Cost Objectives Research Articles

Optimization design of rural residences in severe cold zone of China weighing the dual-control objectives of life cycle carbon emission and economic cost

Optimization design of rural residences in severe cold zone of China weighing the dual-control objectives of life cycle carbon emission and economic cost

Product tracing or component tracing? Blockchain adoption in a two-echelon supply chain management

Consumer awareness of product authenticity and carbon footprint tracing are among the most remarkable reasons for adopting blockchain in the supply chain in today’s world. However, the research literature has not yet examined specific ways to adopt blockchain in the supply chain. This study aims to develop a decision support tool to deal with the adoption of blockchain technology to design a two-echelon supply chain. In this regard, four specific cases for integrating supply chain and blockchain are developed based on types of tracing and block generation authority. In product tracing, green products are investigated throughout the supply chain, while in component tracing, green products are examined between the components of the supply chain. As it is necessary to record and verify the supply chain information by authorities in the blockchain network, in this work, such authorities are taken into account for both links and members of the supply chain. As far as we know, this is the first attempt to classify the various methods of adopting blockchain in Green Supply Chain Management (GSCM) and propose mathematical optimization models related to them. In this line, four Mixed-Integer Linear Programming (MILP) models with the aim of minimizing the costs related to the physical supply chain and blockchain deployment are developed for the integration of the supply chain with blockchain technology. They are treated by the Branch and Efficiency (B&E) algorithm and Simultaneous Data Envelopment Analysis (SDEA) model considering common (cost and service) and innovative (blockchain) criteria. The results showed that link-based and component tracing models are cost-effective. In addition, the cost objective function of green product tracing is more sensitive to the number of blocks than that of component tracing. Eventually, the study provides great opportunities for decision-makers and managers to understand how to adopt blockchain in terms of supply chain network characteristics, cost, transparency, and service.

Application and practice of LILCEO sintering proportion optimization algorithm in sintering plant

Purpose To ensure the stable operation of ironmaking process and the quality and output of sinter, the multi-objective optimization of sintering machine batching process was carried out. Design/methodology/approach The purpose of this study is to establish a multi-objective optimization model with iron taste content and batch cost as targets, constrained by field process requirements and sinter quality standards, and to propose an improved balance optimizer algorithm (LILCEO) based on a lens imaging anti-learning mechanism and a population redundancy error correction mechanism. In this method, the lens imaging inverse learning strategy is introduced to initialize the population, improve the population diversity in the early iteration period, avoid falling into local optimal in the late iteration period and improve the population redundancy error correction mechanism to accelerate the convergence rate in the early iteration period. Findings By selecting nine standard test functions of BT series for simulation experiments, and comparing with NSGA-?, MOEAD, EO, LMOCSO, NMPSO and other mainstream optimization algorithms, the experimental results verify the superior performance of the improved algorithm. The results show that the algorithm can effectively reduce the cost of sintering ingredients while ensuring the iron taste of sinter, which is of great significance for the comprehensive utilization and quality assurance of sinter iron ore resources. Originality/value An optimization model with dual objectives of TFe content and raw material cost was developed taking into account the chemical composition and quality indicators required by the blast furnace as well as factors such as raw material inventory and cost constraints. This model was used to adjust and optimize the sintering raw material ratio. Addressing the limitations of existing optimization algorithms for sintering raw materials including low convergence accuracy slow speed limited initial solution production and difficulty in practical application we proposed the LILCEO algorithm. Comparative tests with NSGA-III MOEAD EO LMOCSO and NMPSO algorithms demonstrated the superiority of the proposed algorithm. Practical applications showed that the proposed method effectively overcomes many limitations of the current manual raw material ratio model providing scientific and stable decision-making guidance for sintering production operations.

Impact of layout strategy and sector-coupling on the multi-criteria assessment of energy technology concepts for residential buildings

Energy system transformation needs decentralisation, decarbonisation and sector-coupling. Local energy systems are often customarily designed using heuristics for the technology layout. However, optimisation of the technology layout and operation is considered a powerful tool. Therefore, we investigate the influence of an optimised layout and the coupling of the electricity and heat sector on economic, ecological and technological criteria. The criteria comprise the annual costs, CO2 emissions, and the energy performance in terms of self-sufficiency and self-consumption. We consider typical energy technology concepts with fixed technology combinations for the heat and electricity supply of residential buildings. The optimisation is performed using a Mixed-Integer Linear Programming under the objective of minimal costs. The assessment is done using the Analytic Hierarchy Process. An optimised layout is found to reduce costs at the expense of emissions and energy performance. In comparison to the heuristic layout, especially the storage sizes differ among the layout strategies. Sector-coupling leads to CO2 emission savings and a higher self-consumption of the PV energy produced within the system. The multi-criteria assessment reveals higher performance scores for heuristically laid out concepts under a local sensitivity analysis of the criteria weights.

