Optimal pricing decision model based on activity-based costing

In order to find out the applicability of the optimal pricing decision model based on conventional cost behavior model after activity based costing has given strong shock to the conventional cost behavior model and its assumptions, detailed analyses have been made using the activity based cost behavior and cost volume profit analysis model, and it is concluded from these analyses that the theory behind the construction of optimal pricing decision model is still tenable under activity based costing, but the conventional optimal pricing decision model must be modified as appropriate to the activity based costing based cost behavior model and cost volume profit analysis model, and an optimal pricing decision model is really a product pricing decision model constructed by following the economic principle of maximizing profit.

Modeling Anthrax with Optimal Control and Cost Effectiveness Analysis

Anthrax is an infection caused by bacteria and it affects both human and animal populations. The disease can be categorized under zoonotic diseases and humans can contract infections through contact with infected animals, ingest contaminated dairy and animal products. In this paper, we developed a mathematical model for anthrax transmission dynamics in both human and animal populations with optimal control. The qualitative solution of the model behaviour was analyzed by determining Rhv, equilibrium points and sensitivity analysis. A vaccination class was incorporated into the model with waning immunity. Local and global stability of the model’s equilibria was found to be locally asymptotically stable whenever Rhv Rhv. It was revealed that reducing animal and human interaction rate, would decrease Rhv. We extended the model to optimal control in order to find the best control strategy in reducing anthrax infections. It showed that the effective strategy in combating the anthrax epidemics is vaccination of animals and prevention of humans.

Optimal control strategy of institutional investor's execution cost

Optimal control of multi-assets liquidation in view of volatility risk was studied. The analytical solution of optimal strategy was achieved with the calculus of variation. Numerical examples and graphical illustrations were also given. The conclusion shows that the optimal strategy is the linear combination of time's hyperbolic sine and hyperbolic cosine. The investor's attitude towards risk can influence the optimal strategy. In order to avoid the uncertainty of the execution cost, the investor with high risk aversion liquidates assets rapidly in the early period. The decrease of liquidation loss is at the cost of the increase of the volatility level.

Game theory approach to optimal capital cost allocation in pollution control

This paper tries to integrate game theory, a very useful tool to resolve conflict phenomena, with optimal capital cost allocation issue in total emission control. First the necessity of allocating optimal capital costs fairly and reasonably among polluters in total emission control was analyzed. Then the possibility of applying game theory to the issue of the optimal capital cost allocation was expounded. Next the cooperative N person game model of the optimal capital cost allocation and its solution ways including method based on Shapley value, least core method, weak least core methods, proportional least core method, CGA method, MCRS method and so on were delineated. Finally through application of these methods it was concluded that to apply game theory in the optimal capital cost allocation issue is helpful to implement the total emission control planning schemes successfully, to control pollution effectively, and to ensure sustainable development.

Collaborative optimization model of renewable energy development considering peak shaving costs of various flexibility resources

China has set carbon emission goals for 2030 and 2060.Renewable energy sources,primarily wind and photovoltaic power,are being considered as the future of power generation.The major limitation to the development of new energies is the limited flexibility of regulations on power system resources,resulting in insufficient consumption capacity.Thus,the flexible resource costs for peak shaving as well as the reasonable coordinated development and operation optimization of regional renewable energy need to be considered.In this study,a renewable energy development layout configuration analysis method was established by considering the composite cost of a power system,comprehensively analyzing the potential of various flexibility regulation resources for the power system and its composite peak shaving cost,and combining renewable energy output characteristics,load forecasting,grid development,and other factors.For the optimization of various flexible resource utilization methods,a peak shaving cost estimation method from the perspective of the entire power system was established by combining the on-grid electricity prices and operating costs of different power sources.A collaborative optimization model of power system operation that aims at the lowest peak shaving cost and satisfies the constraints of operation,safety,and environmental protection was proposed.Finally,a certain area of Gansu Province was used as an example to perform detailed analysis and calculation,which demonstrated that the model has an optimal effect.This model can provide an analysis method for regional renewable energy development layout configurations and system optimization operations.

