensure customer satisfaction in dynamic environments with inflation and fluctuating product availability. This model provides the firm with the necessary information to determine the optimal ordering quantity of both products to optimize total benefit and enhance supply chain efficiency. The model demonstrates substantial cost advantages, with partial substitution resulting in an average cost reduction of approximately 9% compared to no substitution and about 45% compared to full substitution. Numerical experiments validate the applicability of the proposed model.

A location-production-routing problem for distributed manufacturing platforms: A neural genetic algorithm solution methodology

Additive Manufacturing (AM) enhances the flexibility of manufacturing networks. In this paper, we present a Location-Production-Routing (LPR) problem designed for a distributed manufacturing platform, where the manufacturing facilities are distributed in different locations with the support of AM technologies. The proposed LPR problem encompasses three different types of decisions: location-allocation, production planning, and product delivery routing decisions. This is one of the first studies that analyzes integrated logistics and manufacturing optimization under distributed and resilient manufacturing platforms. To efficiently solve the complex problem, we design a novel solution method called the Neural Genetic Algorithm (NGA). The numerical experiments show that the proposed method can attain near-optimal solutions, achieving an average gap of 3% with a standard deviation of 1.4% and a 99% improvement in computational time compared to the CPLEX solver. The sensitivity analysis illustrates the high impact of the unit shortage cost on the customer service level and on the distribution of the AM facilities. Moreover, our results for a given instance show that through the periodic reconfiguration of AM supply chains using the proposed LPR model, we can achieve an average cost reduction of up to 25% in the supply network.

Predictive energy control for grid-connected industrial PV-battery systems using GEP-ANFIS

Rising energy costs in Uganda's industrial sector have significantly increased manufacturing expenses and led to the closure of potential enterprises, underscoring the urgent need for efficient energy management solutions. The study presents an optimization-based predictive energy control model that integrates Gene Expression Programming (GEP) and Adaptive Network-based Fuzzy Inference Systems (ANFIS) for grid-connected solar PV-battery systems. The hybrid GEP-ANFIS model leverages GEP's strength in complex pattern recognition and ANFIS's adaptive learning capability to manage nonlinear data, resulting in more accurate and reliable energy predictions. The model utilizes historical load patterns, grid data, and solar PV/battery inputs to forecast load demand and solar power generation, optimizing energy usage based on Time of Use (TOU) pricing. The approach addresses the limitations of existing energy management techniques by enhancing predictive accuracy and ensuring seamless integration with industrial applications. The present work demonstrates that the proposed GEP-ANFIS double diode model consistently outperforms its counterparts, achieving a mean absolute percentage error (MAPE) of 7.25 %, a mean absolute deviation (MAD) of 2.95 %, and a root mean square error (RMSE) of 8.42 %. Moreover, the model exhibits superior accuracy in predicting short-term load demand, with a MAPE of 2.24 %, a MAD of 0.13 %, and an RMSE of 0.18 %. Additionally, the model results in an average energy cost reduction of 6.7 % for hybrid grid-connected solar PV/battery configurations, highlighting its economic benefits. The study's key contributions include the creation of a novel hybrid predictive model, significant cost savings, improved load prediction accuracy, and the integration of renewable energy sources into industrial energy management. The model's inferences can effectively address the challenges of rising energy costs, providing a robust framework for sustainable and efficient energy management in industrial settings.

Deep neural network accelerated-group African vulture optimization algorithm for unit commitment considering uncertain wind power