Cooperation and Fairness in Multi-Agent Reinforcement Learning

Multi-agent systems are trained to maximize shared cost objectives, which typically reflect system-level efficiency. However, in the resource-constrained environments of mobility and transportation systems, efficiency may be achieved at the expense of fairness — certain agents may incur significantly greater costs or lower rewards compared to others. Tasks could be distributed inequitably, leading to some agents receiving an unfair advantage while others incur disproportionately high costs. It is, therefore, important to consider the tradeoffs between efficiency and fairness in such settings. We consider the problem of fair multi-agent navigation for a group of decentralized agents using multi-agent reinforcement learning (MARL). We consider the reciprocal of the coefficient of variation of the distances traveled by different agents as a measure of fairness and investigate whether agents can learn to be fair without significantly sacrificing efficiency (i.e., increasing the total distance traveled). We find that by training agents using min-max fair distance goal assignments along with a reward term that incentivizes fairness as they move towards their goals, the agents (1) learn a fair assignment of goals and (2) achieve almost perfect goal coverage in navigation scenarios using only local observations. For goal coverage scenarios, we find that, on average, the proposed model yields a 14% improvement in efficiency and a 5% improvement in fairness over a baseline model that is trained using random assignments. Furthermore, an average of 21% improvement in fairness can be achieved by the proposed model as compared to a model trained on optimally efficient assignments; this increase in fairness comes at the expense of only a 7% decrease in efficiency. Finally, we extend our method to environments in which agents must complete coverage tasks in prescribed formations and show that it is possible to do so without tailoring the models to specific formation shapes. [Code]

Mechanical model analysis of column-footing joints with combined socket-corrugated pipe connection

The socket connection method is widely used in precast column, particularly in seismic regions. However, reducing the socket-depth to lower costs may lead to shear failure in the socketed part of the columns. To address this issue and achieve cost objectives, a new approach combines shallow sockets with corrugated pipes for column-footing joints. Comparative tests were conducted to investigate failure in columns with socket-corrugated pipe connections (SCPC), shallow sockets (SSC), and cast-in-place (CIP). Furthermore, finite element models were employed to validate the experimental and simplified model results. The findings suggest potential shear failure in shallow socket connections, which can be mitigated by using the combined socket-corrugated pipe method that alters force transmission paths. As the axial load ratio increases, both the ultimate lateral load capacity of the specimen and the local stresses at the column base increase. In addition, the ultimate lateral load capacity of the column and the stress of the connection reinforcement are increased by increasing the strength of the longitudinal reinforcement, consequently amplifying the extent of joint area damage. Finally, a simplified strut-and-tie model of the SCPC, validated against numerical and experimental data, accurately represents force paths, ultimate lateral load capacity and failure modes in socket joints.

Thermal management of sodium-oxygen-hydrogen (Na-O-H) thermochemical water splitting for sustainable hydrogen production

Owing to environmental problems caused by the consumption of fossil fuels and the growing global demand for energy, interests in usage of hydrogen as a green energy carrier has grown. In particular, water is considered a prospect source for green hydrogen production. In this paper, a three-step sodium-oxygen-hydrogen (Na–O–H) thermochemical cycle, which is a newly developed cycle that can operate between 400 and 500 °C is implemented. Also, low number of steps, which results in less system complexity and easier system design is another advantage of the Na–O–H thermochemical cycle. In the first step of this study, a basic model of the three-step pure Na–O–H cycle is modeled in order to produce 1 kg/s of hydrogen. Moreover, output data from basic modeling is used for cycle energy management. Then, heat recovery is performed within the cycle streams to reduce the external energy demand and make the Na–O–H cycle more feasible. To achieve this objective, pinch method, which is an effective method for designing and optimizing plants is used. Several heat exchanger network designs have been developed and compared from different aspects. Finally, a design with the least heat requirement among others is selected and optimized based on total annualized cost objective function to introduce the optimum final design exchanger network. As a result of this work, it is found that using the final optimized design, external heating loads accounted for just 5.8% of the total load, while external cooling loads accounted for 19.3% of the total load. This implies that the Na–O–H thermochemical cycle recovered 74.7% of the energy required for heating and cooling of the streams. The implementation of the proposed heat exchanger network design boosts the overall energy efficiency of the Na–O–H thermochemical cycle. Using final heat recover scheme the overall efficiency of the cycle increased from 45.36% to 52.86%, representing an impressive 7.50% improvement. This significant achievement underscores the pivotal role of this study in developing an effective and optimized model for the Na–O–H thermochemical cycle, which holds great promise for future applications.

