Cost Reduction Approach Research Articles

Energy exchange optimization among multiple geolocated microgrids: A coalition formation approach for cost reduction

This paper describes a hierarchical energy management system for multiple geolocated microgrids, with the aim of minimizing operational costs and maximizing individual benefits. To achieve this goal, a coalition formation algorithm is developed to optimize energy exchanges between geolocated microgrids, leading to a significant reduction in costs. At the local level (first layer), optimization is performed using mixed-integer linear programming, while at the central level, the optimization is carried out through the coalition formation algorithm. The formation of coalitions among geolocated microgrids has demonstrated substantial benefits. For instance, coalitions showed the highest percentage reductions in losses (52.63% to 71.53%) in the cooperative state compared to the non-cooperative state, indicating significant cost savings. In contrast, lower percentage reductions in losses (12.08% to 16.10%) were observed, yet they still benefited from reduced operational costs. Throughout the day, the cooperative method consistently proved to be more effective than the non-cooperative method. The effectiveness of coalitions in reducing losses and operational costs is demonstrated, emphasizing the importance of flexible approaches in addressing challenges such as geolocation and variable weather conditions. This study contributes to the advancement of distributed energy systems, supporting the transition to more sustainable and resilient systems.

Optimization and Experimental Verification of Lower Articulation for Low-Floor Tram Using Meta-Model

This study aimed to optimize the lower articulation of low-floor trams using a meta-model technique, followed by an experimental validation. The design variables were identified through structural analyses, and the optimization reduced the weight of the lower articulation by 20% while maintaining load-bearing performance. Strain gauges were attached to failure-prone areas for structural and fatigue testing. The results of the optimized design were compared with experimental data, confirming its validity. This method effectively reduces the cost and improves the performance of tram articulation devices. This study presents an effective approach for cost reduction and performance improvement in the lower articulation of low-floor trams.

Optimized Floating Offshore Wind Turbine Substructure Design Trends for 10–30 MW Turbines in Low-, Medium-, and High-Severity Wave Environments

Floating offshore wind is a promising renewable energy source, as 60% of the wind resources globally are found at depths requiring floating technologies, it minimizes construction at sea, and provides opportunities for industrialization given a lower site dependency. While floating offshore wind has numerous advantages, a current obstacle is its cost in comparison to more established energy sources. One cost-reduction approach for floating wind is increasing turbine capacities, which minimizes the amount of foundations, moorings, cables, and O&M equipment. This work presents trends in mass-optimized VolturnUS hull designs as turbine capacity increases for various wave environments. To do this, a novel rapid hull optimization framework is presented that employs frequency domain modeling, estimations of statistical extreme responses, industry constructability requirements, and genetic algorithm optimization to generate preliminary mass-optimal VolturnUS hull designs for a given turbine design and set of site conditions. Using this framework, mass-optimized VolturnUS hull designs were generated for 10–30 MW turbines for wave environments of varying severities. These design studies show that scaling up turbine capacities increases the mass efficiency of substructure designs, with decreasing returns, throughout the examined turbine capacity range. Additionally, increased wave environment severity is shown to increase the required mass of a given substructure design.

Optimal electric vehicle charging and discharging scheduling using metaheuristic algorithms: V2G approach for cost reduction and grid support

The adoption of Electric Vehicles (EVs) in the transportation sector is expected to grow significantly in the coming few years. While EVs offer numerous benefits, including being environmentally friendly, energy-efficient, low-noise, and can intelligently interact with smart grids through Vehicle-to-Grid (V2G) technology, their widespread adoption will increase energy demand and present challenges to grid load management. Furthermore, EV users face issues such as charging costs, charging time, access to public charging infrastructure, and more. In this article, we propose an approach utilizing metaheuristic algorithms to schedule the charging and discharging activities of EVs while parking, leveraging V2G technology with the goal of reducing the daily costs of EV users and addressing energy demand management challenges in smart grids. Four metaheuristic algorithms inspired by evolutionary and swarm concepts are applied, including Differential Evolution (DE), Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), and Grey Wolf Optimizer (GWO). The results obtained from the proposed approach demonstrate the feasibility of scheduling EVs charging and discharging activities to minimize EV user costs through V2G integration. This, in turn, contributes to enhancing the overall EV user experience and addressing energy demand management issues. Additionally, the results show that WOA outperformed the other algorithms in terms of convergence. This work can be further developed to create an integrated algorithm to balance the interests of both EV users and parking facility operators.