The unit commitment (UC) of power systems serves to plan out the starting and shutdown of units and the power generation of units for a future period of time. However, the UC problem for large-scale systems faces the problems of long solution time and insufficiently accurate solution. Uncertainty in wind power generation poses a great challenge in solving the unit combination problem. This work proposes a deep neural network accelerated-double layer optimization method (DNNA-DOM) to improve the solution efficiency, accuracy, and economy of UC. The outer layer of DNNA-DOM utilizes the proposed deep neural network accelerated-group African vulture optimization algorithm (DNNA-GAVOA) to optimize the on-off condition of conventional coal-fired power units. The inner layer of DNNA-DOM adopts quadratic programming to optimize the solution of the economic dispatch problem of load distribution. The GAVOA optimizes the simulated African vultures in four groups to obtain the optimal solution after diverse exploration, exploitation, and exploitation activities, with low complexity and high accuracy. This work first evaluates the performance of GAVOA by solving seven unimodal functions. Furthermore, the 10-unit simulation by incorporating wind power curves and related constraints is optimized through the DNNA-DOM. The results show that GAVOA outperforms the African vulture optimization algorithm (AVOA) and traditional metaheuristics like the particle swarm algorithm and gray wolf algorithm in terms of lower operating cost and optimization stability. The DNNA-DOM combined with DNNA-GAVOA in the outer layer results in an average daily cost reduction of $36.30 and a 45.1997 % speed increase compared to AVOA in the outer layer.

Net Zero Energy Building technologies – Reversible Heat Pump/Organic Rankine Cycle coupled with Solar Collectors and combined Heat Pump/Photovoltaics – Case study of a Chilean mid-rise residential building

In the following years, the building sector will have to considerably reduce its final energy demand and emissions by adopting new technologies.This paper introduces two innovative technologies capable of reducing the final energy demand of residential buildings: A Heat Pump/Organic Rankine Cycle system coupled to solar thermal collectors (HP/ORC-COL), and a Heat Pump coupled with Photovoltaic panels (HP-PV). The HP/ORC-COL system alternates between generating either heat or electricity, whereas the HP-PV system produces them separately. The goal is to compare both systems through their Seasonal Performance Factor (SPF), equivalent emissions, operational cost, and Net Zero Energy Building (NZEB) potential. Additionally, the impact on the heating demand of retrofitting existing buildings with the Passivhaus Standard is studied.The heating, domestic hot water, and electricity demands are determined by considering the climate conditions of three polluted Chilean cities: Santiago, Concepción, and Temuco. The proposed systems are evaluated through numerical models developed in Python and validated with data from manufacturer catalogues. The validation of the models allows to enhance the result accuracy of the integrated overall model by closely aligning outputs with real conditions, and therefore increases the reliability of the prediction on the potential of the building to reach net zero energy.Results show that applying the Passivhaus standard allows to reduce heating requirements by up to 30% in all cities. The SPF and electricity production of the HP-PV system is better than the HP/ORC-COL, reaching a NZEB potential of 76.3% in Santiago, 66.6% in Concepción and 60.4% in Temuco. The average cost reduction of the HP/ORC-COL system compared to oil, natural gas, and wood pellet boilers is 82.7%, 90.4%, and 72.3%, whereas for the HP-PV system is 93.3%, 96.3%, and 89.2% respectively. On the CO2 emissions, the HP/ORC-COL system presents an average reduction of 63.6% and 59.7% compared to an oil and natural gas boiler, while the HP-PV system has an average reduction of 72.5% and 69.5% respectively.

Deployment of Lean Manufacturing in Palm Oil Mill for Maximum Yield: A Case Study of Leading Palm Oil Producer in Nigeria

The deployment of lean manufacturing in the palm oil mill companyaimed at maximizing yield, was thoroughly investigated. The company has been experiencing a decline in palm oil production, adversely affecting productivity and customer satisfaction. The methodology employed for optimization encompassed a comprehensive system description, detailing the case study company's history, operational modalities, production machinery, and system challenges. To gather a holistic understanding of the company's current situation, data was meticulously collected through questionnaires, examination of company records, and other relevant sources. Microsoft excel software was then utilized to analyze the acquired data. The implementation of lean manufacturing tools, such as the fishbone diagram, identification of eight deadly wastesand takt-timeplayed a pivotal role in streamlining operations. In addition, a quantitative model of the supply chain was applied, utilizing critical path method (network analysis) to determine the optimal route. The results derived from the application of lean manufacturing tools and the supply chain quantitative model exhibited a remarkable improvement between (2012-2016) and (2018-2022). The average oil palm extraction increased substantially from 8.67% (2012-2016) to an impressive 30.52% (2018-2022). Furthermore, the management tools led to a significant reduction in machine downtimes, decreasing from an annual average of 5,498.6 hours to 2,472.4 hours. This improvement resulted in a considerable reduction in the annual total average cost of machine maintenance, plummeting from #1,899,578.6 to #261,853.8. The optimization efforts also extended to the supply chain, where selecting Path-2, exhibiting the least total supply time of 11 hours, significantly reduced the cost of supply from an average of #2,168,402 to #1,893,186. Overall, the net revenue of the company witnessed a remarkable surge of 71.99% post-implementation of these management tools. This comprehensive approach not only addressed production challenges but also had a substantial positive impact on the company’s financial performance. The strategic deployment of lean manufacturing emerged as a catalyst for sustainable growth and enhanced operational efficiency.

