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This study focuses on the theoretical analysis of the application of modeling and mathematical, particularly linear programming, in managerial processes within the agro-industrial complex. The authors have dedicated themselves specifically to defining planning processes, classifying models, and organizing production structures, including vertical and horizontal structures. Despite the fact that the benefits of linear programming have been proven, it is still not widely accepted and applied in our region. The reasons for this can be found in the relative complexity of the process, both in creating logical and mathematical models, and in interpreting results. Although there are specialized software and add-ons such as Solver in MS Excel, they are still not accessible enough to a wider range of users in the agro-industrial sector. It is evident that there is a need for the development of new specialized software solutions with user-friendly interfaces, which would make them more accessible, primarily to advisors, and then to agricultural producers.

Effective irrigation management is critical for future food supplies and the prosperity of producers engaged in irrigation production. Through a deficit irrigation field experiment, we determine the financial impact on producers caused by changing irrigation costs, corn prices, extreme weather events, and restricting irrigation levels. Results suggest that the optimal economic strategy within our constrained optimization model is to fully irrigate, with the economic impact highly dependent on commodity prices, restriction level, and irrigation costs. The greatest economic losses caused by irrigation restrictions come from decreases in yield. Some simulations resulted in negative profits, indicating that a switch to alternative crops requiring less irrigation may be warranted.

Smart electric vehicle charging stations (EVCSs) having distributed energy resources (DERs), including photovoltaic (PV) systems and energy storage systems (ESSs), are becoming vital devices for increasing their profit and maintaining stable distribution grid operations by scheduling the real/reactive power of DERs. However, prediction errors of PV generation outputs and electric vehicle (EV) loads from EVCSs may decrease their profit and destabilize the distribution grid owing to incorrect EV charging scheduling and voltage regulation. To address this issue, we propose a two-stage framework for smart EVCS scheduling and inverter-based Volt-VAR control (VVC) using prediction error-integrated deep reinforcement learning (DRL). In the first stage, the EVCS agents train their neural network model with a 30-min resolution to maximize their profit through a day-ahead charging/discharging scheduling of ESSs in the EVCSs while responding to various prediction errors of PV generation outputs and EV loads. The total real power consumption of each EVCS, including the charging/discharging schedules of the ESSs calculated in the first stage, is delivered to the second stage, in which the VVC agent trains its neural network model with a 5-min resolution to minimize the real power loss and voltage violations through real-time reactive power scheduling of the ESSs in the EVCSs via their inverters. The proposed approach was tested in the IEEE 33-node and IEEE 123-node distribution systems. The results show that the proposed approach outperforms DRL methods that do not consider the prediction errors in terms of profitability of the EVCS and reduction of real power loss.

With the goal of profit maximization and overage control, a mathematical model in a single valued triangular neutrosophic fuzzy environment has been designed to fulfil the demands of the industrial sectors during pandemic situations. This research presents that overage is a crucial factor because even in well-arranged businesses, some proportion of the items might be in overage during a prescribed time interval. Well-planned inventory is important to the successful operation of healthiest businesses. The costs of occurrence of overage factors are converted to fuzzy model, and the overall profit is calculated using the signed distance technique and compared using a numerical example. To determine how the overage will influence the overall system, a sensitivity analysis is undertaken. The ideal amount that provides the maximum value of the projected profit per unit of time is found in both crisp and fuzzy models. The suggested maximizing model will undoubtedly aid decision-makers in dealing with overage circumstances induced by pandemic social distance. The new method of this research is to model the merger of profit maximization and overage concept in the framework of COVID-19 to benefit the inventory management and supply chain sectors.

A sure sign that the society needs a firm’s product is its willingness to buy such a product in amounts and at prices that ensure the firm’s profits. In turn, the firm’s sustainable development depends on its ability to convert profits into increased company value by improving the efficiency of current processes, developing new products and implementing innovative business models. The purpose of the study is to determine the directions of the corporate financial service and the ways of its assistance to maximize the value of the firm in the current environment. Theoretical and methodological basis of research was made by scientific works of foreign scientists and experts on the formation of company value. Methods of qualitative and quantitative analysis of scientific publications, analytical materials of famous consulting organizations, statistical data were used. The author proves in the article that the modern corporate financial service has the ability and capacity to manage the creation of value inside and outside the balance sheet of the firm, to actively contribute to the effectiveness of the use of its tangible and intangible assets. In this regard, the author proposes to reclassify the financial function from supporting the firm’s core activities as part of the firm’s infrastructure in the value chain to a primary function.

Energy, greenhouse gas emissions, or water taxes, are present in many power systems as available instruments to implement energy, environmental or climate policies. The goal of these taxes is to change producers’ optimal behavior and hence achieve a cleaner operation. However, these changes in operation are not straightforward to simulate when producers are price makers, or when assets are owned jointly by different agents. State-of-the-art models are unable to consider differentiated income taxes per technology or geographical region, in the case of price makers. In this paper, we present a novel formulation that models the individual market income of each unit using the binary-expansion technique to address the case of a price-maker agent. Unlike existing state-of-the-art formulations, our approach successfully accounts for differentiated income taxes per technology or geographical region and accurate market revenues of shared generators. The proposed model enables evaluating the rational behavior of a generation company confronting a complex yet realistic decision problem, under different types of taxes related to decarbonization or resource conservation policies. The case study incorporates the impact of the installation of carbon sequestration and storage equipment in a fleet of gas-fired power plants, yielding satisfactory numerical results.

