Decrease In Total Cost Research Articles

A MINP model and hybrid heuristic algorithm for railway cold chain service network design problem: a case study of China

Railway Cold Chain Service Network Design (RCC-SND) aims to optimise resource allocation and utilisation to ensure the efficient transportation of perishable foods. In this study, we propose a Mixed Integer Nonlinear Programming (MINP) model to solve the hub selection, service frequency determination, wagon flow organisation and routing problems in RCC-SND. The application of the model is illustrated using the real network comprising 163 cities in China. The PSO-GA algorithm is proven effective in solving the problem. Furthermore, we introduce two future scenarios to assess the carbon reduction potential and economic costs of railway cold chain operations. There are some main findings: as the hub number increases from 30 to 40, the total cost decreases, as the hub continues to rise, this reduction is offset by the increased hub operational costs. Increasing hubs can enhance direct train frequency while causing railway capacity redundancy, this redundancy can be addressed by freight volume increase, as demonstrated in this study, 4.5 times increase in freight volume can boost train utilisation by up to 12.5%. By 2030, railway cold chain carbon emissions are projected to exceed 1.1 million tons, the adoption of hydrogen as alternative energy is an economically viable solution for emission reduction.

Impacts of DRG-Based Prepayment Reform on the Cost and Quality of Patients with Neurologic Disorders: Evidence from a Quasi-Experimental Analysis in Beijing, China.

As one of the pioneering pilot cities in China's extensive Diagnosis Related Groups (DRG) -based prepayment reform, Beijing is leading a comprehensive overhaul of the prepayment system, encompassing hospitals of varying affiliations and tiers. This systematic transformation is rooted in extensive patient group data, with the commencement of actual payments on March 15, 2022. This study aims to evaluate the effectiveness of DRG payment reform by examining how it affects the cost, volume, and utilization of care for patients with neurological disorders. Utilizing the exogenous shock resulting from the implementation of the DRG-based prepayment system, we adopted the Difference-in-Differences (DID) approach to discern changes in outcome variables among DRG payment cases, in comparison to control cases, both before and following the enactment of the DRG policy. The analytical dataset was derived from patients diagnosed with neurological disorders across all hospitals in Beijing that underwent the DRG-based prepayment reform. Strict data inclusion and exclusion criteria, including reasonableness tests, were applied, defining the pre-reform timeframe as March 15th through October 31st, 2021, and the post-reform timeframe as the corresponding period in 2022. The extensive dataset encompassed 53 hospitals and encompassed hundreds of thousands of cases. The implementation of DRG-based prepayment resulted in a substantial 12.6% decrease in total costs per case and a reduction of 0.96 days in length of stay. Additionally, the reform was correlated with significant reductions in overall in-hospital mortality and readmission rates. Surprisingly, the study unearthed unintended consequences, including a significant reduction in the proportion of inpatient cases classified as surgical patients and the Case Mix Index (CMI), indicating potential strategic adjustments by providers in response to the introduction of DRG payments. The DRG payment reform demonstrates substantial effects in restraining cost escalation and enhancing quality. Nevertheless, caution must be exercised to mitigate potential issues such as patient selection bias and upcoding.

Joint routing and computation offloading based deep reinforcement learning for Flying Ad hoc Networks

Flying ad-hoc networks (FANETs) consisting of multiple Unmanned Aerial Vehicles (UAVs) are widely used due to their flexibility and low cost. In scenarios such as crowdsensing and data collection, data collected by UAVs are transmitted to base stations for processing and then sent to data centers. Still, the deployment of base stations is costly and inflexible. To address this issue, this paper introduces a position-based Computing First Routing (CFR) protocol designed for efficient task transmission and computation offloading in FANETs. This protocol facilitates task processing during data transfer and ensures the delivery of fully processed results to the data center. Considering the dynamically changing topology of the FANETs and the uneven distribution of the UAVs’ computation power, deep reinforcement learning is used to make multi-objective decisions based on the Q-values computed by the model. FANETs are centerless clusters, and two-hop neighbor tables containing position and computing power information are used to make less costly decisions. Simulation experiments demonstrate that CFR outperforms other benchmark schemes with an approximately 6% higher packet delivery rate, an approximately 21% reduction in end-to-end delay, and about a 34% decrease in total cost. Furthermore, it effectively ensures the completion of task offloading before reaching the destination node. This occurs due to the design of a hierarchical reward function that takes into account dynamic changes in delay and energy consumption, as well as the injection of neighbor computing power information into the two-hop neighbor table.