Optimization Approaches for Cost Reduction in Preventive Maintenance Strategies: A Comparative Study

Optimization Approaches for Cost Reduction in Preventive Maintenance Strategies: A Comparative Study

Optimum operation management of microgrids with cost and environment pollution reduction approach considering uncertainty using multi‐objective NSGAII algorithm

AbstractGenerating electricity near the consumption can provide more flexibility to supply various services to consumers as well as reduce system losses. The limited fossil fuels and air pollution are among the main incentives for the expansion of this technology. One of the perspectives proposed for effective increase in these resources’ involvement is to combine these resources with the objectives of the visibility of the relationship between distributed generation resources and power network, as well as control of these resources more efficiently. One of the methods of combining distributed generation sources is a new concept called micro grid; a topic that is of great importance in this regard is the proper management of distributed generation resources in micro grid aimed to reduce the costs of generating electricity and polluting the environment. The resource management in micro grid is a completely non‐linear and top‐order problem. In this paper, micro grid optimum programming has been studied considering the capacities available in the electricity market. In order to realize this, as well as reduce pollution and cost simultaneously, NSGAII multi‐objective genetic algorithm is used. Studies have been conducted in the form of three scenarios on a sample micro grid consisting of solar, wind, micro turbine, fuel cell and battery resources, considering the uncertainty of load and solar and wind generations. The probabilistic distribution function (PDF) and Roulette Wheel were used to create variables with uncertainty. The first scenario objective was to reduce costs by PSO, GA, and ABC algorithms’ optimization. In the second scenario, pollution was reduced and the studies were repeated using these three algorithms. Finally, in the third scenario, there was a simultaneous reduction in pollution and cost by these three algorithms, taking into account weight coefficients as well as NSGAII algorithm.

Cost Effective Institution Practices in Indonesia: Case Study of Badung Regency Government

This study tries to explore the practices of cost effective institutions (CEI) in the Badung district government through two approaches, namely the cost-reduction approach and the cost-control approach. This research is a descriptive research with qualitative method. The selection of research loci in Badung Regency was carried out through the Regional Fiscal Capacity Map (KFD) and the Regional Fiscal Independence Index (IKF) Map which showed very developed and good conditions so that they could serve as examples for other local governments in Indonesia. The data collection was carried out by in-depth interviews in two groups, namely the government group and the public group. The results of this study conclude that in principle the Badung Regency Government has implemented a Cost Reduction and Cost Control mechanism with a fairly good implementation strategy and method. However, not all components can be fulfilled according to the ideal concept of Cost Effective Institutions that has been mentioned in Hickel (1993). In general, at least three main stages of CEI implementation practices were found, starting from the planning stage, implementation stage, and monitoring and evaluation stage. There is not much difference between theory and practice, only the model created from the findings of this study reflects the detailed characteristics of budget management in the Badung Regency Government. This study recommends the application of CEI as an approach that is able to encourage the creation of local government functions with a spending control mechanism in order to achieve optimal performance.

An optimal edge server placement approach for cost reduction and load balancing in intelligent manufacturing

An optimal edge server placement approach for cost reduction and load balancing in intelligent manufacturing

Cost reduction approaches for fermentable sugar production from sugarcane bagasse and its impact on techno-economics and the environment

In an enzymatically driven lignocellulosic biorefinery, pretreatment and hydrolysis modules are the two most significant cost contributors for obtaining high gravity sugar solutions. The present study aimed to reduce the use of alkali and Cellic CTec2 during the bioprocessing of sugarcane bagasse (SCB). Later its impact on the overall process economics and the environment was evaluated. During pretreatment, solid loading of 15% (w/w) and use of 2% (w/v) sodium hydroxide at 121 °C for 30 min emerged as an optimum strategy. It resulted in > 65% delignification of SCB, retaining ≥ 90% and 65% of glucan and xylan fraction, respectively, in the pretreated biomass. Two approaches were evaluated in parallel to minimize the requirement of this commercial cellulase enzyme blend. The first strategy involved its partial replacement with an in-house enzyme cocktail by blending. The second route was performing hydrolysis with reduced loadings of cellulase enzyme blend above its optimum temperature, which gave more promising results. Hydrolysis of 20% alkali pretreated SCB with cellulase enzyme blend dosed at 15 mg protein g−1 glucan led to 84.13 ± 1 and 83.5 ± 2.3% glucan and xylan conversion yields respectively in 48 h at 52.5 °C. The filtrate and wash fraction contained ≥ 165 and ≥ 65 g L−1 sugar monomers representing glucose and xylose. However, in both the fractions > 75%, sugar accounted for glucose. The techno-economic analysis revealed that the sugar production cost from SCB was 1.32 US$/kg, with the optimized bioprocess. Environmental impact study showed that the process contributed to 1.57 kg CO2 eq in terms of climate change.