Green Investment Cost Optimization Model in the Supply Chain

The objective of this study is to develop a model that determines the optimal points for investment in green management by defining a mathematical relationship between carbon trading profits and investments in green management using a company’s supply chain information. To formulate this model, we first define and analyze a green supply chain in a multi-dimensional and quantitative manner. The green investment alternatives considering in our model are as follows: 1) purchasing eco-friendly raw materials that cost more than conventional raw materials but whose use in production results in lower CO2 emissions;2) replacing current facilities with new eco-friendly facilities that have the capability to reduce CO2 emissions;and 3) changing modes of transport from less eco-friendly to more eco-friendly modes. We propose a green investment cost optimization (GICO) model that enables us to determine the optimal investment points. The proposed GICO model can support decision-making processes in green supply chain management environments.

A Heuristics-Based Cost Model for Scientic Workow Scheduling in Clou

Scientic Workow Applications(SWFAs)can deliver collaborative tools useful to researchers in executing large and complex scientic processes.Particularly,Scientic Workow Scheduling(SWFS)accelerates the computational procedures between the available computational resources and the dependent workow jobs based on the researchers’requirements.However,cost optimization is one of the SWFS challenges in handling massive and complicated tasks and requires determining an approximate(near-optimal)solution within polynomial computational time.Motivated by this,current work proposes a novel SWFS cost optimization model effective in solving this challenge.The proposed model contains three main stages:(i)scientic workow application,(ii)targeted computational environment,and(iii)cost optimization criteria.The model has been used to optimize completion time(makespan)and overall computational cost of SWFS in cloud computing for all considered scenarios in this research context.This will ultimately reduce the cost for service consumers.At the same time,reducing the cost has a positive impact on the protability of service providers towards utilizing all computational resources to achieve a competitive advantage over other cloud service providers.To evaluate the effectiveness of this proposed model,an empirical comparison was conducted by employing three core types of heuristic approaches,including Single-based(i.e.,Genetic Algorithm(GA),Particle Swarm Optimization(PSO),and Invasive Weed Optimization(IWO)),Hybrid-based(i.e.,Hybrid-based Heuristics Algorithms(HIWO)),and Hyper-based(i.e.,Dynamic Hyper-Heuristic Algorithm(DHHA)).Additionally,a simulation-based implementation was used for SIPHT SWFA by considering three different sizes of datasets.The proposed model provides an efcient platform to optimally schedule workow tasks by handing data-intensiveness and computational-intensiveness of SWFAs.The results reveal that the proposed cost optimization model attained an optimal Job completion time(makespan)and total computational cost for small and large sizes of the considered dataset.In contrast,hybrid and hyper-based approaches consistently achieved better results for the medium-sized dataset.

Charging Ahead with E-Buses: Benefits, Costs, and Transition Roadmap

This paper reaches a recommendation for the 10-year e-bus transition roadmap for New York City. The lifecycle model of emission reduction demonstrates the ecological and financial impacts of a complete transition from the current diesel bus fleet to an all-electric bus fleet in New York City by 2033. This study focuses on the NOx pollution, which is the highest among all major cities by Environmental Protection Agency (EPA) and greenhouse gases (GHG) with annual emissions of over five million tons. Our model predicts that switching to an all-electric bus fleet will cut GHG emissions by over 390,000 tons and NOx emissions by over 1300 tons annually, in addition to other pollutants such as VOCs and PM 2.5. yielding an annual economic benefit of over 75.94 million USD. This aligns with the city mayor office’s initiative of achieving total carbon neutrality. We further model an optimized transition roadmap that balances ecological and long-term benefits against the costs of the transition, emphasizing feasibility and alignment with the natural replacement cycle of existing buses, ensuring a steady budgeting pattern to minimize interruptions and resistance. Finally, we advocate for collaboration between government agencies, public transportation authorities, and private sectors, including electric buses and charging facility manufacturers, which is essential for fostering innovation and reducing the costs associated with the transition to e-buses.

A software cost model with maintenance and risk costs for safety-critical systems

According to the consequences of software failures, software faults remaining in safety-critical systems can be classified into two sets： common faults and fatal faults. Common faults cause slight loss when they are activated. A fatal fault can lead to significant loss, and even damage the safety-crltical system entirely when it is activated. A software reliability growth model for safety-critical systems is developed based on G - 0 model. And a software cost model is proposed too. The cost model considers maintenance and risk costs due to software failures. The optimal release policies are discussed to minimize the total software cost. A numerical exampie is provided to illustrate how to use the results we obtained.