Electric vehicle supply equipment location and capacity allocation for fixed-route networks

Electric vehicle (EV) supply equipment location and allocation (EVSELCA) problems for freight vehicles are becoming more important because of the trending electrification shift. Some previous works address EV charger location and vehicle routing problems simultaneously by generating vehicle routes from scratch. Although such routes can be efficient, introducing new routes may violate practical constraints, such as drive schedules, and satisfying electrification requirements can require dramatically altering existing routes. To address the challenges in the prevailing adoption scheme, we approach the problem from a fixed-route perspective. We develop a mixed-integer linear program, a clustering approach, and a metaheuristic solution method using a genetic algorithm (GA) to solve the EVSELCA problem. The clustering approach simplifies the problem by grouping customers into clusters, while the GA generates solutions that are shown to be nearly optimal for small problem cases. A case study examines how charger costs, energy costs, the value of time (VOT), and battery capacity impact the cost of the EVSELCA. Charger equipment costs were found to be the most significant component in the objective function, leading to a substantial reduction in cost when decreased. VOT costs exhibited a significant decrease with rising energy costs. An increase in VOT resulted in a notable rise in the number of fast chargers. Longer EV ranges decrease total costs up to a certain point, beyond which the decrease in total costs is negligible.

تأثير زاوية الشفرة والسرعة الأمامية على أداء جزازة العشب MURRAY-15.5hp

A field experiment was conducted to design, implement, and test the effect of blade angle represented by cutting angle and foreword speed on the performance of a MURRAY-15.5hp lawn mower machine. Three types of blades with cutting angles of 20o, 25o, and a straight angle were used, as well as three forward speeds of 2.99, 4.32, and 5.60 km h-1. The slippage percentage, field productivity, material quantity and total costs were studied. The results indicated the following: decreasing the cutting angle from 25o to 20o and then to a straight angle considerably decreases the slippage percentage and a significant decrease in total costs .Also, a significant increase in the field and Material productivity. Increasing the foreword speed resulted in a significant increase in the slippage percentage and a significant increase in in the field and material productivity, and decrease in the total costs. The interaction between the straight blade type and the forward speed 2.99 km h-1 led to the lowest slippage at 8.34%, while the interaction between the straight blade type and forward speed 5.60 km h-1 resulted in the lowest total costs amounted to 12036 dinars*hectare-1 and the highest field productivity 0.360 ha h-1 while the highest material productivity was 121.25 kg h-1.

Tow-sectional optimized thermodinamical cycle using different renewable energies including geothermal and biogas to produce stable productions

Finding an appropriate solution to find stable and continuous green energy is one of the significant problems of scientists in the field of renewable energy. In this study, a mixed type of system implementing two geothermal gas turbine energy combined using an innovative method, the suggested system continuously merits productivity of different production. Also, it implements renewable energies to provide the required power to generate conducts which is considered important in the green transient of governments. In addition, thermodynamic rules were used to analyze the suggested system. The results obtained from analysis and equations in the EES software show that power production and thermal load and cooling equal 1432.3kw, 937.9kw and 128.8kw, respectively. the total energy and exergy efficiency is calculated at 58.31 % and 42.17 %. The system's return on investment is calculated in twenty years, with the electricity price of 0.15$/GJ, 6.02 years and with the fuel price of Cfuel = 21$/GJ, 7.15 years, and the system reaches profitability. to improve system function, the multiple optimized genetic algorithm is used. The objective function in optimizing the system is exergy efficiency and total cost, which is considered optimization MATLAB software, which means a decrease of total cost and an increase of exergy efficiency.