The economic production quantity model for an imperfect production process by control chart design

The Economic Production Quantity (EPQ) plays a crucial role in the manufacturing process. The traditional EPQ model operates under the assumption that Non-Conforming (NC) items are not produced, and the process remains under control. However, real-world scenarios often involve the production of NC products, leading to process deviations. This study introduces a model that optimizes the design of a control chart to incorporate NC products into the EPQ model. The model integrates inventory and quality-related costs to minimize the expected total cost by determining EPQ, sample size, and control limit. The solution methodology employs direct search techniques to solve this problem. The sensitivity analysis results demonstrate the significant cost reduction achieved by the proposed model compared to the classical EPQ model. The numerical examples presented in the study show an average cost reduction of approximately 21 percent, highlighting the potential effectiveness of the model in cost reduction efforts.

Economic evaluation of decarbonizing the electricity sector in the Dominican Republic

This paper presents an in-depth analysis of decarbonizing the electricity sector in the Dominican Republic, pivotal for addressing climate change and fostering economic growth. Employing the robust-decision making methodology, we studied multiple scenarios via computational models, capturing inputs from stakeholders and evaluating each scenario across 1,000 futures to capture deep uncertainty. Four scenarios were examined: baseline, reference, natural gas, and renewable. The renewable scenario emerged as the most advantageous, proposing the replacement of coal-fired power generation with renewable sources, primarily solar and wind, coupled with batteries. A significant investment, averaging US$3.3 billion, is necessary for this shift toward renewable energy; however, these investments are overcompensated by savings in operational costs. Crucially, this transition promises substantial benefits by 2050: an estimated cumulative average net economic gain of US$2.7 billion, an 8% reduction in average generation costs in 2050, the creation of 160,000 direct jobs, and the avoidance of circa 140 million tons of CO2. The findings underscore the feasibility and economic viability of transitioning to a 55% renewable energy generation by 2050. The study offers a critical roadmap for policymakers, highlighting renewable energy expansion, transmission grid strengthening, and strategic coal generation replacement, thus offering a comprehensive blueprint for the nation's energy transition.

The Effect of the Temperature Increase on Heat Demand Calculation for Building in the Polish Climatology

Heat demand calculation procedures for buildings are currently being conducted based on the Polish Standard grounded on the European Standard for heat losses methodology. Poland was divided into several climatic zones with given calculations and annual average external air temperature values. In each zone, the technical committee gives the temperature that must be considered. The data included in the Polish Standard preparation concern several years backwards. However, throughout the previous years, we have observed the tendency of the temperature to increase throughout the year, not only in Poland. The phenomenon provides scientists with the conclusion that the temperatures will continue to increase yearly. The low temperatures in the winter season will no longer occur. Therefore, it is necessary to analyse the possibility of lowering the heat demand calculation, including the actual prevailing temperature values. The study was conducted regarding possible economic benefits for the building users due to the lower temperature difference between the internal and external temperatures. Three types of heating systems were analysed. The calculations were conducted for air to water heat pump, pellet boiler and gas condensing boiler, proving that regarding the actual meteorological data, the average annual cost reduction will be in the range of 858 PLN to 1,035 PLN.

