Cost Solution Research Articles

Degree-Constrained Minimum Spanning Hierarchies in Graphs

The minimum spanning tree problem in graphs under budget-type degree constraints (DCMST) is a well-known NP-hard problem. Spanning trees do not always exist, and the optimum can not be approximated within a constant factor. Recently, solutions have been proposed to solve degree-constrained spanning problems in the case of limited momentary capacities of the nodes. For a given node, the constraint represents a limited degree of the node for each visit. Finding the solution with minimum cost is NP-hard and the related algorithms are not trivial. This paper focuses on this new spanning problem with heterogeneous capacity-like degree bounds. The minimum cost solution corresponds to a graph-related structure, i.e., a hierarchy. We study the conditions of its existence, and we propose its exact computation, a heuristic algorithm, and its approximation.

Generalized multi-manufacturer multi-retailer production-delivery supply chain model and its minimum cost solution policy

Production of a product at multiple sources and its deliveries to multiple destinations are a common practice in business. Minimization of the integrated total cost of performing operations and transportation considering the relevant factors such as the minimum order quantity contract, capacities of transport vehicles, and times of transportation in this supply chain is essential, in supplying products to customers at reasonable lesser prices. However, such a supply chain has received little attention in terms of minimizing the integrated total cost taking into account these related factors explicitly. So, there lies a research scope on this topic to fulfill the growing need of minimizing the cost of production-deliveries of products to meet customers’ demands fruitfully. Here we develop such a generalized mathematical model to minimize the integrated total cost of carrying out operations at manufacturers and retailers, and transporting batches (sub-lots) of lots from sources to destinations considering the mentioned realistic constraints. The integrated production-delivery flow is synchronized by delivering lots with batches of equal and/or unequal sizes. First without considering transportation costs, we obtain optimal batches to minimize the total cost of the model. Each of these optimal batches is proportionally distributed at manufacturers as supplies and at retailers as demands. Then the minimum transportation cost solution from manufacturers to retailers is incorporated to the earlier solution to obtain the final result. We illustrate this solution policy with numerical example problems. Sensitivity analyses are performed to see the effect of increasing values of parameters on the minimum total cost.

Storing Excess Solar Power in Hot Water on Household Level as Power-to-Heat System

PV technology has become widespread in the Netherlands, reaching a cumulative installed capacity of 22.4 GWp in 2023 and ranking second in the world for solar PV per capita at 1268 W/capita. Despite this growth, there is an inherent discrepancy between energy supply and demand during the day. While the netting system in the Netherlands can currently negate the economic drawbacks of this discrepancy, grid congestion and imbalanced electricity prices show that improvements are highly desirable for the sustainability of electricity grids. This research analyzes the effectiveness of a Power-to-Domestic-Hot-Water (P2DHW) system at improving the utilization of excess PV electricity in Dutch households and compares it to similar technologies. The results show that the example P2DHW system, the WaterAccu, compares favorably as a low cost and flexible solution. In particular, for twelve different households differing in size (1–6 occupants), PV capacity (2.4–8 kWp), and size of hot water storage boiler (50–300 L), it is shown that the total economic benefits for the period 2024–2032 vary from −€13 to €3055, assuming the current net metering scheme is abolished in 2027. Only for large households with low PV capacity are the benefits a little negative. Based on a multi-criteria analysis, it is found that the WaterAccu is the cheapest option compared to other storage options, such as a home battery, a heat pump boiler, and a solar boiler. A sensitivity study demonstrated that these results are overall robust. Furthermore, the WaterAccu has a positive societal impact owing to its peak shaving potential. Further research should focus on the potential of the technology to decrease grid congestion when implemented on a neighborhood scale.