Management innovation is particularly important today when technological convergence is becoming increasingly evident. Furthermore, as employees are the most important resource in work dynamic scheduling, the mobility of employees will bring extremely discrete regression particle swarm algorithm uncertainty to work projects, making its scheduling problem more complex. This paper first proposes to apply a discrete regression particle swarm algorithm based on refinement management as an enterprise management innovation to optimize three indicators of time, cost, and stability by implementing refinement personnel management. Secondly, we set constraints for dynamic tasks to implement management rescheduling to avoid overworking employees and thus maximize profits for the enterprise. Finally, the effectiveness of the discrete regression particle swarm algorithm is verified by selecting 27 benchmark examples to compare from three indicators of contribution and time and cost. The results show that the discrete regression particle swarm algorithm (S-PSO-VNS) has an average time reduction of 22.65%, a cost reduction of 15.21%, and a stability improvement of 35.74% compared to the other two control algorithms in terms of management algorithm quality and performance analysis. The research data illustrates that the discrete regression algorithm proposed in this paper can have high efficiency and effectiveness, and the discrete regression particle swarm algorithm can provide managers of SMEs with more informed decisions on work schedules, thus maximizing profits and achieving more profitability.

The production of photovoltaic utility varies within the day/night cycle. At night, photovoltaic cells do not produce anything. However, their day-light production, unconsumed on a current basis and exported to the grid, is compensated for with supply from the grid at night. This scheme of exploitation is called net-metering and is considered herein. Solar energy produced by a prosumer and fed into the grid has to be equal to the electricity supplied from the grid at night; otherwise, a shortage or waste of photovoltaic production occurs. The above finding leads us to the proposition for the optimal solution of photovoltaic capacity. We derived a closed-form capacity solution to the maximized non-linear profit function. It solves harmonic and 2-point production functions that vary symmetrically around the mean production. To verify the solution methodology, harmonic and 2-point models from empirical production data are estimated. Then, the solution is presented together with its return rate and internal return rate. The main finding is that the unit cost of the grid electricity, photovoltaic capacity unit cost and exploitation time all affect the total profit and return rate values while not impacting the optimal capacity of the photovoltaics. The optimal capacity depends on the prosumer’s energy consumption volume and on the natural conditions of production captured here by the technology efficiency coefficient estimated from the production time series.

Abstract Determining optimal management practices for the profitable production of perennial energy crops is critical for scaling up production beyond experimental levels. Although many experimental field studies have examined the effects of management practices on the performance of miscanthus and switchgrass, there are no recommendations for economically optimal nitrogen (N) application rates and how they should vary spatially and with the age of the energy crop as well as on optimal rotation age of the energy crop to maximize profits. We develop a modeling framework to determine economically optimal crop management decisions and simulate the variability under various scenarios for miscanthus and switchgrass production across 2287 counties in the rainfed United States. We find that profit‐maximizing N recommendations for these crops vary across maturity stages and regions and can increase the landowner's profits compared with a uniform N rate across ages and regions. We also find that the optimal rotation for these crops is shorter than the productive physical lifespan (15–20 and 10 years for miscanthus and switchgrass, respectively). Specifically, the N rate that maximizes the economic returns is negligible for miscanthus and 111 kg ha−1 for switchgrass production at age 2. The mean profit‐maximizing N rate increases with age for miscanthus, peaking at 151 kg ha−1 at age 11 before declining to 114 kg ha−1 at the optimal rotation age of 13 years while that for switchgrass is 150 kg ha−1 for middle‐aged stands and declines to 114 kg ha−1 at the optimal rotation of 8–9 years. We find that miscanthus is the most profitable energy crop in the northern region of the rainfed United States while switchgrass is most profitable in the south of the rainfed United States. Our findings are useful for improving assessments of the profitability of energy crops and guiding future management decisions by landowners.

Abstract With the further development of service-oriented, performance-based contracting (PBC) has been widely adopted in industry and manufacturing. However, maintenance optimization problems under PBC have not received enough attention. To further extend the scope of PBC’s application in the field of maintenance optimization, we investigate the condition-based maintenance (CBM) optimization for gamma deteriorating systems under PBC. Considering the repairable single-component system subject to the gamma degradation process, this paper proposes a CBM optimization model to maximize the profit and improve system performance at a relatively low cost under PBC. In the proposed CBM model, the first inspection interval has been considered in order to reduce the inspection frequency and the cost rate. Then, a particle swarm algorithm (PSO) and related solution procedure are presented to solve the multiple decision variables in our proposed model. In the end, a numerical example is provided so as to demonstrate the superiority of the presented model. By comparing the proposed policy with the conventional ones, the superiority of our proposed policy is proved, which can bring more profits to providers and improve performance. Sensitivity analysis is conducted in order to research the effect of corrective maintenance cost and time required for corrective maintenance on optimization policy. A comparative study is given to illustrate the necessity of distinguishing the first inspection interval or not.

Cryptocurrencies have recently attracted considerable attention, resulting in research mainly on deep learning-based price prediction models to maximize profit. Two research approaches have been adopted. Studies adopting the first approach directly predict the future cryptocurrency price. Long short-term memory (LSTM) and gated recurrent unit (GRU), which show high performance in time-series data, are mainly used for this approach. Further, studies adopting the second approach recommend actions to investors to maximize profits, such as “Sell”, “Buy”, and “Wait.” In this approach, classification models are used and results are derived based on probabilities. However, these action recommendation models do not consider the quality of the result. For example, it is risky to accept the result when the probability that the result of the action recommendation model for two classes is the correct answer is approximately 51%. To solve this problem, we recommend a method for adjusting the result of the action recommendation model based on Twitter sentiment analysis. The experimental results show that the proposed adjustment method improves the performance by approximately 3% compared to the conventional methods and are statistically validated.