Synchronizing delivery and installation with vehicle sharing: A hybrid adaptive large neighborhood search

This paper presents the real-life synchronized delivery and installation with vehicle sharing problem (SDIVSP). Two fleets of vehicles serve a set of customers within their specified time windows. Delivery vehicles drop products to customers while installation vehicles drop and pick up technicians without necessarily waiting for them. Delivery must precede installation. This paper models SDIVSP as a mixed integer linear program, which solves small-sized instances. For large instances, this paper proposes a heuristic HALNS that hybridizes adaptive large neighborhood search and variable neighborhood search. Extensive experimentation provides computational evidence of the superiority of HALNS when solving well-known benchmark instances of (synchronized) vehicle routing problems with time windows and its effectiveness in tackling newly proposed SDIVSP instances. HALNS outperforms two state-of-the-art algorithms by an average 6.41% and 12.43% on synchronized vehicle routing with time windows benchmark sets. It further provides computational evidence of the benefits of shared installation vehicles: Total cost decreases by 41.72% and 35.35% as the installation vehicle capacity increases for tight and large time windows respectively.

Risk management and components’ coordination assessment in the design of a composite riverine flood defence system

ABSTRACT Flood control is one of the most important and difficult tasks in river basin management in terms of present several uncertainties. This study aims to follow the risk management and coordination analysis in designing a riverine flood defence system, actually, a composite multi-section flood control levee system considering the aleatory and epistemic uncertainties using contagious distributions. A stochastic optimization model was developed based on Monte Carlo analysis for uncertainty propagation, and then the system's optimum dimensions were computed in the single-section and multi-section cases for overall comparisons. Using the concept of ‘scenario probability’, the coordination of the system and its individual components was analysed. The results indicated that by increasing the complexity (from a single-section to a multi-section model) and by the separation of uncertainties (into epistemic and aleatory), the coordination of the system decreases 130% and 30%, respectively. Moreover, it was found that when economic uncertainty was considered, unexpected results may be obtained so that by increasing the system dimensions the total cost decreases. Finally, after analysing the sample histograms of the system's components, several distributions have been suggested. When there are vast datasets that might be confusing for decision-makers, the systematic method proposed here makes risk management easier.

The budget impact of introducing the OMNI® surgical system to a United States health plan for managing mild-to-moderate primary open-angle glaucoma

ABSTRACT Objectives Minimally invasive glaucoma surgery devices fill an unmet need in the treatment paradigm between topical intraocular pressure medicines and more invasive filtration procedures. This study evaluated the adoption of The OMNI® Surgical System with or without cataract surgery in primary open-angle glaucoma patients. Methods A budget impact analysis estimated costs before and after adoption of OMNI® to a hypothetical US health plan with 1 million Medicare-covered lives over two years. Model input data were derived from published sources and development of the model included primary research with key opinion leaders and payers. The model compared total annual direct costs for OMNI® versus other treatment options (medications, other minimally invasive surgical procedures, selective laser trabeculoplasty) to calculate budget impact. A one-way sensitivity analysis was conducted to assess parameter uncertainty. Results Increased adoption of OMNI® resulted in budget neutrality over the two years with a decrease in total costs of $35,362. Per member per month incremental costs were $0.00 when used without cataract surgery and yielded cost savings of -$0.01 when used with cataract surgery. Sensitivity analysis confirmed model robustness and identified surgical center fee variability as a key driver of costs. Conclusion OMNI® is budgetary efficient from a US payer perspective.

Model and algorithm for augmenting logistics network resilience with hybrid facilities and robust strategies

This paper introduces a mixed-integer nonlinear programming (MINLP) model aimed at designing a dependable closed-loop supply chain that can function effectively despite disruptions. The model incorporates several strategies to reduce the risks associated with random facility disruptions. First, the concept of p-robustness criterion is employed, which sets upper bounds on the cost in various disruption scenarios to protect against disruptions. To accommodate for the varying levels of reliability of facilities, the network consists of two categories of infrastructure: reliable and defective hybrid infrastructure. Thirdly, a sharing strategy is proposed to mitigate for the capacity loss at unreliable facilities by permitting transportation of products from reliable facilities to unreliable facilities. Additionally, an improved genetic algorithm (IGA) is devised for the MINLP model efficiently. The efficacy of the model and the tailored algorithm is evaluated by means of computational experiments. Finally, a sensitivity analysis is ultimately performed to furnish managers with meaningful insights. For example, the sharing strategies demonstrate a significant decrease of total cost. This paper contributes to enhancing resilience in logistics networks, ensuring their reliable performance even in the face of disruptions.