Impacts of Plug-in Electric Vehicles and Renewable Energy Source on Unit Commitment Problem using Cat Mouse Based Optimization A lgorithm

Objectives: The main objective of this paper is to solve the Cost Based Unit Commitment (CBIC) problem considering Renewable Energy Sources (RES) and Plug-in Electric vehicles (PEVs). The proposed problem minimizes the total operating of the interconnected system. Methods: A novel and recently developed successful optimization tool of Cat and Mouse Based Optimizer (CMBO) is proposed for optimal solution of Cost Based UC problem. The proposed CMBO approach is having two groups such as Cat group and Mice group for searching the global optimal solution with less computational time. It contains two phases like Cat’s movement towards Mice and Mice escape to a safe place is effectively updating the new position of Cat and Mice to easily reaching the best solutions. The control parameter of CMBO is number of agents is 50, number of variables is 10 and maximum number of iteration is 500. MATLAB 14.0 platform is utilized for the projected problem. Findings: The method tested on standard IEEE-39 bus system (10 generators and 24 hour) with an equivalent PEV and Wind farm. The CMBO approach minimize the total operating cost with various test cases. The outcomes such as UC schedule, Real power output of thermal, wind and PEV units, fuel cost, startup cost and total operating cost of the interconnected system are numerically and graphically reported. The total operating cost is 4.11 % minimized when compared with base case approach and 0.73 % reduced than the other existing method viz. Parallel UCs-RP method. Novelty: The CMBO is recently developed powerful optimizer and parameter free algorithm, so easily reaches the global optimal solutions. The obtained simulated results are compared with other mathematical and intelligent computational approaches for proving the efficiency and performance of the proposed CNBO technique. Keywords: Unit Commitment, Plug­in Electric Vehicles, Renewable Energy Sources, Cost minimization, Cat and Mouse Based Optimizer

Primjena ekspertnih sustava u građevinarstvu

Expert systems represent “IF-THEN” algorithms that integrate human expert knowledge to solve complex problems. This area of research focuses on finding solutions to challenging situations that are often difficult to resolve using traditional methods. Expert systems have widespread applications in the field of construction. The paper presents some of these applications, including their use in building maintenance, inspection of water and sewage systems, geotechnical engineering, road maintenance, and the selection of appropriate construction materials. Artificial neural networks are effective in optimizing financial plans, estimating the duration of activities, assessing the productivity of work teams, and selecting contractors. Genetic algorithms find their application in resource allocation, selecting optimal solutions for task scheduling and project costs, as well as in building maintenance, while fuzzy logic aids in risk analysis, project management, and cost estimation. Expert methods can be integrated into information systems to provide consistent information necessary for the effective management of construction projects. A review of published papers on the application of expert methods in construction shows that these methods are already significantly represented across all sectors of the construction industry, but also indicates that there are still substantial opportunities for their future application.

Exact methods and a variable neighborhood search for the robust capacitated [formula omitted]-median problem

The capacitated p-median problem (CPMP) involves placing p identical facilities in a network and assigning customer nodes to these facilities to satisfy all customer demands with minimal transportation costs. In practical applications, demand and distance parameters are often uncertain during the planning process, leading to infeasible or excessively costly solutions if these uncertainties are disregarded. This paper addresses the robust CPMP (RCPMP), which incorporates demand uncertainty into the problem using the robust optimization paradigm. We propose a general framework to model and solve the RCPMP, considering different polyhedral uncertainty sets, namely the cardinality-constrained and the knapsack sets. We develop exact approaches encompassing compact models, different families of valid inequalities, and branch-and-cut and branch-and-price algorithms, exploring both implemented uncertainty sets and problem structure. Furthermore, we implement an efficient Variable Neighborhood Search (VNS) heuristic to solve these robust variants, which incorporates state-of-the-art algorithms, parallelization techniques, and optimized data structures. Computational experiments using adapted benchmark instances with up to 400 nodes indicate the effectiveness of the proposed approaches. The results show that using parallelization and hash tables within the VNS heuristic promotes significant performance improvements and yields near-optimal solutions for most instances, as well as outperforming the exact approaches in several instances where the optimal solution was not found. Moreover, these results highlight the benefits of using robust solutions in practical settings, especially when considering different uncertainty sets to generate solutions with advantageous trade-offs between cost and risk.