Locating and Sizing of Distributed Generation Sources and Parallel Capacitors Using Multiple Objective Particle Swarm Optimization Algorithm

In this paper, the MOPSO algorithm has been used to locate and determine the capacity of distributed generation sources and capacitor banks in the distribution system. The intended objective function is a combination of different objective functions. The first goal is to reduce losses, and the second goal is to improve the voltage profile and the third goal is to reduce costs, which has been used by placing weight coefficients in the form of an objective function in the algorithms. For this purpose, the standard 33-bus system has been used to conduct studies. Studies have been repeated in three scenarios. In the first scenario, the locating and determination of the capacity of active and reactive resources has been accomplished with the approach of reducing losses and improving the voltage profile. However, in the second scenario, the locating and determining the capacity of these resources has been accomplished with loss and cost reduction approach and it was considered as constraint in voltage profile. In the third scenario, the simultaneous reduction of all three objective functions has been performed simultaneously. To validate the results obtained by the MOPSO algorithm, its results were compared with genetic and particle swarm algorithms. The results indicate better and more accurate performance of MOPSO algorithm in minimizing objective functions relative to other two algorithms.

Review of space habitat designs for long term space explorations

This review paper attempts to provide a useful reference for space habitat designs that advance the ability of humans to live in space for long periods of time. Five space habitat design concepts are reviewed with the principal aim of addressing the five fundamental problems in long term space exploration: artificial gravity, efficient radiation protection, sustainable food, growth strategy, and commercial values. The design of principal categories of life support systems, habitat subsystems, economics and cost reduction approaches are presented with a focus on establishing useful sources of fundamental issues in the literature, as well as highlighting important research directions and topics. Humans have enjoyed isolated successes in space, but the international space station (ISS) is going to retire soon. The space habitat design for long-duration use will be the next ambitious space program with contributions from the world-wide scientific and technical community. The overarching theme of the paper is to show the diversity, richness, and fundamental problems of habitat designs that have been undertaken in the last 60 years of space exploration.

A Dry Room-Free High-Energy Density Lithium-ion Batteries Enabled by Impurity Scavenging Separator Membrane

Although lithium-ion batteries (LIBs) are used in various fields, such as small devices and electric vehicles, the low cycling stability due to the acceleration of salt degradation at high temperatures remains a significant challenge. The batteries are typically assembled in a dry room that controls moisture because lithium salts in the electrolytes are highly reactive with moisture, which has a significant effect on the battery performance. In this work, impurity scavenging separator membrane (ISM) was fabricated using a powerful H2O and HF scavenging material. This material was synthesized by an urethane reaction between porous silica (p-SiO2) and (3-isocynatopropyl)triethoxysilane (ICPTES). The p-SiO2 reaction with ICPTES suppressed the acidification of the electrolyte with water and resulted in maintaining the shape of the SiO2 particles. The multifunctional separator exhibited high capacity retention of 87%, 79%, and 74% at various electrodes including LiMn2O4 (LMO)//Li4Ti5O12 (LTO), Li[Ni0.8Co0.1Mn0.1]O2 (NCM)//graphite, and LMO//graphite, respectively, at high temperature (55℃). Furthermore, the ISM improves the cycle stability of batteries that use an electrolyte containing 1000 ppm of water. For the first time, a pouch full-cell was manufactured in a dry room-free system to confirm the excellent H2O and HF scavenging ability of the developed ISM, which was confirmed by the large area of battery size (4ⅹ6 cm2). This method presents a new approach for cost reduction in the electric vehicle market .

Design Approach for Quality Improvement and Cost Reduction: A Case Study of Cable Manufacturing

In this study, an extrusion process was studied in a cable manufacturing company located in Southeastern Nigeria. The critical-to-quality (CTQ) characteristic considered in the study was the cable diameter and the design effort was to reduce the possibilities of having cables with inconsistent dimensions. Taguchi Orthogonal Array Design L16 (4˄2) was used to achieve the optimum parameter settings after statistically investigating the assumed correlations between the process parameters in relation to cable dimension. Thereafter, an appropriate engineering tolerance interval for the improved process was designed to tighten the existing tolerance of the process from T±0.185 to T±0.032, thus reducing the engineering tolerance by 82.7% from the initial tolerance limit. After the process improvement, the Taguchi loss function approach was used to estimate cost attributed to deviations. The loss function estimation result has shown that the cost attributed to cable diameter deviations from the nominal value reduced from the initial value of ₦7.34/coil to a reduced cost of ₦2.08/coil. The sigma level increased from 0.6 to 5.2 at the project termination stage and the estimated annual loss decreased by 72% from the baseline value.