碳减排“时-空-效-益”统筹理论:IV.收益统筹

面对巨大的气候变化损害以及高额的减排行动成本,如何在多时期、多主体框架下统筹经济发展与减排行动,协同推进发展与减排,成为全球气候治理中的关键议题。作为碳减排“时-空-效-益”统筹理论系列论文的组成部分,构建了收益统筹理论。该理论在综合集成时间统筹、空间统筹、效率统筹相关理论与方法基础上,以最优经济增长为核心,将碳排放问题引入长期宏观经济分析中,深入探析经济发展与减排行动二者间的复杂关系,构建了经济-气候双向耦合的中国气候变化综合评估模型,并设计了多区跨期优化福利函数,实现发展与减排的统筹。基于碳减排“时-空-效-益”统筹理论,应用中国气候变化综合评估模型C3IAM,明确了未来全球及各区域最优减排路径,并提出了各国及国家内部减排任务分配方案,科学回答了碳减排系统工程的减多少、何时减、谁来减、如何减、何效果等关键问题,为统筹发展与减排提供方法基础。

Enterprise resource planning implementation decision & optimization models

To study the uncertain optimization problems on implementation schedule, time-cost trade-off and quality in enterprise resource planning （ERP） implementation, combined with program evaluation and review technique （PERT）, some optimization models are proposed, which include the implementation schedule model, the timecost trade-off model, the quality model, and the implementation time-cost-quality synthetic optimization model. A PERT-embedded genetic algorithm （GA） based on stochastic simulation technique is introduced to the optimization models solution. Finally, an example is presented to show that the models and algorithm are reasonable and effective, which can offer a reliable quantitative decision method for ERP implementation.

A system-wide approach to minimize the operational cost of bench production in open-cast mining operations

The production cycle of open-cast coal mines generally in eludes drilling, blasting, loading, hauling and coal preparation activities. Individual optimization of these activities does not mean that the whole system is optimized. This paper proposes a cost model considering all activities in mining cycle and system-wide approach to minimize the total mining cost of bench production. Since the fragmentation size and blast-hole diameter are linked to all activities of mining system, they are considered as decision variables in the problem form ul at io n. The operatio n costs are then minimized by using the evolutionary algorithm. Moreover, the impact of the change in the explosive price, and the hourly unit cost of equipment on total mining cost is quantified by sensitivity analysis. A case study is implemented to demonstrate the developed model.

Multi-objective optimization scheduling for new energy power system considering energy storage participation

For the low utilization rate of photovoltaic power generation,taking a new energy power system constisting of concentrating solar power(CSP),photovoltaic power(PP)and battery energy storage system as an example,a multi-objective optimization scheduling strategy considering energy storage participation is proposed.Firstly,the new energy power system model is established,and the PP scenario generation and reduction frame based on the autoregressive moving average model and Kantorovich-distance is proposed.Then,based on the optimization goal of the system operation cost minimization and the PP output power consumption maximization,the multi-objective optimization scheduling model is established.Finally,the simulation results show that introducing energy storage into the system can effectively reduce the system operation cost and improve the utilization efficiency of PP.

Analysis of Listeriosis Transmission Dynamics with Optimal Control

Listeriosis is an illness caused by the germ Listeria monocytogenes. Generally, humans are infected with listeriosis after eating contaminated food. Listeriosis mostly affects people with weakened immune systems, pregnant women and newborns. In this paper, a model describing the dynamics of Listeriosis is developed and analysed using ordinary differential equations. The model was analysed both quantitatively and qualitatively for its local and global stability, basic reproductive number and parameter contributions to the basic reproductive number to understand the impact of each parameter on the disease spread. The Listeriosis model has been extended to include time dependent control variables such as treatment of both humans and animals, vaccination and education of humans. Pontryagin’s Maximum Principle was introduced to obtain the best optimal control strategies required for curbing Listeriosis infections. Numerical simulation was performed and the results displayed graphically and discussed. Cost effectiveness analysis was conducted using the intervention averted ratio (IAR) concepts and it was revealed that the most effective intervention strategy is the treatment of infected humans and animals.