Optimizing smart home appliance energy monitoring using Factorial Hidden Markov Models for Internet of Behavior

The energy demand which is increasing globally strongly underlines the necessity of solutions that will improve power efficiency. This study addresses the research problem of enhancing energy management in smart homes through Non-Intrusive Load Monitoring (NILM), a method that enables consumers and companies to optimize their energy usage by disaggregating total household energy consumption into specific appliance-level data. The research focuses on the creation and establishment of an Internet of Behavior (IoB) model that encourages NILM to develop more energy-efficient and clever residential buildings as its main goal. The Factorial Hidden Markov Model (FHMM) is employed as the core methodology in this NILM process, facilitating the disaggregation of overall household energy usage into individual appliance consumption patterns. This method was used over the known Reference Energy Disaggregation Dataset (REDD) for comparing the actual energy consumption of the appliances with the projected estimates. The results are evidence that the FHMM method is an energy disaggregation technique that can provide important information about the customer usage of energy and, hence will help in the strategic shifting of high-energy appliances to non-peak hours for the user. This study's main value lies in the new application of IoB-based NILM methods to increase energy efficiency, which is the real solution for energy costs and the grid peak load problem. The results of the proposed method are compared with other methods and real data. The comparisons show the acceptable response of the proposed method when it is compared with the real data and is better than another method.

Early post-operative outcome of pre-pectoral implant-based immediate total breast reconstruction with Polyglactin 910 (Vicryl™) mesh – low cost solution for a low-middle income country

BackgroundThe incidence of breast cancer in Pakistan has been rising with approximately one third of these patients requiring mastectomy. Among breast reconstruction treatment options, the use of Acellular Dermal Matrix (ADM) for pre-pectoral breast implant surgery has proven effective with improved cosmetic outcome. However, due to high cost it cannot be regularly implemented in a developing country like Pakistan. An alternative to ADM, Polyglactin 910 (Vicryl™, Ethicon) mesh has been introduced in pre-pectoral breast reconstructive surgery which has shown to be almost 10 times lower in cost. We set out to determine the frequency of early postoperative complications when using Polyglactin 910 mesh for pre-pectoral implant-based breast reconstruction surgery.MethodsA single centre, retrospective, chart review was conducted, and a total 28 women were included in the study. Thirty-two pre-pectoral implant-based mastectomies with Polyglactin 910 mesh were performed. Early post-operative outcomes (within 12 months of procedure) including duration of antibiotic use, post-operative infection, implant displacement, flap necrosis, seroma formation, wound dehiscence, hematoma formation, capsular contracture and reconstruction failure, were recorded.ResultsOnly 4 (12.5%) women experienced early post-operative morbidity. One patient developed a wound dehiscence, which eventually led to reconstruction failure and removal of the implant. Another patient had seroma formation and flap necrosis. None of the patients developed postoperative implant displacement, hematoma formation or capsular contracture in the early post-operative period.ConclusionThis study reveals that early post-operative outcomes with Polyglactin 910 mesh in breast reconstructive surgery are few, thus making it a cost effective, reliable, and safe treatment option, especially in developing countries like Pakistan.

Chaotic self-adaptive sine cosine multi-objective optimization algorithm to solve microgrid optimal energy scheduling problems