Transit Dynamic Operation Optimization Using Combination of Stop-Skipping Strategy and Local Route Optimization

The efficient operation and intelligent upgrading of public transportation can effectively enhance the attractiveness of conventional public transportation. In order to improve the delicacy management level of bus operations, this study designed a new dynamic optimization model for single-line bus operations with the dual optimization objectives of the lowest passenger travel cost and lowest operation cost, using a combination of the strategy of stop-skipping control and local route optimization. Simulated annealing (SA) was introduced into the genetic algorithm (GA) to design a hybrid heuristic algorithm for model solving. The effectiveness of the optimization model and the hybrid algorithm were verified and evaluated by using the No. 115 bus line in Ganzhou City as an example. The results showed that the proposed optimization model had a good usability, which can effectively improve the average vehicle speed, shorten the overall waiting time of passengers, and enhance the operational efficiency of the line. The hybrid algorithm saw significant improvement in terms of the iteration speed and the quality of the optimal solution compared with the conventional genetic algorithm.

Data-driven study/optimization of a solar power and cooling generation system in a transient operation mode and proposing a novel multi-turbine modification concept to reduce the sun's intermittent effect

This study explores the integration of solar energy via parabolic trough collectors (PTC) with the Goswami cycle, focusing on evaluating life cycle costs (LCC) and energy performance in both steady-state and transient conditions. A suitable system configuration of a single-turbine Goswami cycle is designed for integration with PTC. Then, in the system's steady-state, a parametric study is performed based on the change in the design components. Subsequently, a multi-objective optimization method using energy and lifetime cost objectives are defined to identify the best system performance. This optimization technique utilizes artificial neural network (ANN) and the grey wolf optimizer (GWO) algorithm. Accordingly, the transient performance of the system is investigated during its optimal operating point using the TRNSYS software. Suitable controllers are considered for regulating power production and cooling system. Annual transient performance graphs of the systems are obtained. Following these investigations, a novel multi-turbine system is proposed. This configuration aims to enhance system reliability and diminish the reliance on storage devices by optimizing performance. The proper configuration of the five-turbine system with the required controllers is also provided. Hence, the method of dividing the turbine into similar turbines with lower capacity is a new concept proposed in this study. In the optimal state, it is observed that for 100 kW of net power output, 161 kW of effective cooling load can be achieved. At this configuration, the system's LCC amounts to 2.91 M. Moreover, during transient operation, an average power of 90 kW is attainable with a cooling load of 120 kW. The findings indicate that modifying the system from single turbine to five-turbines incurs an additional cost of approximately 9.2 % and reduces power output by 6 %. However, this modification significantly mitigates the transient impact of solar fluctuations.

Study on Multi-Objective Optimization of Construction Project Based on Improved Genetic Algorithm and Particle Swarm Optimization

Construction projects require concurrent consideration of the three major objectives of construction period, cost, and quality. To address the multi-objective optimization issues of construction projects, mathematical models of construction period, quality, and cost are established, respectively, and multi-objective optimization models are constructed for different construction objectives. A hybrid optimization method combining an improved genetic algorithm (GA) with a time-varying mutation rate and a particle swarm algorithm (PSO) is proposed to optimize construction projects, which overcomes the shortcomings of the original GA and improves the global optimality and stability of results. Various construction projects were considered, and different construction objectives were analyzed individually. Finally, an uncertainty analysis is developed for the proposed GA-PSO algorithm and compared with GA and PSO. The results indicate that the proposed hybrid approach outperforms the PSO and GA algorithms in providing a better and more stable multi-objective optimized construction solution, with performance improvements of 4.3–8.5% and volatility reductions of 37.5–64.4%. This provides a reference for the optimal design of wind farms, buildings, and other construction projects.