Auswirkungen der komplexen Modellintervention CoCare auf die medizinische Versorgung in deutschen Pflegeheimen: eine Übersicht über die Ergebnis- und Prozessevaluation

IntroductionMore and more people are being cared for in nursing homes these days. Multimorbidity results in frequent but sometimes unnecessary patient transports and hospitalisations. The CoCare intervention was developed as a nursing home-based intervention to improve coordination of medical care and GP and specialist care in long-term care homes. The project aimed to reduce avoidable hospital admissions and ambulance transportation, minimise costs through the resulting better collaboration and improving the quality of life of nursing home residents. This article presents the results of the process and outcome evaluation of the intervention in an integrated way and assesses them against the background of the project objectives. MethodsIntervention and data collection started in January 2018 and ended in September 2020. A mixed-methods design was chosen for the evaluation. The (cost) effectiveness of the intervention was tested by a controlled observational study, comparing intervention (IG) and control group (CG). As part of the evaluation of the results, claims data from health insurance funds and a questionnaire-based survey among nursing staff, physicians and nursing home residents were analysed. As part of the process evaluation, subjectively perceived changes in care and implementation difficulties were recorded with the help of focus groups and telephone interviews conducted quarterly. ResultsFrom the point of view of the health economic evaluation, with a decrease in total costs of € 468.56 (p<.001) per nursing home resident and quarter, an advantageous cost-benefit ratio can be assumed. Thus, the significant increase in outpatient care for nursing home residents goes along with a reduction of ambulance transportation by 0.19 (p=.049) and hospitalization rates by 0.08 (p=.001). In the nursing staff sample, a significant positive difference between IG (T1) and CG was observed with regard to communication and cooperation. In addition, pre-post comparison showed a significant improvement in the nursing staff's assessment of interprofessional cooperation in IG at T1 compared to T0. Both nursing staff and physicians perceived positive changes in care and positively assessed the benefit of the intervention. While practitioners experienced an intensification and improvement of communication and cooperation through the implementation of the CoCare measures, these changes were not perceived by residents. DiscussionThe CoCare intervention lowered the barriers for nursing homes to contact general practitioners, specifically in unclear situations and can thus be seen as an effective tool to reduce potentially avoidable hospital admissions and costs. It is conceivable that results can be transferred to other regions in Germany and to similar care scenarios. It should be examined to what extent approaches of this new form of care can be transferred to standard care and whether adjustments to facilitate the implementation of coordinated care approaches across occupational groups in inpatient care facilities can be proposed within the framework of legislative procedures.

Numerical investigation of methane steam reforming in packed bed reactor with internal helical heat fins

Hydrogen is one of the essential clean energy sources and methane steam reforming in a packed bed reactor is the primary method of hydrogen production. The internal helical heat fins are used extensively for enhancing the performance in different industrial devices. To improve the methane steam reforming, the packed bed reactors installed with the different internal helical heat fins have been investigated and the effects of different pitch have been compared. When the pitch become larger, the outlet mass flow and the velocity disturbance are increased by 9.5% and 382.3% respectively, however, too larger pitch brings the higher flow resistance. The internal helical heat fins improve the fluid temperature and reduce the thermal resistance. The temperature is increased by 19.2 K and the thermal resistance is decreased by 43.1% at maximum compared with the normal packed bed. While the pitch equals 120 mm, the efficiency is the highest, which is increased by 42.3%. Although the internal helical heat fins increase the cost of reactor due to the structure complexity, the total cost decreases. As the pitch equals 120 mm, the total cost is the lowest, which is about 1.6 USD/kg and decreased by 24.6%.

Present and future cost of alkaline and PEM electrolyser stacks

We use complementary bottom-up and top-down approaches to assess the current cost of AE and PEM stacks and how the costs are expected to come down by 2030. The total AE and PEM stack cost reduce from a range of 242–388 €/kW and 384–1071 €/kW in 2020 to 52–79 €/kW and 63–234 €/kW in 2030 respectively. The main drivers of these cost reductions are an increased current density and a reduction and/or replacement of expensive materials with cheaper alternatives. To a lesser extent, manufacturing and labor costs reduction is expected due to mass manufacturing at a GW scale. The total cost decrease is less prominent for AE than PEM due to AE's maturity. The uncertainty range for PEM stacks is due to the low TRL associated with the advanced design PEM stack.