Prolonged waits reduce tourist satisfaction and have become a major factor in the theme park experience. How to improve tourist satisfaction is one of the main challenges for theme parks, and the key is to develop an appropriate queue management strategy. We develop a theme park queueing model considering the Fast Pass system, derive the optimal pricing strategy under profit maximization, and analyze the Shanghai Disney theme park as an example. The contribution of this paper is to establish a mathematical model of the queuing of theme parks with heterogeneous tourists and incorporate tourist satisfaction into the objective function. The results show that the equilibrium result of the Fast Pass system under profit maximization can optimize both tourist satisfaction (travel utility) and theme park profit, which is more acceptable to theme park operators.

Shea butter is a vital commodity in the global economy, engaging millions of women across the African shea belt, as income directly or indirectly in the value chain. The shea kernel is obtained from processing fresh shea fruits and further processed into shea butter. Manual and semi-mechanized methods are employed in processing shea kernel into butter. Major inputs are; kernel, water, energy, and time and outputs are; shea butter and wastewater. Focus group discussions and individual interviews and material flow analysis were applied to cross-sectional data generated from five Shea Butter Extraction Women Cooperative centres in the Northern and Upper West Regions of Ghana. Results showed that with an input of 86.3 ± 1.88 kg of the kernel, 305.2 ± 24.3 L of water, 49.7 ± 7.2 kg of fuelwood and 15.11 ± 1.9 h, the output was 29.7 ± 2.39 kg of shea butter, 46.86 ± 13.8 kg of shea residues and 202.3 ± 3.95 L of wastewater (effluent). The study recommends more efficient use of inputs to ensure profit maximization. Processing at the cooperative level coupled with sustainable value addition promotion, could guarantee security and increased income. Further research on the chemical composition of shea waste will be required for possible reuse.

One of the key strategies for decarbonization and green transportation is using electric vehicles (EVs). However, challenges like limited charging infrastructure, EV battery characteristics, and grid integration complexities persist. This study proposes a mixed-integer linear programming (MILP) approach to optimize a grid-connected solar PV-based commercial EV charging station (SPEVCS) with a battery energy storage system (BESS) for profit maximization. The MILP model efficiently manages SPEVCS operations, considering solar power fluctuations, EV charging patterns, and BESS usage. By coordinating charging schedules, grid stability is reinforced, and excess solar power can be lucratively managed. Comparing grid-connected and off-grid SPEVCS scenarios highlights grid integration benefits. Solar power mismatches with optimal charging periods pose a challenge, addressed here by BESS utilization and import/export of deficit/surplus power from/to the grid. The proposed framework incorporates solar power forecasts and probabilistic EV arrival predictions, enhancing decision accuracy. This approach fosters viable commercial EV charging, promotes green transportation, and reinforces grid resilience.

This paper proposes a profit maximization model for a data consumer when it buys personal data from data providers (by obtaining consent) through data brokers and provides their new services to data providers (i.e., service consumers). To observe the behavioral models of data providers, the data consumer, and service consumers, this paper proposes the willingness-to-sell model of personal data of data providers (which is affected by data providers’ behavior related to explicit consent), the service quality model obtained by the collected personal data from the data consumer’s perspective, and the willingness-to-pay model of service consumers regarding provided new services from the data consumer. Particularly, this paper jointly considers the behavior of data providers and service users under a limited budget. With parameters inspired by real-world surveys on data providers, this paper shows various numerical results to check the feasibility of the proposed models.

This research studies the Field Technician Scheduling Problem with Experience-Dependent Service Times (FTSP–EDST), involving three main features: matching maintenance tasks with available technicians, sequencing the tasks, and considering the experience-dependent service times. Given a limited number of technicians, the objective is to maximize the collected profit for servicing tasks. This study formulates the problem as a mixed-integer linear programming model and proposes a Modified Iterated Local Search (MILS) to solve the benchmark problem instances of various sizes. A set of FTSP–EDST instances is generated based on existing publicly accessible data, and MILS is utilized to solve these newly generated instances. Computational results confirm the effectiveness of MILS in solving FTSP–EDST.

Online Food delivery, a specialized application of mobile crowdsourcing, has accelerated its popularity due to rushed urban lifestyle in recent times. The food delivery order assignment to workers that maximizes the service qualities, such as maximization of workers’ profit and minimization of order completion time to enhance the customer satisfaction at the same time, is a challenging problem. Existing works in the literature are limited either by focusing solely on minimizing order completion time or reduction of cost incurred in delivery of orders. In this paper, we develop a framework for optimal assignment of food delivery orders to workers as a multi-objective linear programming (MOLP) problem that makes a trade-off in between the worker profit and customer satisfaction. Due to NP-hardness of the above MOLP, a polynomial time solution of the food delivery problem has been developed, namely WWOFooD, exploiting Water Wave Optimization, a meta-heuristic algorithm. The WWOFooD not only explores ways of enhancing service quality of the customers, but also ensures giving additional incentives to the workers providing faster and reliable food delivery services. The results of simulation experiments depict that the WWOFooD offers competitive workers’ profit as well as significantly enhances customer satisfaction compared to the state-of-the-art works.

The article analyzes the impact of innovative development of industrial enterprises on the efficiency of their operation, namely on the cost of enterprises within the cost approach. The correlation-regression analysis of the dependence of enterprise value on the volume of innovation financing for industrial enterprises of Ukraine for the selected period of time, showed a direct relationship between the selected variables for analysis, and in combination with qualitative and quantitative analyzes confirmed the implementation of innovation. Scientific, journalistic, and static sources of information were used in the study. The subject of research in the article is the innovative development of enterprises in the context of the efficiency of enterprises, which is expressed in increasing the value of the company. The purpose of the article is to establish the dependence of the value indicator on such variables as the value of sold industrial products, the amount of funding for innovations for industrial enterprises, equity of industrial enterprises for the period 2008-2019. The paper aims to determine the role of innovation activities of enterprises within the innovation environment; to establish directions and forms of realization of innovative activity at the enterprises; analyze the dependence of cost indicators on the activity of innovation. The following methods are used: statistical, correlation-regression analysis, graphical. The following results were obtained: based on the analysis of the dynamics of changes in the main indicators of the value of industrial enterprises that implemented innovations, financed innovative development, the interdependence between increasing the value of companies and increasing the efficiency of innovation. The impact of the application of innovative development of enterprises in the context of maximizing its value is quantitatively calculated and proved. The article has proved the need for implementation and development of innovative activities in industrial enterprises in the formation of an innovative business strategy for the operation of economic entities, which is an important aspect of management decisions for company managers.