Exponential qubit reduction in optimization for financial transaction settlement

We extend the qubit-efficient encoding presented in (Tan et al. in Quantum 5:454, 2021) and apply it to instances of the financial transaction settlement problem constructed from data provided by a regulated financial exchange. Our methods are directly applicable to any QUBO problem with linear inequality constraints. Our extension of previously proposed methods consists of a simplification in varying the number of qubits used to encode correlations as well as a new class of variational circuits which incorporate symmetries thereby reducing sampling overhead, improving numerical stability and recovering the expression of the cost objective as a Hermitian observable. We also propose optimality-preserving methods to reduce variance in real-world data and substitute continuous slack variables. We benchmark our methods against standard QAOA for problems consisting of 16 transactions and obtain competitive results. Our newly proposed variational ansatz performs best overall. We demonstrate tackling problems with 128 transactions on real quantum hardware, exceeding previous results bounded by NISQ hardware by almost two orders of magnitude.

A New Solution Technique for Fuzzy Transportation Problem Using Novel Ranking Functions on Heptagonal Fuzzy Numbers: A Case Study of Regional Shipment

This study focuses on the fuzzy transportation problem that manifests as a problem of optimization in an uncertain environment. The idea of fuzziness in the objective of transportation cost is based on real-world decision-making difficulties, and the efficiency is demonstrated using the proposed heptagonal fuzzy number. We propose a new ranking technique to de-fuzzify the Linear heptagonal fuzzy number and the proposed $\alpha$-cut based ranking technique has been compared with the existing ranking technique. In addition, we presented an approach for performing multiplication and division for any two heptagonal fuzzy numbers incorporating an example. Furthermore, we have presented a generalized ranking heptagonal fuzzy method (GRHFM) algorithm to solve the heptagonal fuzzy transportation. This article determines the best transportation cost for vegetable shipment from the markets utilizing heptagonal fuzzy numbers, which further compares the feasible solutions obtained using various strategies for fuzzy transportation problems and provides the result from the algorithm. We have demonstrated the effectiveness of our proposed method through a numerical example of an adopted case study. The conclusions section discusses the suggested GRHFM efficiency, outcomes, decision management and future directions based on the existing transportation study.

Optimal scheduling of integrated energy systems for eco-economic operation

Abstract In the context of rising environmental awareness and the pursuit of sustainable development, it is critical to advance low-carbon economies, especially within the field of energy system management. Our research is dedicated to enhancing the efficiency of Combined Cooling, Heating, and Power (CCHP) systems to meet both cost and environmental objectives. We have devised an optimized CCHP model that employs the particle swarm optimization algorithm to manage complex constraints and improve solution accuracy. Through case studies, we have verified that our model significantly increases the system’s eco-efficiency and operational excellence.

Issues of Improving Expense Tracking and Calculating Prime Cost in Agriculture

Objectives : This article is devoted to the consideration of the issues of cost accounting and cost calculation in agriculture. In addition, the article highlights the issues of the correct distribution of costs by cost items and objects, as well as the correct calculation of the cost of Uzbekistan's products. In addition, Uzbekistan is an agrarian country, so the article presents suggestions and recommendations for improving the accounting of costs and production costs in agriculture. Previous studies in this area conducted in other parts of the world indicate a direct relationship between the indicators of production costs and market indicators; however, based on the features of Uzbekistan, it may be different, given the accounting policy and the specifics of agricultural costs. Methods: A linear regression method to determine the impact of costs and methods of calculating the cost of production was used. Costs are divided into variables and constants. Based on the features of agriculture, simple, advanced, and regulatory methods are used. Each of these methods is associated with variables and regresses individually for each type of agricultural product. Findings: Our results confirm that there are some methods of calculating the cost of production of farms. The results also indicate that the correct determination of the cost of production affects the profitability of these farms. The results obtained indicate that the calculation of the cost of agriculture is an indicator of the profitability of farms in Uzbekistan in the short term. The impact of the change in the cost of agricultural products influences profitability, in the end on profit; as it influences the prices of sales of products in the end affect the financial performance of farms. Novelty: The impact of the cost of agricultural products on the profitability of farms is becoming more important than ever before since the negative externalities of cost changes affect the financial performance of farms. Keywords: Farms, Expenses, Cost, Product cost, Accounting register