Researchers are increasingly focusing on renewable energy due to its high reliability, energy independence, efficiency, and environmental benefits. This paper introduces a novel multi-objective framework for the short-term scheduling of microgrids (MGs), which addresses the conflicting objectives of minimizing operating expenses and reducing pollution emissions. The core contribution is the development of the Chaotic Self-Adaptive Sine Cosine Algorithm (CSASCA). This algorithm generates Pareto optimal solutions simultaneously, effectively balancing cost reduction and emission mitigation. The problem is formulated as a complex multi-objective optimization task with goals of cost reduction and environmental protection. To enhance decision-making within the algorithm, fuzzy logic is incorporated. The performance of CSASCA is evaluated across three scenarios: (1) PV and wind units operating at full power, (2) all units operating within specified limits with unrestricted utility power exchange, and (3) microgrid operation using only non-zero-emission energy sources. This third scenario highlights the algorithm’s efficacy in a challenging context not covered in prior research. Simulation results from these scenarios are compared with traditional Sine Cosine Algorithm (SCA) and other recent optimization methods using three test examples. The innovation of CSASCA lies in its chaotic self-adaptive mechanisms, which significantly enhance optimization performance. The integration of these mechanisms results in superior solutions for operation cost, emissions, and execution time. Specifically, CSASCA achieves optimal values of 590.45 €ct for cost and 337.28 kg for emissions in the first scenario, 98.203 €ct for cost and 406.204 kg for emissions in the second scenario, and 95.38 €ct for cost and 982.173 kg for emissions in the third scenario. Overall, CSASCA outperforms traditional SCA by offering enhanced exploration, improved convergence, effective constraint handling, and reduced parameter sensitivity, making it a powerful tool for solving multi-objective optimization problems like microgrid scheduling.

Ni3C nanosheets and PVA nanocomposite based memristor for low-cost and flexible non-volatile memory devices

Two-dimensional (2D) MXenes have gained extensive attention in the field of memristors due to their high ion mobility, chemical stability and tunable electrical and surface properties. Moreover, MXenes have tunable interlayer bonding which enables enhancement in memristor performance. In this work, we synthesize a novel nanocomposite of Ni3C nanosheets and polyvinyl alcohol (PVA) and fabricate a flexible memristor with Ag/Ni3C-PVA/ITO/PET configuration for non-volatile memory applications. Ni3C nanosheets were synthesized from Ni-MAX (Ni3AlC2) through a solvothermal etching and exfoliation process. The nanosheet like structure of Ni3C is confirmed through Scanning Electron Microscopy (SEM) and Transmission Electron microscopy (TEM). Characterization techniques like X-ray diffraction (XRD), Raman spectroscopy and Fourier Transform Infrared Spectroscopy (FTIR) reveal the crystalline phases, surface functional groups and chemical bonds present in Ni3C nanosheets and Ni3C-PVA nanocomposite. The memristor was fabricated through a facile low cost solution processed based fabrication technique. The Ag/Ni3C-PVA/ITO/PET memristor exhibits bipolar non-volatile switching characteristics. The device with optimised mass ratio of 4:3 (Ni3C: PVA) shows a decent off/on ratio of 2.09 × 102, long retention time of 104 s, and an endurance of 102 DC cycles. The SET voltage of the as-fabricated device is 2.8 V and the RESET voltage is −5.33 V. The flexible memristor exhibits outstanding mechanical robustness over 500 bending cycles. The high charge carrier mobility of Ni3C results in lower switching voltage and the dielectric property of PVA improves data retention and off/on ratio of the memristor. The resistive switching behaviour is explained through conductive metallic bridge mechanism. The conduction mechanism follows Ohmic conduction in ON state and trap controlled space charge limited current (TCSCLC) mechanism in OFF state. The results demonstrate that the nanocomposite with its superior resistive switching effects is suitable for cost-effective, high-performance, flexible non-volatile memory devices.

DFT-D study of carbon-doped β12 borophene as a desirable anode material for sodium-ion batteries

In the quest for the elaboration of advanced energy storage systems (EESs), sodium-ion batteries (SIBs) have emerged as a hopeful alternative to their lithium-ion counterparts, owing to the abundant availability of sodium. This article delves into the utilization of carbon-doped β12 borophene as an anode material for SIBs using density functional theory (DFT) calculations. Based on the band structure calculations, the metallic electronic character of this structure facilitates rapid electron transfer, contributing to its superior performance. Moreover, the remarkably low-energy barrier of 0.066–0.328 eV for migration ensures efficient sodium ion transport, which is critical for new fast-charge technology and better charge/discharge performance. Our investigation reveals that carbon-doped β12 Borophene’s two-dimensional structure, high electrical conductivity, and theoretical capacity of 1972 mAh g−1 make it an ideal candidate for SIB anodes, offering a new avenue for the development of low cost and high-capacity energy storage solutions.