Performance and economics of fall grazing cover crops compared to a traditional drylot system of fall-calving cow-calf pairs

The objective of this study was to evaluate cow and calf performance while strip grazing a cover crop mix consisting of radishes, turnips, oats, and common vetch (CC) compared to a traditional, drylot management system (CON) of the Iowa State University Beef Teaching Farm fall-calving herd. The study was repeated for 3 consecutive years to account for weather variation.  Each growing season presented a unique situation. With a targeted seeding date of August 1st each year, yields ranged from 2,750 – 4,830 lbs of dry matter per acre, averaging 3,952 lbs. The greatest yield was observed in 2019 while the lowest yield occurred in 2020 with drought conditions restricting growth. Growth in 2021 was intermediate, but peak yield occurred after grazing began due to a delayed killing frost. Turnout dates ranged from early-November to mid-December, and fall pairs grazed for an average of 32 days, ranging from 17 – 45 days.  Despite fluctuation in cow body weights between treatments over the years, calves on CC had equal to or greater average daily gain and final body weights compared to calves on CON. Over the 3 years, CC calves gained an average of 0.36 lbs more per head per day than their CON counterparts. This advantage may be a reflection of CC dams producing a higher quality milk due to the high-quality forage diet and the ability of calves to graze alongside their dam.  An economic analysis of the 3-year project revealed a 29% average reduction in calf cost of gain of cow-calf pairs grazing cover crops ($1.11/lbs of gain) compared to cow-calf pairs fed in a drylot setting ($1.57/lb of gain). Compared to the standardized drylot in this scenario, this efficiency boost emphasizes the value of extending the grazing season, especially in times of high feed costs.

A regret-based robust optimization model for municipal water distribution network redesign under disruption risks

In recent decades, the increasing demand for water and resource limitations have posed challenges to water resources management. This study proposes a comprehensive model for redesigning urban water distribution network under disruptions, focusing on household consumption. The proposed model integrates strategic decisions for network, and addresses disruption risks. To ensure the resilience of the network against disruptions caused by earthquakes, we developed a regret-based robust optimization model. The model minimizes redesign and operational costs by optimizing water flow, construction locations, and pipeline connections. Numerical results demonstrate the efficacy of the proposed model, revealing a reduction in average costs—approximately 0.6 %—compared to the deterministic model. This reduction is noteworthy given the scale of municipal water distribution configuration costs. Also, resiliency analysis demonstrates the superiority of the robust solution compared to the deterministic structure. The model supports policymakers for operational and long-term decisions, considering complex hydraulic constraints and horizontal transfers between reservoirs.

Cost-Effectiveness of Extracorporeal Photopheresis in Patients With Chronic Graft-vs-Host Disease

Background: The mainstay first-line therapy for chronic graft-vs-host disease (cGVHD) is corticosteroids; however, for steroid-refractory patients, there is a distinct lack of cost-effective or efficacious treatment. The aim of this study was to assess the cost-effectiveness of extracorporeal photopheresis (ECP) compared with standard-of-care therapies for the treatment of cGVHD in Australia. The study formed part of an application to the Australian Government to reimburse ECP for these patients. Methods: A cost-utility analysis was conducted comparing ECP to standard of care, which modeled the response to treatment and disease progression of cGVHD patients in Australia. Mycophenolate, tacrolimus, and cyclosporin comprised second-line standard of care based on a survey of Australian clinicians. Health states in the model included treatment response, disease progression, and death. Transition probabilities were obtained from Australian-specific registry data and randomized controlled evidence. Quality-of-life values were applied based on treatment response. The analysis considered costs of second-line treatment and disease management including immunosuppressants, hospitalizations and subsequent therapy. Disease-specific mortality was calculated for treatment response and progression. Results: Over a 10-year time horizon, ECP resulted in an average cost reduction of $23 999 and an incremental improvement of 1.10 quality-adjusted life-years per patient compared with standard of care. The sensitivity analysis demonstrated robustness over a range of plausible scenarios. Conclusion: This analysis demonstrates that ECP improves quality of life, minimizes the harms associated with immunosuppressant therapy, and is a highly cost-effective option for steroid-refractory cGVHD patients in Australia. Based in part on this analysis, ECP was listed on the Medicare Benefits Schedule for public reimbursement.

Cost-Effectiveness of Extracorporeal Photopheresis in Patients With Chronic Graft-vs-Host Disease.