Approach to cost-of-quality management in pharmaceutical manufacturing

Introduction: The costs related to pharmaceutical production, including quality costs are a substantial and persistent part of the costs of pharmaceutical companies. Moreover, there are a number of activities in pharmaceutical production, originating from regulatory requirements to ensure the quality of pharmaceuticals, which do not contribute to real value; however, they consume time, materials and human resources. One of them is the operation of an effective quality management system in accordance with the requirements of the Good Manufacturing Practice for pharmaceuticals. The adherence to all regulatory requirements related to pharmaceutical production is quite costly and constitutes a substantial part of the budget of pharmaceutical companies. There are, however, opportunities for cost reduction which can increase profit without extra investments, ensuring at the same time regulatory compliance. Materials and Methods: A scientific literature review was performed with regards to cost management in pharmaceutical production. On the grounds of the analysis, personal experience and observation, some factors were identified which could impact the costs of pharmaceutical manufacturers and an approach for their management was developed, aiming to reduce the costs and improve company performance. Results: The costs for materials, equipment, production process, personnel, control and facilities were identified as major sources of production costs in the pharmaceutical industry, which affect the quality costs. The costs were classified by type and specific approaches for cost reduction were proposed; the expected effects of this reduction on the company results were assessed, e.g. reduction of total costs, losses and product unit costs, reduction of the duration of production, reduction of downtime and the time required for preparation and effective use of facilities, as well as quality costs (for assessment, prevention, internal and external issues/failures). Conclusion : The proposed approaches for cost reduction can be used as a manual for cost assessment, as well as for the suitability and prioritization of the relevant approaches for their reduction. They have a major impact on the “good quality” costs, related to assessment and prevention; they also contribute to the reliability of quality assurance, thus reducing the costs for its application for handling of internal and external issues. The proposed approaches have a synergistic effect on quality costs reduction and on the improvement of company performance.

An Energy-Efficient and High Throughput in-Memory Computing Bit-Cell With Excellent Robustness Under Process Variations for Binary Neural Network

In-memory computing (IMC) is a promising approach for energy cost reduction due to data movement between memory and processor for running data-intensive deep learning applications on the computing systems. Together with Binary Neural Network (BNN), IMC provides a viable solution for running deep neural networks at the edge devices with stringent memory and energy constraints. In this paper, we propose a novel 10T bit-cell with a back-end-of-line (BEOL) metal-oxide-metal (MOM) capacitor laid on pitch for in-memory computing. Our IMC bit-cell, when arranged in a memory array, performs binary convolution (XNOR followed by Bit-count operations) and binary activation generation operations. We show, when binary layers of BNN are mapped into our IMC arrays for MNIST digit classification, 98.75% accuracy with energy efficiency of 2193 TOPS/W and throughput of 22857 GOPS can be obtained. We determine the memory array size considering the word-line and bit-line nonidealities and show how these impact classification accuracy. We analyze the impact of process variations on classification accuracy and show how word-line pulse tunability provided by our design can be used to improve the robustness of classification under process variations.

An Effective Regression Test Case Selection Using Hybrid Whale Optimization Algorithm

Test suite optimization is an ever-demanded approach for regression test cost reduction. Regression testing is conducted to identify any adverse effects of maintenance activity on previously working versions of the software. It consumes almost seventy percent of the overall software development lifecycle budget. Regression test cost reduction is therefore of vital importance. Test suite optimization is the most explored approach to reduce the test suite size to re-execute. This article focuses on test suite optimization as a regression test case selection, which is a proven N-P hard combinatorial optimization problem. The authors have proposed an almost safe regression test case selection approach using a Hybrid Whale Optimization Algorithm and empirically evaluated the same on subject programs retrieved from the Software Artifact Infrastructure Repository with Bat Search and ACO-based regression test case selection approaches. The analyses of the obtained results indicate an improvement in the fault detection ability of the proposed approach over the compared ones with significant reduction in test suite size.