Guaranteed cost solution for discrete-time uncertain/nonlinear dynamic games

Motivated by an example of fiscal and monetary policy interaction of a national economy, the problem of uncertain/nonlinear two players discrete-time noncooperative games is investigated. Since the models of the systems are uncertain, the notion of Nash equilibrium solution is not suitable, instead, new Nash guaranteeing strategies and Nash guaranteed costs are defined. The system’s uncertainties and/or nonlinearities are assumed to be of quadratically bounded type. First, conditions of the Nash guaranteeing strategies are derived for general uncertain nonlinear systems. These results are specified for systems that have linear nominal part and quadratic cost functions. Approximate solutions are obtained by tractable quadratic matrix inequalities. To illustrate the application of the proposed method, two numerical examples are given.

Hosting capacity maximization of distributed energy resources through simultaneous optimized volt–var curve and network reconfiguration

Distributed Energy Resources (DERs) integration into distribution systems becomes problematic when the penetration level exceeds the system DER hosting capacity. Although several studies propose to quantify DER hosting capacity, methodologies to maximize it in distribution systems are scarce. Some approaches propose installing var compensators, incurring in costly solutions; others propose reduced cost solutions like network reconfiguration or optimized Volt–Var curve. Nevertheless, the combination of these approaches has not been proposed. Therefore, this paper proposes the simultaneous network reconfiguration and optimized setting of grid-tie inverter Volt–Var curve to maximize DER hosting capacity and minimize power losses. The uncertainties are addressed using Monte Carlo simulation, whereas Non-Dominated Sorting Genetic Algorithm II handles the multi-objective problem. The simulations are performed using Matlab and OpenDSS software, considering the IEEE 33-bus test system. Finally, the results demonstrate the superiority of the proposed approach against previous studies that performed network reconfiguration and Volt–Var optimization separately.

Prediction of Fuel and Exhaust Emission Costs of Heavy-Duty Vehicles Intended for Gas Transportation

This research focuses on heavy-duty vehicles intended to transport compressed natural gases, i.e., class-2 dangerous goods. The analysis includes heavy-duty vehicles powered by diesel and compressed natural gas and trailers with two body types. The body types used in the research are battery bodies and multiple-element gas containers, with pressure vessels made of composite materials (Type-4) and steel (Type-1). The paper presents the methodological procedure for predicting fuel and exhaust gas emission costs as a function of fuel consumption and transported gas quantities. The effects of different types of bodies and different types of fuel on the transported quantities of gas, vehicle mass utilization, fuel consumption, and exhaust gas emissions are shown. The obtained results show that bodies with Type-4 pressure vessels transport 44% more gas than bodies with Type-1 pressure vessels for one turn. The most cost-effective solution for emission costs is diesel-powered, newer-technology vehicles and Type-4 vessels, requiring EUR 2.82 per ton of gas. Similarly, the most economical choice for fuel costs is compressed natural-gas-powered vehicles with Type-4 bodies and a cost of EUR 19.77 per ton of gas. The research results’ practical application pertains to the selection procedures of vehicles and bodies intended for the transport of gases; they should be considered in the decision-making process, with the aim of attaining a sustainable transport sector with lower costs and less impact on the environment.

Human-wildlife conflicts in the Central River Region of The Gambia: scale, cost, and potential solutions

Human-wildlife conflicts in the Central River Region of The Gambia: scale, cost, and potential solutions

Georectifying drone image data over water surfaces without fixed ground control: Methodology, uncertainty assessment and application over an estuarine environment