Development and application of artificial hydraulic lime for Chinese architectural heritage restorations

Lime is commonly employed in the restoration of historic buildings due to its strength properties, which align well with the demands of architectural heritage conservation. However, recent shortages in high-quality limestone raw materials and the commercial inclusion of finely ground limestone powder in hydrated lime powder have led to significant challenges. These include low initial strength, pronounced shrinkage and cracking, and poor resistance to frost. Although natural hydraulic lime (NHL) offers moderate strength, its high cost presents an obstacle to widespread use in architectural heritage restoration. Three types of artificial hydraulic lime (AHL) — AHL1, AHL2, and AHL3 — were prepared with the objective of low cost and easy scalability. These were formulated by incorporating white cement (WC), blast furnace slag powder (BFSP), heavy calcium carbonate powder (HCCP), and industrial-grade white sugar (WS) into industrial-grade hydrated lime powder (IHLP). To align the setting time of the AHLs with the requirements of traditional builders, WS was incorporated as a retarding agent. The addition of WC, BFSP, and HCCP to IHLP resulted in enhancements over IHLP in several key aspects: compressive strength, pore structure post-hardening, dry shrinkage rate, and frost resistance. This approach will enhance the restoration quality of architectural heritage in China, leading to fewer repeated repairs and significant savings in manpower and material costs. Ultimately, AHL2 was applied in the restoration of the Confucius Temple, proving its efficacy and alignment with traditional construction requirements. This research provides valuable theoretical and practical insights for enhancing the preservation of architectural heritage.

Deploying Value Engineering Strategies for Ameliorating Construction Project Management Performance: A Delphi-SWARA Study Approach

This study was carried out to rank and prioritize the aspects that have been shown to have an impact on the improvement of construction project management (CPM) performance based on value engineering. This analysis was carried out with the Iranian construction industry’s current situation in mind. The respondents and the Delphi panel were chosen from among Iranian managers and project management professionals with a focus on building projects. These professionals had more than 20 years of experience working on international projects, and in addition to their expertise in project management, they also understood the principles of earned value analysis and value engineering. Thus, the components extracted from the literature review were used as the basis for designing a structured interview based on 39 important effective components previously determined as identified factors. The questionnaires were distributed among the experts, and the returned questionnaires were analysed using the SWARA technique to rank the weight and importance of the factors. Using the SWARA method, the opinions of the expert panel members consisting of 20 engineers and a semi-structured questionnaire was used to gather expertise in project management and managers, with a focus on Iranian CPM. As shown by the results, stakeholder management, time value of money, and worth, allocated the first to the third rank to themselves with respective weights of 0.104, 0.103, and 0.087. The sub-criteria of cost objective, function objective, and value objective were in the 37th to 39th ranks with respective weights of 0.00050, 0.00033, and 0.00021.

Optimal distribution of emergency resources to accidents for pre-rescue in chemical industrial parks

Medical and fire resources are scheduled to chemical industrial parks which have caused mass casualties due to accidents. However, most of models of emergency logistics do not account for pre-rescue before large-scale rescue which may increase the casualty rate because of the long emergency response time and distance. This paper presents a mathematical model proposed for optimal distribution of emergency resources for rescuing the trapped victims who are highly likely to be seriously injured or even killed before the arrival of ambulances and fire engines. Due to the different types and risks of accidents at various locations have different impacts on people, we consider the detailed characteristics of accident scenarios and maximum tolerance time for the trapped victims to determine the priority of rescue sites. In addition, this paper proposes two indices of pavement resistance coefficient and obstacle to reflect the environment after accidents, which are not involved in other models of emergency resources distribution. Based on these, a penalty cost objective function is established to maximize the rescue rate. The optimal delivery route which reflects the distribution process is planned for the robot that will save more casualties. Two simulations and a real accident case study were conducted by minimizing the objective function, and the numerical results showed the reliability and effectiveness of the presented model for making optimal decisions for emergency distribution to accidents in chemical industrial parks.