This study delves into the offshore fishing industry in Taiwan, emphasizing the significance of the aquatic product market, supply chain development, and the maturity of cold chain technology. Taiwan’s geographical advantage of being surrounded by the sea provides a thriving environment for marine resources and migratory fish. This study is motivated by the increasing demand for diverse fish products, driven by the growing need for high-quality protein. Recognizing the importance of meeting this demand, this study aims to investigate the capacity of logistics systems and cold storage in the offshore fishery industry, particularly under conditions of uncertainty. To tackle the optimization challenges prevalent in the offshore fishery supply chain, this study employs the bat algorithm (BA), a metaheuristic algorithm inspired by the remarkable echolocation behavior of bats. Additionally, a systematic literature review methodology is utilized to gather relevant articles and establish a comprehensive understanding of the study domain. This study culminates in proposing an optimized fishing model for the offshore fishery supply chain, highlighting the significance of evaluating supply chain value from a management perspective and identifying existing deficiencies and bottlenecks in current research. By focusing on optimizing the offshore fishery supply chain, this study aims to enhance the industry’s efficiency and effectiveness, providing valuable insights and recommendations to improve the capacity of logistics systems and cold storage. Furthermore, this study presents the results of the BA, showcasing its effectiveness in approaching optimization challenges, thereby validating its utility for the offshore fishery industry. Sensitivity analysis reveals the potential for higher profits by raising the inventory limit of the manufacturer, enabling the supplier to provide materials to more profitable trading partners. While this study is based on a revenue and cost model, it acknowledges that the objectives and constraints would become more complex in varying logistic system circumstances. The future study aims to expand the scale of the model and incorporate practical cases to further enhance its applicability.

This study considers building a dynamic model of profit maximization for a carsharing system and its verification based on the case of implementing such a system in Astana, Republic of Kazakhstan. The region, bounded by the administrative boundaries of Astana, was divided into subregions that covered the region with regular hexagons placed side by side. A dataset was built with information on 1168 trips to Astana from January to March 2023. The Kepler visualization service constructed maps of the beginning and end of the trips to the region and a map of trips binding to the hexagonal grid cells. Each cell of the grid corresponds to a specific subregion, for which the quantitative parameters necessary for solving the profit maximization problem in the carsharing system are calculated. Stations with cars are placed in the cells of the grid, which are available to carsharing service customers. Based on the collected data, dynamic (four periods per day) and static profit maximization models in the carsharing system were built. Modeling was carried out based on the built models in the case of Astana. It was established that using a dynamic profit maximization model in the carsharing system increases profit by 3.7%. The obtained results are important for the development of the infrastructure of the capital of Kazakhstan and for finding a solution to the problems of urban science in this region.

Characterizing the demand curve of products is important for pricing them optimally. However, in deriving empirical demand curves, econometricians have to contend with identification issues. Furthermore, theoretical demand curves derived using standard economic theory are divorced from empirical realities: firms rarely have information on customers’ budget constraints; theoretical utility functions are seldom derived empirically. Recognizing these issues, we propose an experimental approach for determining a product’s demand curve and, in turn, its profit-maximizing price in online environments. The proposed approach yields precise estimates and is quick and inexpensive to implement.

Efficient meat production is crucial in addressing global market demands and sustainability goals. Modeling production systems has gained worldwide attention, offering valuable insights for predicting outcomes and optimizing economic returns. In the poultry industry, researchers have developed mathematical models to predict animal performance and maximize profits. These models incorporate theories to explain real-world processes and enable future event predictions. One such model is the Broiler Growth Model (BGM), which serves as a predictive tool for estimating feed intake, growth, and body composition of broilers. The BGM takes into account the genetic potential of the broilers, the feed they are provided, and several constraining factors that may prevent the animal from achieving their genetic potential. To evaluate the BGM, a series of simulations were performed: (i) model behavior was evaluated by simulating the response of males and females from 22 to 35 d to feeds differing in dietary protein content and nutrient density; (ii) model prediction was evaluated using the results of a protein response trial conducted at UNESP in which six dietary protein levels were fed to male and female broilers over a 56 d period; and (iii) model optimization was used to maximize economic returns in the above trial. The model behaved as expected when feeds differing in protein content were fed, with feed intake per kg of BW increasing as protein level was decreased, resulting in lower gains and higher body lipid contents. Increasing nutrient density resulted in higher feed intake in the second level, followed by a reduction in feed intake in the highest nutrient feed. The simulated response to nutrient density resulted in increasing body lipid deposition as the nutrient density increased. In comparing the simulated and actual results of the protein response trial, the overall error of prediction was up to 15% for feed intake, BW, and body protein. The optimization routine allows the simulation of different economic scenarios, helping in decision-making. The Broiler Growth Model emerges as a valuable tool for the poultry industry, offering predictive capabilities and economic optimization potential. While minor discrepancies between simulated and actual results exist, the BGM holds significant promise for enhancing efficiency and profitability in broiler production, contributing to the broader goals of sustainable broiler meat production.

