Cost Control Method Research Articles

Challenges Inherent in Controlling Prickly Pear Species; a Global Review of the Properties of Opuntia stricta, Opuntia ficus-indica and Opuntia monacantha.

Opuntia species (prickly pear) were deliberately introduced to many countries around the world for fruit, cochineal dye production, living fencing or as ornamentals. They are now some of the world's most significant weeds, particularly in regions with warm and or dry climates, as they pose threats to economic and environmental assets. In addition, they can cause considerable health issues for humans and animals. Opuntia spp. have prolific reproduction abilities, being able to reproduce both vegetatively and by seed. They have generalist pollination and dispersal requirements, which promotes their establishment and spread. Opuntia stricta, O. monacantha and O. ficus-indica are the most globally widespread of the Opuntia spp. In many countries, biological control agents, particularly the cactus moth (Cactoblastis cactorum) and various cochineal insects from the Dactylopius genus, have successfully reduced land-scape scale populations. On a smaller scale, controlling these weeds by either injecting or spraying the cladodes with herbicides can provide effective control. Care must be taken during herbicide treatments as any untreated areas will regenerate. While biological control is the most cost and time effective control method for landscape-scale infestations, further research into the combined efficacy of herbicides, fire, grubbing and pre-burial techniques would be beneficial for land managers to control small-scale and establishing populations. It would also be useful to have greater knowledge of the potential seedbank longevity and seed ecology of these species so that integrated management strategies can be developed to not only deal with initial populations but also the subsequent seedling regrowth.

LMI-Based Delayed Output Feedback Controller Design for a Class of Fractional-Order Neutral-Type Delay Systems Using Guaranteed Cost Control Approach

In this research work, we deal with the stabilization of uncertain fractional-order neutral systems with delayed input. To tackle this problem, the guaranteed cost control method is considered. The purpose is to design a proportional–differential output feedback controller to obtain a satisfactory performance. The stability of the overall system is described in terms of matrix inequalities, and the corresponding analysis is performed in the perspective of Lyapunov’s theory. Two application examples verify the analytic findings.

The Analysis of Environmental Cost Control of Manufacturing Enterprises Using Deep Learning Optimization Algorithm and Internet of Things

Under the background of the Internet of things (IoT), the problems between the actual production and the environment are also prominent. The environmental cost control in the production process of manufacturing enterprises are discussed to reduce the environmental cost and promote the improvement of production efficiency. First, the environmental cost under the background of IoT is analyzed. Also, the environmental cost control methods in the production process of traditional manufacturing enterprises are investigated. Second, based on the principle of traditional genetic algorithm, the fast-nondominated sorting genetic algorithm (NSGA-II) of multiobjective genetic algorithm is introduced to complete the optimization of BP neural network (BPNN) algorithm in deep learning (DL), and the multiobjective GA optimization BPNN model is established. Finally, the multiobjective GA algorithm is used to empirically analyze the environmental cost control capability of a paper-making enterprise. It is compared with enterprises with excellent and poor environmental cost control capabilities in the same industry to find out secondary indexes. The results show that environmental costs have long-term and economic characteristics. The global search ability of BPNN optimized by multiobjective GA is improved, and the local optimal dilemma is avoided. Through empirical analysis, it is found that the comprehensive capability of the environmental cost control of the enterprise is better, scored 79 or more, and the indexes of insufficient development and advantages are obtained. As IoT rapidly develops, it is necessary to further improve the ability of enterprises in environmental cost management, which is very important to promote the development of enterprises and enhance their core competitiveness. It is hoped that this investigation can provide certain reference significance for improving the environmental cost management capability of enterprises, increasing production efficiency, and reducing environmental costs.

Environmental Cost Control of Manufacturing Enterprises via Machine Learning under Data Warehouse

Environmental cost refers to the cost paid by enterprises to reduce environmental pollution and resource depletion in production and operation. To help enterprises reduce environmental costs, a manufacturing environmental cost control algorithm based on machine learning is proposed. The probabilistic neural network is used to classify the current environmental cost control level of different manufacturing enterprises. Then, the particle swarm optimization (PSO) algorithm is improved to build a multi-objective backbone PSO algorithm for multi-objective decision-making, which is used in the selection of environmental cost control methods. The experimental results show that there is a strong correlation between the original data classification and the proposed probabilistic neural network, and the correlation reaches 96.1%. PSO performance test results show that the algorithm has the best performance, the best stability, and the shortest time needed to find the optimal solution set when the initial particle number is 140 and the number of iterations is 60. Based on the comprehensive experimental results, the following conclusions are drawn. Enterprises should strengthen collaboration and cooperation with customers, suppliers, and waste-profiting enterprises, so as to well control environmental costs. To sum up, the proposed model provides some references for the adoption of machine learning in environmental cost control of manufacturing enterprises.