Light-weight consumer-grade drones have the potential to provide geospatial image data to study a broad range of oceanic processes. However, rigorously tested methodologies to effectively and accurately geolocate and rectify these image data over mobile and dynamic water surfaces, where temporally fixed points of reference are unlikely to exist, are limited. We present a simple to use automated workflow for georectifying individual aerial images using position and orientation data from the drone's on-board sensor (i.e. direct-georectification). The presented methodology includes correcting for camera lens distortion and viewing angle and exploits standard mathematics and camera data processing techniques. The method is used to georectify image datasets from test flights with different combinations of altitude and camera angle. Using a test site over land, directly-georectified images, as well as the same images georectified using standard photogrammetry software, are evaluated using a network of known ground control points. The novel methodology performs well with the camera at nadir (both 10 m and 25 m above ground level) and exhibits a mean spatial accuracy of ±1 m. The same accuracy is achieved when the camera angle is 30° at 10 m above ground level but decreases to ±2.9 m at 30° and 25 m. The accuracy changes because the uncertainties are a function of the altitude and angle of the camera versus the ground. Drone in-flight positioning errors can reduce the accuracy further to ±5 m with the camera at 30° and 25 m. An ensemble approach is used to map the uncertainties within the camera field-of-view to show how they change with viewing distance and drone position and orientation. The complete approach is demonstrated over an estuarine environment that includes the shoreline and open water, producing results consistent with the land-based field-tests of accuracy. Overall, the workflow presented here provides a low cost and agile solution for direct-georectification of drone-captured image data over water surfaces. This approach could be used for collecting and processing image data from drones or ship-mounted cameras to provide observations of ocean colour, sea-ice, ocean glitter, sea surface roughness, white-cap coverage, coastal water quality, and river plumes. The Python scripts for the complete image georectification workflow, including uncertainty map generation, are available from https://github.com/JamieLab/SArONG.

A multi-generation system with integrated solar energy, combining energy storage, cooling, heat, and hydrogen production functionalities: Mathematical model and thermo-economic analysis

Increasing the proportion of renewable energy is of paramount importance for all countries in the world. In this work, a novel multi-generation system is designed to fully utilize solar energy, which includes a photovoltaic/thermal subsystem (PV/T), an absorption refrigeration cycle (ARC), a proton-exchange membrane (PEM) electrolysis, and a promising pumped thermal electricity storage (PTES) energy storage subsystem, which can simultaneously achieve the purposes of refrigeration, hydrogen production, heat production, and energy storage. Meanwhile, the solar radiation data of Wuwei City, Gansu Province, China for the year 2022 are selected for the study. According to different seasons, the thermodynamic and thermo-economic performances of the combined power, heat and hydrogen production (CPHH) mode operated in spring, autumn, winter, and the combined power, cooling and hydrogen production (CPCH) mode operated in summer are investigated, and multi-objective optimization is conducted. The results indicate that the direct normal irradiance (DNI) of solar, the area of PV/T, the thermal storage temperature of the PTES subsystem, and the pinch point temperature difference significantly affect the total energy efficiency, total exergy efficiency, and total product unit cost of the system. The multi-objective optimization results show that the optimal solutions of total energy efficiency and total product unit cost of the system are 85.90 %, 24.25 $·GJ−1 and 86.97 %, 24.25 $·GJ−1 for the CPHH mode in spring and winter, respectively, and 88.26 % and 22.21 $·GJ−1 for the CPCH mode in summer. This work can provide a valuable reference for the research of multi-generation systems with integrated renewable energy sources.

Solution combustion synthesis and exploration of chromium reduction and organic dyes degradation of cobalt tungstate (CoWO4) nanoparticles

Facile preparation route for cobalt tungstate (CoWO4) via cost effective, time saving, easy and solution combustion approach is described in the present paper. The structural, morphological properties of cobalt tungstate characterized by different physiochemical techniques which include X-ray diffraction, scanning electron microscopy (SEM), energy dispersive X-ray absorption (EDS). The optical properties assessed by UV–visible spectroscopy (DRS) and photoluminescence spectroscopy (PL). Using these methods, it was discovered that CoWO4 which has been synthesized displayed a monoclinic phase of Wolframite with great crystallinity and purity. The average crystallite size of prepared CoWO4 was 22 nm and band gap obtained was 2.84 eV due to which it can be used as visible light photocatalyst in aqueous medium. As a result, in this study, the photocatalytic degradation ability of CoWO4 was assessed by observing the aqueous phase degradation of the most widely used dyes, methylene blue (MB) and Rhodamine-B as well as Cr(VI) reduction under visible light. Almost all MB degraded in 90 min whereas 69 % degradation of RhB takes place in 240 min under visible light irradation. The different aspects for low rate of photodegradation of RhB dye than MB dye was also discussed in this report. The present study deals advanced approach that CoWO4 can also be used as capable visible light photocatalyst for degradation of dangerous contaminants and for reduction of carcinogenic Cr(VI) into nontoxic Cr(III) from waste water.