Using a hypothetic southern pine plantation in the state of Georgia of the United States, a benefit-cost analysis of forest carbon is conducted for landowners in the voluntary carbon market. With carbon values being defined by the marginal forest growth, it is found that the inclusion of carbon into the objective function usually leads to longer rotations and higher total profits. In our baseline analysis, the rotation age increases by four years when both timber and carbon are considered. Thus, landowners can benefit from voluntary carbon trading and additional carbon can be fixed in the forests. Landowners’ decision on forest carbon is most sensitive to the discount rate and timber and carbon prices. When the discount rate is higher, future cash flows become less valuable and the optimal rotation shortens. When timber prices exceed carbon price by a large margin, timber value dominates carbon value and the optimal carbon rotation approaches the Faustmann rotation. Therefore, with a higher discount rate and higher timber prices, voluntary carbon trading will result in less carbon additionality. Finally, when the economic uncertainty is incorporated into the decision making, the results tend to be sustained albeit carbon additionality is slightly reduced.

The article is devoted to an effective methods of cost optimization in small business. All of them, used at the right time, make it possible to improve the situation and maximize profits by reducing costs.«The process of cost optimization cannot be started for a while and then stopped. It is necessary to understand that business process, investment and procurement management should be considered in the long term from the point of view of cost optimization. First of all, it concerns new projects, new equipment and new technologies» — the most important thesis of this article. The author offers an algorithm of actions for entrepreneurs who are ready to solve the optimization problem in a complex way.

This paper proposes a profit maximization model for a Decentralized Electric Vehicle Supply Equipment (D-EVSE) equipped with a renewable energy sources system such as a solar energy system. We acknowledge a power connection to the central power grid when necessary. We allow EV to offer its surplus power as discharging power processes. We design a Decentralized Profit Maximization Algorithm (DPMA) to help D-EVSEs take profit from the electricity price variation during the day when selling or buying electricity respectively to EVs or from the grid or EVs as discharging processes. Finally, numerical simulations with MATLAB are conducted to prove the effectiveness of our proposed solution.

A storage-based customer flexibility (SCF) platform enables on-demand remote access to a shared pool of utility-scale energy storage resources at substantially low costs. It can provide users the ability to charge and discharge electrical energy to and from remote batteries. This study demonstrates the economic feasibility of an SCF platform business under realistic conditions. Accordingly, the load-serving entity is set as the SCF operator, and the subscriptions from various types of customers and multi-use battery energy storage systems are considered to increase profitability. An economic feasibility verification is provided based on the generating pattern of individual customers, and a robust optimization is performed for the SCF platform operations considering the market price uncertainty, aggregated requests, and customers’ willingness to pay for the SCF service. The proposed study is simulated in a California environment.

Virtual bidding provides a mechanism for financial players to participate in wholesale day-ahead (DA) electricity markets. The price difference between DA and real-time (RT) markets creates financial arbitrage opportunities for financial players. Physical market participants (MP), referred to as participants with physical assets in this paper, can also take advantage of virtual bidding but in a different way, which is to further amplify the value of their physical assets. Therefore, this work proposes a model for such physical MPs to maximize the profits. This model employs a bi-level optimization approach, where the upper-level subproblem maximizes the total profit from both physical generations and virtual transactions while the lower-level model mimics the multi-period network-constrained DA market clearing process. In this model, uncertainties associated with other MPs as well as RT market prices are considered. Moreover, the conditional value-at-risk (CVaR) metric is utilized to measure the risk of diverse strategies. The optimal strategy of the strategic physical MP is derived by solving this bi-level optimization model. The proposed bi-level model is transformed to a single level mixed integer linear programming (MILP) model using Karush–Kuhn–Tucker (KKT) optimality conditions and the duality theory. Case studies show the effectiveness of the proposed method and reveal physical MPs may choose to deploy virtual transactions in a very different way than pure financial MPs.

This paper analyzes the rapid onset of Robotic process automation (RPA) technologies in the Czech financial sector between 2015-2020. The development and experience from the “hype-and-fear” phase contributed to business process integration and technological spillovers are expected in the future. If ICT capabilities are the source of performance differences, then most likely candidates are inventory and finances business process integration and implemented Enterprise Resource Planning and Customer Relationship Management systems. The RPA should not be seen only as simple automation but as a complex instrument offering a lot of advantages with a focus on benefits for internal and external stakeholders. PURPOSE. The goal is to qualitatively analyze the experience with RPA implementation and quantitatively assess ICT capabilities via analysis of differences between various organizational ICT activities and types of companies in the Czech financial sector. DESIGN/METHODOLOGY/APPROACH. The qualitative case study was performed in the bank ČSOB, a.s. in 2019, respondents, owners of processes in the final part of automation, were chosen randomly from different departments of the bank. Data for the quantitative part comes from the ICT survey (Czech Statistical Office), business performance dataset (EMIS) and a case study about ICT capability implementation. Differences are assessed and indirectly interpreted using goodness-of-fit approach. FINDINGS AND IMPLICATIONS. The results from the RPA case study revealed that the Czech financial sector is past the “hype and fear” phase and many companies focused on their return on investment and are beginning to focus more on other stakeholders. According to this development, the requirements and outputs are suggested in the phases of RPA implementation. The possible source of rofitability performance differences are integrated business processes. LIMITATIONS. Financial sector data are anonymized in ICT surveys and the measurement of the competitive advantage of ICT capabilities is only indirect. Qualitative approach is suggested with focus on technological efficiency measurement using data envelopment approach. ORIGINALITY. This paper provides an understanding of the strong experience in RPA in Czech financial sector. Certain initial setbacks in RPA are expected and this paper suggest to focus on knowledge management (lessons learned) and other requirements influencing the successful RPA prototyping and implementation process.