OSS Effort Expense Optimization Based on Wiener Process Model and GA

Various open source software are managed by using several bug tracking systems. In particular, the open source software extends to the cloud service and edge computing. Recently, OSF Edge Computing Group is launched by OpenStack. There are big data behind the internet services such as cloud and edge computing. Then, it is important to consider the impact of big data in order to assess the reliability of open source software. Various optimal software release problems have been proposed by specific researchers. In the typical optimal software release problems, the cost parameters are defined as the known parameter. However, it is difficult to decide the cost parameter because of the uncertainty. The purpose of our research is to estimate the effort parameters included in our models. In this paper, we propose an estimation method of effort parameter by using the genetic algorithm. Then, we show the estimation method in section 3. Moreover, we analyze actual data to show numerical examples for the estimation method of effort parameter. As the research results, we found that the OSS managers would be able to comprehend the human resources required before the OSS project in advance by using our method.

An Algorithm of 0-1 Knapsack Problem Based on Economic Model

In order to optimize the knapsack problem further, this paper proposes an innovative model based on dynamic expectation efficiency, and establishes a new optimization algorithm of 0-1 knapsack problem after analysis and research. Through analyzing the study of 30 groups of 0-1 knapsack problem from discrete coefficient of the data, we can find that dynamic expectation model can solve the following two types of knapsack problem. Compared to artificial glowworm swam algorithm, the convergence speed of this algorithm is ten times as fast as that of artificial glowworm swam algorithm, and the storage space of this algorithm is one quarter that of artificial glowworm swam algorithm. To sum up, it can be widely used in practical problems.

A New Searching Strategy for the Lost Plane Based on RBF Neural Network Model and Global Optimization Model

In this paper, we construct two models for the searching task for a lost plane. Model 1 determines the searching area. We predict the trajectory of floats generated after the disintegration of the plane by using RBF neural network model, and then determine the searching area according to the trajectory. With the pass of time, the searching area will also be constantly moving along the trajectory. Model 2 develops a maritime search plan to achieve the purpose of completing the search in the shortest time. We optimize the searching time and transform the problem into the 0-1 knapsack problem. Solving this problem by improved genetic algorithm, we can get the shortest searching time and the best choice for the search power.

Optimal Dividend-Equity Issuance Strategy in a Dual Model with Fixed and Proportional Transaction Costs

In this paper, we consider the problem of optimal dividend payout and equity issuance for a company whose liquid asset is modeled by the dual of classical risk model with diffusion. We assume that there exist both proportional and fixed transaction costs when issuing new equity. Our objective is to maximize the expected cumulative present value of the dividend payout minus the equity issuance until the time of bankruptcy,which is defined as the first time when the company＇s capital reserve falls below zero. The solution to the mixed impulse-singular control problem relies on two auxiliary subproblems： one is the classical dividend problem without equity issuance, and the other one assumes that the company never goes bankrupt by equity issuance.We first provide closed-form expressions of the value functions and the optimal strategies for both auxiliary subproblems. We then identify the solution to the original problem with either of the auxiliary problems. Our results show that the optimal strategy should either allow for bankruptcy or keep the company＇s reserve above zero by issuing new equity, depending on the model＇s parameters. We also present some economic interpretations and sensitivity analysis for our results by theoretical analysis and numerical examples.

面向可靠性-费用优化的网格任务调度模型及算法研究

网格资源的异构性、动态性等特征使得网格任务调度仍面临着诸多问题。针对传统可靠性评估模型仅考虑资源失效的问题,在考虑本地任务会抢占网格任务执行资源的情况下,引入任务执行延期失效,从而建立了一种新的网格资源可靠性评估模型。该模型使用随机服务系统理论建模网格资源的动态负载压力,给出了任务在资源上的执行可靠性的计算方法及证明。基于建立的网格资源可靠性模型,建立了面向可靠性和费用的多目标任务优化调度模型,以获得最大化任务执行可靠性、最小化任务执行费用的任务调度策略。针对该NP问题,采用化学反应优化算法对该优化问题进行求解,并给出了算法4种操作的具体实施方法。仿真实验表明,所提出的可靠性评估模型更符合真实的网格系统,与遗传算法、粒子群算法相比,化学反应优化算法能更好地解决可靠性-费用双目标优化的网格任务调度问题。

基于R-Tree的空间查询代价模型研究

本文对基于 R- Tree的空间查询代价模型进行了探讨 ,分析了 Y.Theodoridis等提出的矩形密度模型〔2 ,3〕,利用其结果提出了代价估计的概率模型 。