Unmanned aerial system plant protection products spraying performance evaluation on a vineyard

In the context of increasing global food demand and the urgent need for production processes optimization, plant protection products play a key role in safeguarding crops from insects, pests, and fungi, responsible of plant diseases proliferation and yield losses. Despite the inaccurate distribution of conventional aerial spraying performed by airplanes and helicopters, Unmanned Aerial Spraying Systems (UASSs) offer low health risks and operational cost solutions, preserving crops and soil from physical damage. This study explores the impact of UASS flight height (2 m and 2.5 m above ground level), speed (1 m s−1 and 1.5 m s−1), and position (over the canopy and the inter-row) on vineyard aerial spraying efficiency by analysing Water Sensitive Papers droplet coverage, density, and Number Median Diameter using a MATLAB script. Flight position factor, more than others, influenced the application results. The specific configuration of 2 m altitude, 1.5 m s−1 cruising speed, and inter-row positioning yielded the best results in terms of canopy coverage, minimizing off-target and ground dispersion, and represented the best setting to facilitate droplets penetration, reaching the lowest parts generally more affected from disease. Further research is needed to assess UASS aerial PPP distribution effectiveness and environmental impact in agriculture, crucial for technology implementation, especially in countries where aerial treatments are not yet permitted.

Isolated decompression for degenerative spondylolisthesis is less costly than fusion even with high revision rates

BACKGROUND CONTEXTIsolated decompression and decompression with instrumented fusion are accepted surgical treatments for lumbar spondylolisthesis. Although isolated decompression is a less costly solution with similar patient-reported outcomes, it is associated with higher rates of reoperation than primary fusion. PURPOSETo determine the costs associated with primary decompression, primary fusion, and decompression and fusion for degenerative spondylolisthesis. We further sought to establish at what revision rate is primary decompression still a less costly surgical treatment for degenerative lumbar spondylolisthesis. STUDY DESIGN/SETTINGA retrospective database study of the Medicare Provider Analysis and Review (MEDPAR) limited data set. PATIENT SAMPLEPatients who underwent single-level fusion or decompression for degenerative spondylolisthesis. OUTCOME MEASURESCost of surgical care. METHODSAll inpatient stays that underwent surgery for single-level lumbar/lumbosacral degenerative spondylolisthesis in the 2019 calendar year (n=6,653) were queried from the MEDPAR limited data set. Patients were stratified into three cohorts: primary decompression (n=300), primary fusion (n=5,757), and revision fusion (n=566). Univariate analysis was conducted to determine cost differences between these groups and results were confirmed with multivariable regression. An economic analysis was then done to determine at what revision rate would primary decompression still be a less costly treatment choice. resultson univariate analysis, the cost of primary single-level decompression for spondylolisthesis was $14,690±9,484, the cost of primary single-level fusion was $26,376±11,967, and revision fusion was $26,686±11,309 (p<0.001). on multivariate analysis, primary fusion was associated with an increased cost of $3,751, and revision fusion was associated with increased cost of $7,502 (95%ci: 2,990-4,512, p<0.001). economic analysis found that a revision rate less than or equal to 43.8% would still result in primary decompression being less costly for a practice than primary fusion for all patients. CONCLUSIONSIsolated decompression for degenerative lumbar spondylolisthesis is a less costly treatment choice even with rates of revision fusion as high as 43.8%. This was true even with an assumed revision rate of 0% after primary fusion. This study solely looks at cost data, however, and many patients may still benefit from primary fusion when appropriately indicated.