Improving the efficiency of subsidy policy to better promote R&D investment in new energy vehicles (NEVs) is of great strategic importance for reducing greenhouse gas emissions and achieving sustainable development. Using the technology adoption life cycle theory, this paper proposes a tri‐level programming model among a government, a NEVs manufacturer and customers to investigate the NEVs manufacturer's R&D investment strategies, the government's purchase subsidy policies, and customers' purchasing decisions. Through theoretical and numerical analysis, it is found that when the manufacturer is to maximize the sales, it commits R&D investment which maximizes sales of NEVs, or its entire R&D investment capital. When the manufacturer is to maximize its profit, it commits R&D investment which maximizes its profit, or makes the sales constraint satisfied. Purchase subsidy can raise the sales of NEVs, but cannot increase manufacturer's R&D investment, or even have a crowding‐out effect on its R&D investment. These findings will help government improve the efficiency of purchase subsidy to better support the development of the NEVs industry.

Cloud computing has attracting more and more attention for its flexibility and economic benefits. To maintain the supply-demand relationship among different participants in cloud computing environment, the exchange of value is the inner drive. From the perspective of cloud service provider, its primary concern is to earn the profit, which can be obtained by finishing the tasks published from customers. In this paper, we consider each task consists of numbers of sub-tasks in the logical order, each sub-task corresponds to a type of service requests, which can be served in unique multi-server system. On this basis, we propose a profit maximization problem in the multistage multi-server queue systems, in which customers are served at more than one stage, arranged in a series structure. Moreover, a deadline constraint is taken into consideration, which demonstrates the maximum tolerance degree that the customers can wait. Therefore, how to configure the parameters in multistage multi-server queue systems to maximize profit on the premise of reducing the waiting times of customers is a critical issue for cloud service provider. To address this problem, we first discuss the probability distribution function of the waiting time for single multi-server system and multistage multi-server queue systems respectively, and then propose a profit maximization model under the deadline constraint. Due to the complexity of this model, the analytical solution can hardly be obtained, we study a heuristic method to search for the optimal solution. At last, a series of numerical simulations are implemented to describe the performance of the proposed profit maximization scheme, the results show that not only the profit can be maximized, but also the waiting time of customers have been reduced effectively.

The fifth generation of mobile communication networks (5G) is an emerging paradigm that can accommodate more audiovisual entertainment video sites from mobile applications and the video quality can be greatly improved by reducing latency. Due to the limited multimedia resources and insufficient bandwidth, it is of great importance to allocate multimedia resources reasonably and enhance user experience. Although researchers have proposed many resources allocation mechanisms, most of them focus only on maximizing system efficiency. Considering system efficiency, transmission delay and the profit of service providers, an optimal multimedia resources allocation algorithm in the 5G edge cloud networks is proposed from the perspective of service providers. Based on the Lyapunov stochastic profit maximization algorithm, system efficiency, transmission delay and the profit of service providers are regarded as performance indicators and the multimedia resources allocation problem is transformed into queue stability issues. Finally, rigorous analyses and simulation experiments show the performance of Lyapunov stochastic profit maximization algorithm in multimedia resources allocation.

This study presents a bi-level framework to obtain optimal energy management of electrical energy storage (EES) units in power systems by minimizing the operation cost of the system to satisfy daily demand and by maximizing the benefit of storage units' owner. Two agents are considered consisting of the power system operator and the owner of EES units. The former seeks to determine the minimum operation cost of the system in providing the system load, while the latter seeks to provide its maximum profit. The power system operator has the option to supply energy by the thermal generation plants or from the storage units. The proposed bi-level model provides optimal operation strategies for both the EES owner in the outer level and the power system operator in the inner one. In other words, the decision making of the power system operator is taken into account explicitly in optimal charge/discharge scheduling of EES owner in the introduced bi-level framework. The introduced bi-level approach is applied to the IEEE RTS 24-bus network in order to assess the performance of the model.

Any engineering system requires engineers to make numerous managerial and technological decisions during the design, building, and maintenance phases. All such decisions aim to either maximize the desired benefit or minimize the amount of effort needed. There is no solitary approach that can effectively address all optimization problems. As a result, number of optimization techniques have been created to address various optimization problems. Transportation problem (TP) formulation is the most significant programing applications in optimization which is attached to everyday life and used in industries under logistics. This study proposed a new algorithm to obtain a basic feasible solution (BFS) for the maximization TP. The approach outlined in this study yields an initial solution that is close to or optimal in most scenarios. A variety of numerical examples are used to demonstrate the new technique. The proposed technique is effective for analyzing balanced or unbalanced transportation problems with maximization objective function.

Cloud computing is a widely used technology today and thus, large number of applications are being stored in the cloud. To cater to the needs of the consumers an intermediate role known as cloud broker has been introduced. It helps to cut down overall expenses of the cloud users. The proposed system concentrates on configuration of the cloud broker to improve profit. This maximization scheme depends upon various factors like customer request, selling price of the resource, purchasing price of the resource, intensity of the request and so on. The system developed, aims at maximizing the profit of the cloud broker who services consumer requirements by providing cloud infrastructure at a lower cost from an infrastructure vendor. Availing pay-as-you-go schemes or on-demand payment has proven to be useful. The complex cloud landscape along with various billing schemes paves a way for a profit maximization model that incorporates temporal multiplexing by a middleman entity for a better economic upfront. The resource multiplexing is further enhanced by incorporating M/D/c queueing model and short-term and long-term renting schemes to optimize the resource allocation. The entire application has been implemented using Java especially the NetBeans IDE 8.2. Different algorithms were compared to further enhance the pricing scheme selection. Finally, Hill-Climbing algorithm is used to allocate the resources dynamically. The system further notifies the cloud broker on resource shortage through dynamic disk utilization graph. The proposed system considers quality of service and price of service as the determining factors in maximizing the net profit of the cloud broker.