Multidisciplinary Design Optimization of Active Debris Removal Mission via Electric Propulsion

A design optimization framework for an active orbital debris removal mission to minimize the life cycle cost and mission time is presented. The general concept of operations consists of spacecraft capable of deorbiting 22 identical high-priority debris from sun-synchronous orbit via continuous low thrust from electrostatic thrusters. This modular subsystem framework is proposed and investigated with a fractional factorial design of experiments approach as preliminary analysis in order to inform effects of design variables as well as identify the scope of possible design and output space. The single-objective optimization minimizes the life cycle cost of the mission through a heuristic-based genetic algorithm, resulting in a 4.12-year, $354 million mission using 5.34 kW xenon Hall thrusters. Pareto fronts generated by a multiobjective weighted-sum genetic algorithm are presented to highlight the cost and time objective space for both a nominal and expensive xenon price per kilogram to show design space fluctuations with xenon price volatility. High-power 8 kW xenon Hall thrusters are proven to be in closest proximity to the utopia of the multiobjective design space with a cost of $687 million and mission time of 1 year. High-power krypton Hall thrusters prove to offer a comparable alternative to xenon systems.

Forecast Horizon of Dynamic Lot Sizing Problem with Perishable Inventory and Multiple Chain Stores: Shipping and Stockout Cost

Perishable products are very common, but managing inventory of perishable products can be very challenging for firms, especially in distribution systems, including multiple chain stores. In this environment, we consider a dynamic lot-sizing problem faced by a distribution center that dis-patches a single perishable product to multiple chain stores. Demand cannot be backlogged, but it does not have to be satisfied; unsatisfied demand means stockout (lost sale). The first step is to transform the total profit function into a special total cost function. Our next step is to explore the properties of the optimal solution and use them to formulate a dynamic programming algorithm to solve the problem. Furthermore, we establish forecast and decision horizon results, which help the operation manager to decide the precise forecast horizon in a rolling decision-making process. Based on the model setting and the methods of dynamic programming, we obtained two interesting findings: (1) the maximized profit objective function is equivalent to the minimized cost objective function, and (2) the famous zero inventory property conditionally holds in the inventory management of perishable products. On an extensive test bed, useful insights were obtained on the impact of the lifetime of the product and cost parameters on the total cost and length of the forecast horizon. Thus, the contributions of this study are as follows: (1) we explore two structure policies in an optimal solution to devise efficient algorithms to reduce computational complexity; (2) we provide a sufficient condition for forecasting and decision horizons; and (3) we determine that, for a given fixed cost, the median forecast horizon first increases with the lifetime of the product and stockout cost and then remains invariable when it reaches a certain level.

Methodological aspects of analysis of production costs of an industrial enterprise

The article is aimed at substantiating the methodological and organisational provisions of the analysis of costs of production of industrial products and developing recommendations for their use at industrial enterprises to improve their competitiveness. The article substantiates that in order to achieve efficiency and sustainability of production processes, it is important for industrial enterprises to systematically improve their cost management strategies. The theoretical provisions of cost analysis are substantiated. Approaches to organising and conducting cost analysis are systematised. A conceptual model for analysing production costs of an industrial enterprise has been developed, which includes the purpose of production cost analysis, its tasks, subjects, objects, principles, and methodological tools. The priority tasks of analysing the production costs of an industrial enterprise include: efficiency of the production process; determination of the cost of production; planning and control of budgets; and managerial decision-making. It is substantiated that the costs of production of industrial products are closely interrelated with some indicators of production efficiency, such as: indicators of profitability, liquidity, financial stability of enterprise. The analysis of production efficiency indicators is carried out on the example of the activities of the production and trading enterprise "PTC "ECMI" LLC. To analyse liquidity, the following indicators are used: total liquidity ratio, intermediate liquidity ratio and absolute liquidity ratio. The following indicators were used to analyse financial stability: autonomy ratio, financial stability ratio and financial risk ratio. To analyse profitability, the following indicators were used: return on assets (total), return on operating expenses, and return on sales. In order to assess changes in the structure of production costs, the article analyses the cost by cost elements, estimates their total volume, structural changes, and traces the dynamics of changes in the period from 2020 to 2022. The results of the analysis show that the company has optimised its costs, which has led to their reduction and increase in profitability. It is substantiated that an efficiently established analytical system enables the enterprise to timely monitor changes in production and prevent cost overruns, theft and inefficient use of raw materials, which is especially relevant during the period of military aggression in our country.