Penerapan Limit Fungsi untuk Mencari Keuntungan Maksimum Ditinjau dari Biaya Marginal

Marginal costs are changes in total costs in the form of increases or decreases in total costs due to changes in the quality of goods or services. Through the types of production costs used as processing costs, it will be seen to what extent the use of capital will be used and profits will be obtained. This analysis will help to what extent a company can determine the planning for the business that is built, whether to get maximum or minimum profits. This study uses literature study, which is research in which data sources and data collection are obtained by taking library data, reading, recording and processing the data as research material. Based on an analysis of literature data regarding "Maximum Profits from Fishermen Business in East Balikpapan District" the results obtained are the maximum profit in terms of marginal costs, namely the maximum profit for the total catch of fishermen and occurs at a maximum catch rate (Q) of 159.92. So it was concluded that the fishing business in East Balikpapan District had not yet achieved maximum profit, because the fishermen's catch exceeded the maximum catch level requested.

Penerapan Limit Fungsi untuk Mencari Keuntungan Maksimum Ditinjau dari Biaya Marginal

Marginal costs are changes in total costs in the form of increases or decreases in total costs due to changes in the quality of goods or services. Through the types of production costs used as processing costs, it will be seen to what extent the use of capital will be used and profits will be obtained. This analysis will help to what extent a company can determine the planning for the business that is built, whether to get maximum or minimum profits. This study uses literature study, which is research in which data sources and data collection are obtained by taking library data, reading, recording and processing the data as research material. Based on an analysis of literature data regarding "Maximum Profits from Fishermen Business in East Balikpapan District" the results obtained are the maximum profit in terms of marginal costs, namely the maximum profit for the total catch of fishermen and occurs at a maximum catch rate (Q) of 159.92. So it was concluded that the fishing business in East Balikpapan District had not yet achieved maximum profit, because the fishermen's catch exceeded the maximum catch level requested.

Optimasi Waktu dan Biaya dengan Metode Crashing pada Proyek Pembangunan Jalan Simp. 4 Kaliorang – Talisayan Kalimantan Timur

A common construction problem is delays in project implementation. Delays on the project will affect the cost of the project. Seeing this incident, this study will analyze the acceleration of project time on the Road Construction project Simp. 4 Kaliorang - Kalisayan using the Precedence Diagram Method (PDM) using Microsoft Project 2010 software, so that jobs are on the critical path. The work that is on the critical path will be crashing with the method of adding working hours (overtime), and adding tools, the final result of the two alternative crashing will be a change in the acceleration of the duration of a project at an optimal cost. From the results of time and cost optimization research with the crashing method on the road construction project simp. 4 kaliorang - talisayan using the alternative of additional working hours (overtime) for 3 hours, 4 hours, 5 hours, and 6 hours and the second alternative with variations in the addition of tools and energy by 25%, 50%, 75% and 100% of the total equipment and normal power obtained the final result of the most optimum alternative of the two alternatives is the alternative of adding tools and energy by 100% of the number of normal tools and energy, the crash duration is 91 days from the normal duration of 135 days or an acceleration of duration of 44 days, with a total cost of Rp. 10,825,046,816.96 from the normal total cost of Rp. 10,868,916,625,88 or a decrease in total costs of 0.40%. Keywords: Delays, critical path, crashing, additional working hours (overtime), additional workforce, PDM (Precedence Diagram Method)

Automatic Lane-Changing Trajectory Planning: From Self-Optimum to Local-Optimum

Existing lane-changing (LC) algorithms in general prioritize self-interest above the benefits of others. We argue that an autonomous vehicle should be socially responsible during lane change without causing excessive impact on its surroundings. Thus, in this paper, we propose an LC algorithm that could generate a trajectory to optimize the overall benefits of the subject vehicle and others in its vicinity. This is mainly achieved by introducing Longitudinal Control Model to characterize the driving behaviors of neighboring vehicles, and drivers’ needs such as comfort, efficiency, and safety are simultaneously satisfied. We combine macro and micro comparative analysis to evaluate the advantage of our approach against existing algorithms. Our findings reveal that the vehicle controlled by our algorithm is capable of safely performing the LC maneuver while causing the least amount of impact. This is reflected in the decrease in total cost of all vehicles and the increase in traffic speed and throughput in its vicinity. In addition, we use HighD dataset for further validation and demonstrate that our algorithm is practically sound. Application of this research can lead to improved autonomous driving technology.