Light transmission and internal scattering in pulsed laser-etched partially-transparent silicon wafers

Continuing trend in silicon wafer thickness directed at cost reduction approaches basic boundaries created by: (a) mismatch between Al paste and Si wafer thermal expansion and (b) incomplete optical absorption. With its symmetrical front and back electrical contacts, the bifacial solar cell setup reduces stress due to mismatch thermal expansion, decreases metal use and increases high temperature efficiency. Efficiency improvement is accomplished in bifacial solar cells by capturing light from the back surface. Partially transparent wafers provide an option to improve near-infrared radiation absorption within Si wafer. To fully absorb optical radiation, three-dimensional texture of these kinds of wafers is essential. Pulsed laser interactions, thermal oxidation, and wet chemical etching are included in this research. A feature of its energy and pattern setup is the interaction of pulsed laser with Si, running at 1.064 μm wavelength and micro-second length. Two experimental settings were explored: (a) post-laser chemical etching with potassium hydro-oxide etching with thermal oxide as etching mask and (b) post-laser heat Si surface oxidation. Due to fast melting and recrystallization, laser pulsed processing inherently produces its own texture. Some of these spherically-shaped, randomly focused characteristics improve inner scattering and boost near-infrared absorption within the wafer. These characteristics are separated during chemical etching with the thermally-grown oxide layer as an etch mask. Comparison of optical absorption in both surfaces shows almost a rise in the magnitude of absorption in non-etched surfaces. Detailed optical (optical microscope and IR absorption), morphological (field emission scanning electron microscope) and heat imaging (far IR camera) analyses were performed to comprehend physical processes that contribute to near-IR absorption improvement. Such kinds of partially-transparent, three-dimensional textured Si wafers are anticipated to discover applications for bifacial solar cells as substrates.

Combined Density Functional and Algebraic-Diagrammatic Construction Approach for Accurate Excitation Energies and Transition Moments.

A composite of time-dependent density functional theory (TDDFT) and the second-order algebraic-diagrammatic construction [ADC(2)] approach is presented for efficient calculation of spectral properties of molecules. Our method can be regarded as a new excited-state double-hybrid (DH) approach or a dressed TDDFT scheme, but it can also be interpreted as an empirically tuned ADC(2) model. Several combinations of exchange-correlation functionals and spin-scaling schemes are explored. Our best-performing method includes the Perdew, Burke, and Ernzerhof exchange and Perdew's 1986 correlation functional and employs the scaled-opposite-spin approximation for the higher-order terms. The computation time of the new method scales as the fourth power of the system size, and an efficient cost-reduction approach is also presented, which further speeds up the calculations. Our benchmark calculations show that the proposed model outperforms not only the existing DH approaches and ADC(2) variants but also the considerably more expensive coupled-cluster methods.

Mutation-based genetic algorithm for efficiency optimisation of unit testing

Fault in a software program can be detected by mutation testing. However, mutation testing is an expensive process in a software testing domain. In this paper, we have introduced a method based on genetic algorithm and mutation analysis for unit testing process. Software industry produces high quality software in which software testing has an important role. First, we make a program/software and intent some mutant in this program/software, find most critical path and optimise test cases using genetic algorithm for the unit testing. Initially generated test cases are refined using genetic algorithm. We use a mutant function for measuring the adequacy of the test case set. The given mutant function is used to calculate a mutant score. We have achieved 100% path coverage and boundary coverage using mutation testing. The objective is to produce a set of good test cases for killing one or more undesired mutants and produces different mutant from original software/program. Unlike simple algorithms, genetic algorithms provide suitability for reducing the data generation at a comparable cost. An optimised test case has been generated by proposed approach for cost reduction and revealing or killing undesired test cases.

Mutation-based genetic algorithm for efficiency optimisation of unit testing

Fault in a software program can be detected by mutation testing. However, mutation testing is an expensive process in a software testing domain. In this paper, we have introduced a method based on genetic algorithm and mutation analysis for unit testing process. Software industry produces high quality software in which software testing has an important role. First, we make a program/software and intent some mutant in this program/software, find most critical path and optimise test cases using genetic algorithm for the unit testing. Initially generated test cases are refined using genetic algorithm. We use a mutant function for measuring the adequacy of the test case set. The given mutant function is used to calculate a mutant score. We have achieved 100% path coverage and boundary coverage using mutation testing. The objective is to produce a set of good test cases for killing one or more undesired mutants and produces different mutant from original software/program. Unlike simple algorithms, genetic algorithms provide suitability for reducing the data generation at a comparable cost. An optimised test case has been generated by proposed approach for cost reduction and revealing or killing undesired test cases.