Observer-based guaranteed cost control for networked control systems with packet dropout and nonlinear disturbance

In this article, the observer-based guaranteed cost control method is investigated for the networked control systems (NCSs) with packet dropout and nonlinear disturbance involving event-triggered mechanism (ETM). The packet dropout processes, which appear in both the sensor-to-controller and the controller-to-actuator links, are represented by two mutually independent Bernoulli distributions, and the nonlinear disturbance is supposed to satisfy the Lipschitz condition. Since the systems states are unmeasurable, a state observer is designed to estimate the values of system state. The sufficient condition for the stability of the closed-loop system is addressed by applying the Lyapunov theorem, and a guaranteed cost controller is obtained based on the stability condition with the satisfaction of some given specified value of cost function. Furthermore, the controller design issue can be formulated as a convex optimization problem that is addressed by the linear matrix inequality (LMI) technique using the cone complementarity linearization algorithm. Finally, the availability of above method is proved by a simulation example.

Overlapping Decentralized Guaranteed Cost Control Method for Discrete-Time Systems of Building Structures with Uncertain Parameters under Earthquake Action

This paper proposes a discrete-time overlapping decentralized guaranteed cost control algorithm via state feedback. Based on the inclusion principle, a structure system is decoupled into multiple subsystems, and the discretized subsystems’ controllers are designed by using the guaranteed cost control algorithm. Finally, the subsystems’ controllers are contracted into overlapping controllers of the original system, and the design of the control system is completed. This control method is introduced to the civil engineering field to solve the seismic control problem for discrete-time systems of building structures with uncertain parameters. The numerical analysis of the nine-story building structure model with uncertain parameters is carried out. The results show that the proposed control method can still effectively reduce the seismic response of discrete-time system when the structural parameters are uncertain, and the control effect is similar to the centralized control strategy; at the same time, the sampling rate and data transmission speed of the system are improved to ensure the reliability of the system. It illustrates the feasibility and effectiveness of the presented approach.

Research on Supply Chain Cost Control Method of Intelligent Manufacturing Enterprise Based on Improved IQC Algorithm

In order to improve the cost control effect of the supply chain of intelligent manufacturing enterprises, this paper improves the IQC algorithm. In order to overcome the problem of multitype data processing in the supply chain, this paper describes the high-frequency NCS as a delta arithmetic system with input time-varying delay, high-frequency constraints, and actuator saturation. Moreover, through a double closed-loop feedback configuration, this paper combines the loop structure of the supply chain to analyze the stability of the actuator saturated high-frequency NCS and combines the experiments to verify the reliability of the improved algorithm. In addition, on the basis of the improved algorithm, this paper constructs an intelligent supply chain cost control system, evaluates its functional modules, sets up the system architecture, and analyzes the performance of the system in combination with experimental research. The experimental research results show that the supply chain cost control method of intelligent manufacturing enterprises based on the improved IQC algorithm proposed in this paper has good results and meets the operational needs of intelligent manufacturing enterprises.

Optimal guaranteed cost intermittent control to the efficient movement of freight trains

Freight train system is under constant pressure to optimize operational efficiency, a factor that has led the sector to spearhead many new technologies. To address this, the optimal guaranteed cost intermittent cruise control of freight train system with time-varying uncertain parameters is investigated. In particular, the guaranteed cost intermittent control approach is proposed, which can achieve both advantages of guaranteed cost control and intermittent control methods. In addition, the acceptable conditions for the guaranteed cost control laws are weakened to insure that the proposed strategy is suitable for the application in freight train systems. Moreover, to promote the optimal control design in the freight train system, the proposed optimal guaranteed cost intermittent control for the nonlinear system subjected to norm bounded parametric uncertainties is addressed. The results of numerical experiments are presented to ascertain the effectiveness of the proposed freight train control methodology and to compare it to prior methods.

Analysis of Cost Structures and Cost Control Strategies of Airlines: An Empirical Study on A Hypothetical Airline Company

In the context of controlling costs in the airline industry, this study aims to determine empirically until which available seat mile (ASM) should an airline continue its activities based on an analysis of the costs and revenues incurred relating to these ASM. The study is based on a hypothetical company and the data used indicated that the company has reached profitability between certain efficiency levels (ASM). The results also showed that the airline can still continue its activities at efficiency levels where it is making a loss but only in situations where its total income exceeds its total variable costs (creating positive added value). In the study, it is pointed out that companies in the airline industry, which are under the pressure of intense competition and uncontrollable factors, can still survive and maintain their competitive power through the cost control methods they can apply.