Background: The mainstay first-line therapy for chronic graft-vs-host disease (cGVHD) is corticosteroids; however, for steroid-refractory patients, there is a distinct lack of cost-effective or efficacious treatment. The aim of this study was to assess the cost-effectiveness of extracorporeal photopheresis (ECP) compared with standard-of-care therapies for the treatment of cGVHD in Australia. The study formed part of an application to the Australian Government to reimburse ECP for these patients. Methods: A cost-utility analysis was conducted comparing ECP to standard of care, which modeled the response to treatment and disease progression of cGVHD patients in Australia. Mycophenolate, tacrolimus, and cyclosporin comprised second-line standard of care based on a survey of Australian clinicians. Health states in the model included treatment response, disease progression, and death. Transition probabilities were obtained from Australian-specific registry data and randomized controlled evidence. Quality-of-life values were applied based on treatment response. The analysis considered costs of second-line treatment and disease management including immunosuppressants, hospitalizations and subsequent therapy. Disease-specific mortality was calculated for treatment response and progression. Results: Over a 10-year time horizon, ECP resulted in an average cost reduction of $23 999 and an incremental improvement of 1.10 quality-adjusted life-years per patient compared with standard of care. The sensitivity analysis demonstrated robustness over a range of plausible scenarios. Conclusion: This analysis demonstrates that ECP improves quality of life, minimizes the harms associated with immunosuppressant therapy, and is a highly cost-effective option for steroid-refractory cGVHD patients in Australia. Based in part on this analysis, ECP was listed on the Medicare Benefits Schedule for public reimbursement.

Berth and quay cranes allocation problem with on-shore power supply assignment in container terminals

On-shore power supply (OPS) is a promising carbon emission-reduction technology for vessels and ports. Optimizing its allocation for berthing vessels is a new operational challenge due to different deployments on shore and on board. This article proposes an innovative solution to enhance the effectiveness of berths and on-shore power utilization in container terminals. The proposed solution integrates continuous berth allocation, quay crane assignment, and OPS allocation to address optimization challenges. First, a mixed-integer programming model is formulated based on the in-depth analysis of the OPS service coverage and carbon emissions of vessels, aiming to increase service efficiency, reduce energy consumption, and minimize carbon taxation. Then, an adaptive immune clone selection algorithm is developed to solve this problem. Adaptive and elite strategies are adopted to enrich population diversity. To improve convergence speed, a heuristic algorithm is designed based on the established model. Numerical experiments based on practical instances confirm the model's validity and the proposed algorithm's superiority over benchmark methods. The proposed integrated optimization strategy yields an average cost reduction of 2.14% and reduces carbon emissions by 2.90% by combining carbon taxation and OPS implementation. The study also highlights that OPS utilization is determined by fuel price and vessel retrofit ratio. Managerial insights suggest that raising the OPS service coverage to 50% and the vessel retrofit ratio to 70% would greatly benefit the port both economically and environmentally. To increase OPS utilization, coordination between port and shipping companies is necessary.

Simulation-based variable neighborhood search for optimizing skill assignments in multi-server facilities with inventories

This paper addresses the joint optimization problem of skill assignments and inventory in a multi-skill, multi-server repair facility. Failures of different part types occur according to Poisson processes, and each part type requires a certain repair skill. The repair facility supplies ready-to-install spare parts when available in the inventory, according to the (S−1,S) inventory policy. The repair times follow exponential distributions, with rates dependent on the part type. After repair, the parts are returned to the inventory as ready-to-install spare parts. If the inventory is empty when a failed part arrives, the replacement part is backordered, and a penalty cost is incurred. The objective of the problem is to find an assignment of repair skills to servers and inventory levels that minimize the expected total cost of the system. That is, the costs for servers, the costs to upgrade the skills of servers, and the expected holding and backorder costs. We propose to solve this problem by a simulation-based Variable Neighborhood Search (VNS) approach, in which a Discrete Event Simulation is applied to evaluate the expected backorder and holding costs given the skill assignments.The proposed method is capable of significantly improving the results of a recently published Genetic Algorithm, achieving an average cost reduction of 5.1% in the same running time. Moreover, it is able to find comparable solutions in one fifth of the GA running time.