Introduction The world is pace with the processes of globalization. Iran is not apart from this globalizing process and has been involved in many trends, including the phenomenon of pyramid companies (network marketing). This issues a new style of modern business, with its new work in the late of 70s Hijri (or in the early 80s) with companies such as Goldquest and Goldmine in Iran. Their activities created very crucial negative effects to the public, the community and economic body of the country and made a constitutional crisis in the country’s atmosphere. Finally, the discretion of the legal authorities banned their activities in the country. The business, however, is now a few years with a new light in the face by removing the same obstacles in the country with a legal permit. While the country’s current state is worse than the previous decade (80s) in terms of economic difficulties, the sharp recession, the problems of country’s active population and thousands of fundamental problems whose main origins go back to the economy. On the other hand, it is expected that this kind of trade problems and barriers in the past and may be solved to a great extent, but we are witnessing difficulties and obstacles. The high rate of the young population of the country has been mentioned in recent years by the social sciences thinkers. According to the demographic theory of open window of population, Iran is a highly sensitive site in the region as well as inside the country due to its young population, that, if taken in the right direction, it would promote the country and otherwise damage. This high percentages, despite university education, face a demographic inflation of youth by failing to attract the labor market. Based on different studies, they report a high percentage of frustration, depression, and so on. On the other hand, in recent years, we see major changes in the values and attitudes of Iranian youth for different reasons, including the growth and development of communication and technologies. Thus, the characteristics of social context in Iran have different forms of development for boosting networks. The prevalence of globalization and its ideology, the sudden rise of population and unemployment, social networks, and strong local regions (derived from pre modern times) have all contributed to the prevalence of pyramid-based enterprises (network marketing), which have the slogan of acquiring huge fortunes in a short period of time: This study investigates how profit maximization strategies are implemented in pyramid companies and what the consequences of these strategies are. Material & Methods The paradigm used in this study is critical ethnography and research is done using qualitative methodology. The critical ethnography has been chosen by the 5-scale approach of Carspecken. Sampling is based on purposive and chain sampling. In this direction, after participatory hidden observation and semi structured interviews with the active members in the companies, 16 cancelled individuals were selected from 3 network marketing companies to do deep interviews to achieve theoretical saturation. Discussion of Results & Conclusions The result indicate that pyramid companies are able to attract a large number of people in Iran through the use of credit over a very long period of time. In this way, compliant and reagent credits are key factors that these companies use to obtain their own purposes. Pyramid companies are trying to change the style of business and create a fundamental shift in the field. But, they try to achieve their goals by exploiting the traditional market failures and variables involved in the traditional world. The actors involved in these enterprises are usually from poor levels of the society who have little economic and social power. In particular, class categories include the middle and lower classes which are relatively marginalized in the society and are constantly suppressed by various social, political, and economic forces. In such circumstances, these people usually have a neutral agency. Iran as a polarized society has suffered extensive social damage due to its policy of adjustment and corruption resulting from this issue (Piran, 2004: 24) which has caused to attract a particular context of modern commerce. People on the margins are also influenced by the positive advertisements of this emerging systems, and tend to identify their own identity as a networking one and rebuilt their existing ruined and suppressed identity. It is at this point that agents’ state of mind at modern tribes change (confronting with reality). They have to limit many areas of their lives to gain profits, therefore, separation of family, lack of intimacy with wife, borrowing money and etc. are damages that are imposed on them. This process is moving forward that in the face of structure, the functionality of the actors is weak and defeated, and eventually leads to the withdrawal of the membership from the system.

The article describes the logic of solving the main financial and production task of managing the development of industrial corporations and provides the practical approaches to the application of this solution. The existing academic theoretical approaches disclose the logic of increasing the rates of financial and industrial growth of the industrial corporation profit but they are not appropriate for the development of the methodology for meaningful practical solution of the mentioned financial and production problem. The author shows that this problem is soluble on the basis of equilibrium theory of an industrial corporation. The article reflects the principles of static, dynamic and compositional theories of equilibrium theory of an industrial corporation. It compares the logic of a modern theory of CVP-analysis with the logic of the equilibrium theory, focuses on the economic essence of these theories’ application and identifies the main practical mistake of such an application. The article provides the basis for the practical application of these theories to increase the rate of financial and production growth and maximize the profits of industrial corporations. The practical applicability of the theory is verified at a number of large Russian corporations including some big enterprises of the defense industry.

This paper aims to achieve enlightenment from Maqasid shariah in terms of profit maximization within Islamic banking. Therein the study conducting library-based approach for every single data obtained by virtues of books, articles, and other scholastic literatures and additionally different supporting approaches, namely Islamic jurisprudence, regulatory framework, accounting, and risk are engaged in this paper to exhibit more substantial apologia as regard of the topic. In respect of finding, making the best use of resources to achieve profit maximization within the bounds of the maqasid should be believed as maximization of other fundamental independent-rights, inter alia, right of Allah (zakah) and right of state (taxation). Moreover, the profit generated ought to bring refinement not solely to strengthen the standing of nifs mal (preservation of wealth), but also to nurture whosoever muslim person who successfully transforms its distress, time, and assets into financial gain. It is obvious that in shariah perspective specifically in the case of profit, nonetheless the right of Allah is the supreme, the right of human-being coming first after all worldly-obligations are adequately accomplished. Differently expressing, Maqasid al-shariah is a conception of precedence on how putting the best interest into the right time and the right place without inflicting suffering on the other interests with purpose that every single of interest could be on stage in the long run. For all practical purposes, this study call attention to the muslim public particularly by not losing sight of the fact apropos to the most fundamental of the maqasid principally in the circle of wealth namely, protection and establishment sequentially whereby IB should not be dictated to move with insistent haste by taking exhortations of greater distant of Maqasid provided by numerous contemporary Islamic scholars due to inherent nature of the bank per se. Through careful interpretation, this study encapsulates on how to derive and benefit from the maqasid by stages (protection, establishment, nurture) rather than in an instant manner.