Internet of things enabled the control and optimization of supply chain cost for unmanned convenience stores

In the context of “new retail“, various new retail models have appeared in the market one after another, such as unmanned retail, and unmanned convenience stores, which are based on a model in the unmanned retail industry. At present, unmanned convenience stores still have to deal with the challenges in operations, including difficulty in landing site selection, immature technology, and low public awareness. The most important issue is the supply chain cost control. It seems that operating methods that reduce costs through technical support cannot effectively control their costs. This article not only combines cost analysis methods such as flexible budgeting method to calculate and analyze the supply chain cost of unmanned convenience stores, but also proposes cost control methods from the perspective of supply chain management, and gives suggestions on the future development direction of unmanned convenience stores.

Research on Main Factors Affecting Project Cost and Cost Control Methods

Research on Main Factors Affecting Project Cost and Cost Control Methods

Optimal reset law design based on guaranteed cost control method for Lipschitz nonlinear systems

AbstractThis article proposes a systematic approach for optimal reset law design of a class of nonlinear systems. By using the guaranteed cost control method, sufficient conditions for the design of optimal reset law are derived in terms of linear matrix inequalities. In an offline design procedure, the reset law is computed that minimizes the upper bound of a quadratic cost function. The proposed method can be implemented for real‐time applications even with small sampling time. The simulation results verify the efficacy and effectiveness of the proposed theoretical results.

Selective assembly efficiency of precision optical systems based on products virtual assembly in the production organization at a mechanical engineering enterprise

In connection with the need to ensure high accuracy of the product, the task of accurate assembly arises. One of the options for optimizing the costs of the process of installing parts in a case is selective assembly. The economic effi ciency and cost control methods for the selective assembly of precision optical systems based on virtual assembly are analyzed.

Automatic assembly cost control method of Industry 4.0 production line based on deep reinforcement learning

Automatic assembly cost control method of Industry 4.0 production line based on deep reinforcement learning

Automatic assembly cost control method of Industry 4.0 production line based on deep reinforcement learning

Automatic assembly cost control method of Industry 4.0 production line based on deep reinforcement learning

Research on civil engineering management and engineering cost control

In order to improve the level of civil engineering construction and reduce the cost of construction expenditures, research on civil engineering management and engineering cost control methods is carried out. Comprehensively consider the risk factors that may increase the additional expenditures of the construction of the construction site, and manage the sub-items of the basic cost of civil engineering in a targeted manner; control the cost of the civil engineering project in each construction stage according to the three steps before, during and after the event expenditure. Choose a real project as the experimental project, and control the project construction expenditure with reference to the designed control method. After practice, it is proved that this method can realize the control of the actual expenditure of the expenditure cost project during the construction process to ensure that the project is in the existing High-quality completion within the scope of funds.

Shipping Company Decision Analysis

This article analyzes the current state and trends of the shipping industry, the cost control methods of some large shipping companies, and helps a company consider whether it is more profitable to spend money on repairing an old ship or just operating a new one.

Logistics cost control in food processing enterprises based on TD-ABC

In order to improve the logistics operation of food processing enterprises and control the logistics cost, this paper starts from the analysis of the logistics operation process and characteristics of enterprises, analyzes the logistics cost classification and composition of food processing enterprises, puts forward the basic process of applying TD-ABC to control the logistics cost, constructs the logistics cost accounting model of food processing enterprises based on TD-ABC, and puts forward relevant logistics cost control methods including the design of logistics cost control system, the determination of logistics cost control points and specific implementation steps. The typical food processing enterprises are selected for the empirical study of logistics cost accounting and controlling. By comparing the cost accounting results of TD-ABC and ABC, the result shows that TD-ABC (Time-Driven Activity-Based Costing) method can accurately grasp the logistics cost composition, identify inefficient logistics operations and non-value-added operations, and analyze the efficiency of various logistics departments, verify the feasibility and effectiveness of logistics cost accounting model and control method system, and provide some advice for logistics cost control and logistics process transformation of food processing enterprises.

Method of Assessing the Risk of Implementing Railway Investments in Terms of the Cost of Their Implementation

Exceeding the approved budget is often an integral part of the implementation of construction projects, especially those where unforeseen threats may occur. Therefore, each construction investment should contain elements of risk forecasting, mainly in terms of the cost of its implementation. Only a small number of institutions apply effective cost control methods, taking into account the specifics of a given industry. Especially small construction companies that participate in the structure of the implementation of large construction projects as subcontractors. The article presents a method by which it is possible to determine, with certain probability, the final cost of railway construction investments carried out in Poland. The method was based on a reliable database of risk factors published in sources. In this article, the main presumptions of the original method are presented, which take into account the impact of potential, previously recognized, risks specific to railway investments, and enable project managers to relate them to the conditions where the implementation of a specific object is planned. The authors assumed that such a relatively simple method, supported by a suitable computational program, would encourage teams that plan to implement railway projects to use it and increase the credibility of their schedules.

Discussion on the Process and Method of Cost Control in Mechanical and Electrical Installation Engineering

Discussion on the Process and Method of Cost Control in Mechanical and Electrical Installation Engineering