Modified Social Group Optimization to Solve the Problem of Economic Emission Dispatch with the Incorporation of Wind Power

Economic dispatch, emission dispatch, or their combination (EcD, EmD, EED) are essential issues in power systems optimization that focus on optimizing the efficient and sustainable use of energy resources to meet power demand. A new algorithm is proposed in this article to solve the dispatch problems with/without considering wind units. It is based on the Social Group Optimization (SGO) algorithm, but some features related to the selection and update of heuristics used to generate new solutions are changed. By applying the highly disruptive polynomial operator (HDP) and by generating sequences of random and chaotic numbers, the perturbation of the vectors composing the heuristics is achieved in our Modified Social Group Optimization (MSGO). Its effectiveness was investigated in 10-unit and 40-unit power systems, considering valve-point effects, transmission line losses, and inclusion of wind-based sources, implemented in four case studies. The results obtained for the 10-unit system indicate a very good MSGO performance, in terms of cost and emissions. The average cost reduction of MSGO compared to SGO is 368.1 $/h, 416.7 $/h, and 525.0 $/h for the 40-unit systems. The inclusion of wind units leads to 10% reduction in cost and 45% in emissions. Our modifications to MSGO lead to better convergence and higher-quality solutions than SGO or other competing algorithms.

A multi-factor intelligent biologic search algorithm for closed-area fiber optic network planning

Fiber optic network planning is a critical aspect in numerous intelligent park construction projects. This study addresses the issue of fiber routing allocation within these enclosed parks, which necessitate substantial real-time communication during their operation. To optimize route allocation efficiency and account for line costs, we propose a Multi-factor Intelligent Biologic Search Algorithm (MIBSA). This algorithm operates within a hierarchical framework and progresses through three stages of enhancement. The foundational model utilizes an enhanced Lifelong Planning A* (LPA*) algorithm to delineate the initial path and non-uniformly allocate the primary pheromone of the ant colony algorithm. The upper tier integrates an improved Ant Colony Optimization (ACO) model, incorporating various factors into the pheromone update strategy for multi-objective fiber optic network planning. Simultaneously, this algorithm introduces a batch pheromone boosting mechanism and a momentum method to expedite the convergence process. Experimental findings highlight that MIBSA surpasses the conventional ACO method in solving fiber optic network planning problems, yielding an average comprehensive cost reduction of 16.42% and 7.82% under the environmental map of the objective problem and the general environmental map test, respectively.

Outcomes of an integrated practice unit for vulnerable emergency department patients

BackgroundAn integrated practice unit (IPU) that provides a multidisciplinary approach to patient care, typically involving a primary care provider, registered nurse, social worker, and pharmacist has been shown to reduce healthcare utilization among high-cost super-utilizer (SU) patients or multi-visit patients (MVP). However, less is known about differences in the impact of these interventions on insured vs. uninsured SU patients and super high frequency SUs (\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ge$$\\end{document}8 ED visits per 6 months) vs. high frequency SUs (4–7 ED visits per 6 months).MethodsWe assessed the percent reduction in ED visits, ED cost, hospitalizations, hospital days, and hospitalization costs following implementation of an IPU for SUs located in an academic tertiary care facility. We compared outcomes for publicly insured with uninsured patients, and super high frequency SUs with high frequency SUs 6 months before vs. 6 months after enrollment in the IPU.ResultsThere was an overall 25% reduction in hospitalizations (p < 0.001), and 23% reduction in hospital days (p = 0.0045), when comparing 6 months before vs. 6 months after enrollment in the program. There was a 26% reduction in average total direct hospitalization costs per patient (p = 0.002). Further analysis revealed a greater reduction in health care utilization for uninsured SU patients compared with publicly insured patients. The program reduced hospitalizations for super high frequency SUs. However, there was no statistically significant impact on overall health care utilization of super high frequency SUs when compared with high frequency SUs.ConclusionsOur study supports existing evidence that dedicated IPUs for SUs can achieve significant reductions in acute care utilization, particularly for uninsured and high frequency SU patients.Trial RegistrationIRB201500212. Retrospectively registered.