Optimization of Vehicle Paths considering Carbon Emissions in a Time-Varying Road Network

Regarding the study of the time-dependent green vehicle path problem (TDGVRP), most of the literature uses the step function to represent the vehicle speed change in order to reduce the computation, ignoring the continuity of vehicle speed, which leads to the lack of accurate carbon emissions measurement. This study represents the vehicle speed variation as a continuous function to make the constructed model more consistent with the actual situation, in order to promote the reduction of carbon emissions generated in the logistics and distribution process, improve the greenhouse effect and ecological environment, and ultimately promote sustainable development. In this paper, a simulated annealing-genetic hybrid algorithm (GA-SA) is proposed to solve the constructed optimization model, and two sets of comparison experiments are designed. The experimental results show that compared with the two classical algorithms, the simulated annealing-genetic hybrid algorithm (GA-SA) has better solution performance, inherits the robustness and potential parallelism of the genetic algorithm, and has a higher practical value. Meanwhile, although the total driving distance of the vehicle path considering carbon emissions increases by 3.52 km, the carbon emission cost and the total cost decrease by 5.6% and 3.4%, respectively, which confirms that the path optimization model considering carbon emissions constructed in this study can not only play the role of restraining carbon emissions but also reduce the total distribution cost and the waste of resources. In this study, a continuous function is used to represent the vehicle speed variation, and two classical optimization algorithms (the genetic algorithm and simulated annealing algorithm) are combined and parameter-optimized, and certain innovations are made in the processing of vehicle speed and the solution algorithm. Finally, the effectiveness of the model and algorithm is verified by experiments.

Environmental and economic assessment of food additive production from mushroom bio-residues

This work analysed the environmental and economic impacts of extracts enriched in ergosterol and vitamin D 2 production from bio-residues of Agaricus Bisporus mushrooms. The main hotspots of the extractive processes in environmental terms were identified through the standardized methodology Life Cycle Assessment. In respect to the economic analysis, the Material Flow Cost Accounting approach was applied to identify production costs, distinguishing between valued and wasted costs. The LCA results showed that electricity consumption is a key factor in environmental aspects, having the highest contribution to the process's environmental impact. In general, the most electricity-consuming unit processes analysed were lyophilization, extraction and evaporation. Despite evaporation being a process with great energy consumption, it also mitigates environmental impacts, since it allows solvent recovery through the extraction and reutilization phases in new iterations. The production costs for the identified extracts were determined: extract enriched in ergosterol (2.43€/1 g of extract) and extract enriched in vitamin D 2 (8.28€/1 g of extract). Recovered solvent incorporation in posterior iterations resulted in a total cost decrease of about 47% for ergosterol-extract and about 39% for vitamin D 2 extract. Solvent's recovery and reutilization allowed to not only reduce the environmental impacts but also the production costs.

Modeling the risk-based decisions of the microgrid in day-ahead energy and reserve markets considering stochastic dispatching of electrical and thermal energy storages

• The MGO’s bids in the markets are optimized with dispatching of local resources. • Uncertainties of electrical demand and power generation of PV are modeled. • A robust two-stage stochastic optimization model is developed. • The most capacity of electrical energy storage is used to provide reserve capacity. • With increasing the robust parameter, the MGO’s cost increases. In this paper, the electrical and thermal energy management problem of a micro-grid operator (MGO) is addressed under uncertainties aiming at participating in the day-ahead energy and reserve markets. For this purpose, a robust two-stage stochastic model is developed to protect the first stage MGO’s decisions, i.e., its bids in the energy and reserve markets, against the uncertainty of the real-time energy market price. This is done through stochastic dispatching of the MG resources which includes the electrical and thermal energy storages and the combined heat and power unit as the second-stage decisions. The results showed that the MGO’s expected total cost decreases when it participates in both the energy market and the reserve market in comparison with the case it only participates in the energy market. Also, the risk-based behavior of the MGO showed that increasing the robust parameter decreases the reserve provided for the market and the net power trading with the market. However, the proposed robust two-stage stochastic model leads to a smaller reduction of the MGO’s first-stage decisions in the worst case in comparison with the conventional methods, i.e. deterministic and probabilistic ones. This issue proves the effectiveness of the proposed approach to protect the MGO’s decisions against the uncertainties.