Network slicing has been viewed as a key enabler for the next-generation software-defined and cloud-based network (e.g., 5G and beyond) to accommodate diverse services in a flexible and cost-efficient fashion. Network slicing allows a network slice provider (NSP) to operate on a common network infrastructure to create customized isolated logical networks (i.e., network slices) for network slice customers (NSCs), (i.e., service providers). NSP and NSCs are independent operators who pursue profit maximization, while in the literature, only network cost optimization is intensively investigated in terms of service function chain embedding, i.e., virtual network function (VNF) placement and flow routing. Therefore, slices should be dimensioned (i.e., resources allocated to slices) according to the resource availability and the economic mechanism in the network, so as to optimize the resource utilization and improve the profit of NSP/NSCs. In this paper, we study elastic slice dimensioning with resource pricing as a Stackelberg pricing game, in which the NSP sells slices by pricing resources and NSCs adjust their slice's resource demand on VNF capacity and bandwidth, while both are trying to maximize their profit. Then, we formulate optimization problems for the pricing game and find that a closed form solution of the optimal price cannot be obtained for a non-trivial network. Hence, we propose a resource pricing algorithm that aims to maximize the NSP's profit and the network's social welfare. Compared with existing usage-based pricing method and two heuristic methods, our proposed pricing algorithm for slice dimensioning strikes a trade-off between maximizing NSP's profit and other metrics, including the resource utilization. Hence, it will helpfully exploiting the benefits of network slicing.

As network operators invest more and more in infrastructure to keep up with the ever-increasing traffic demand, it has become important for them to operate networks in a profitable manner. The resulting expansion of network infrastructure also increases power consumption, which has a negative impact on both the environment and revenue. In this regard, the cloud-based radio access network (C-RAN), which is a promising next-generation network architecture, has gained much attention as a solution. In addition, by utilizing the macro base stations for coverage, the resulting heterogeneous C-RAN (H-CRAN) can further help optimize the network while increasing the complexity of resource optimization. In this paper, we study an optimal resource allocation for C-RANs to maximize profit while minimizing power consumption considering the inherent network uncertainties. Moreover, by allowing network operators to share networking resources among themselves, we show that the service outage can be minimized without installing additional bandwidth or base stations. The proposed multi-stage stochastic programming model makes a robust optimal decision that effectively responds to uncertainties from users' mobility and service demand while maximizing both profit and energy efficiency. The extensive evaluation and comparison results show that the proposed solution can maximize profit and energy efficiency while minimizing the service outage under network uncertainties.

With the widespread use of the Internet of Things, data-driven services take the lead of both online and off-line businesses. Especially, personal data draw heavy attention of service providers because of the usefulness in value-added services. With the emerging big-data technology, a data broker appears, which exploits and sells personal data about individuals to other third parties. Due to little transparency between providers and brokers/consumers, people think that the current ecosystem is not trustworthy, and new regulations with strengthening the rights of individuals were introduced. Therefore, people have an interest in their privacy valuation. In this sense, the willingness-to-sell (WTS) of providers becomes one of the important aspects for data brokers; however, conventional studies have mainly focused on the willingness-to-buy (WTB) of consumers. Therefore, this paper proposes an optimized trading model for data brokers who buy personal data with proper incentives based on the WTS, and they sell valuable information from the refined dataset by considering the WTB and the dataset quality. This paper shows that the proposed model has a global optimal point by the convex optimization technique and proposes a gradient ascent-based algorithm. Consequently, it shows that the proposed model is feasible even if the data brokers spend costs to gather personal data.

Network externality is an important feature of the era of network economy. The competition between online and offline retail pharmacies caused by network externality is getting fiercer and fiercer. In this context, we studied the long-term market competition evolution of two-channel retail pharmacies under the network external environment, in order to obtain higher profits, two-channel retail pharmacies prefer to choose profit maximization marketing strategy or market share maximization marketing strategy. Using evolutionary game theory, an evolutionary game model of two-channel retail pharmacies was established. Through the establishment of the model, obtain the payment matrix of different marketing strategies selected by the two-channel retail pharmacies and the evolution and stability of the selection of marketing strategies. The results show that when the strength of the network externality is within a certain range, the traditional retail pharmacies with higher marketing cost choose to pursue the profit maximization strategy, while online pharmacy with lower marketing costs choose market share maximization strategy to achieve greater profits; when the network externality is large, in order to maintain a certain market share in the long-term market evolution, the traditional retail pharmacy with high marketing cost chooses to pursue the market share maximization marketing strategy in order to obtain greater profits, while the online pharmacy with low marketing cost chooses to profit from the profit maximization marketing strategy.

Discount is a frequently used marketing tool. This paper is devoted to designing an effective dynamic discount pricing (DDP) strategy in competitive marketing. First, we introduce a competitive word-of-mouth (WOM) propagation model with DDP mechanism. On this basis, we model the original problem as an optimal control problem. Next, we derive the optimality system for the optimal control problem and, thereby, propose the concept of competitive DDP strategy. Finally, through comparative experiments, we find that the profit of a competitive DDP strategy is satisfactory. Our findings contribute to maximizing the marketing profit in the presence of commercial competitors.

Electric vehicles (EVs) will be dominating the modes of transport in the future. Current limitations discouraging the use of EVs are mainly due to the characteristics of the EV battery and lack of easy access to charging stations. Charging schedules of EVs are usually uncoordinated, whereas coordinated charging offers several advantages, including grid stability. For a solar photovoltaic (PV)-based charging station (CS), optimal utilization of solar power results in an increased revenue and efficient utilization of related equipment. The solar PV and the arrival of EVs for charging are both highly stochastic. This work considers the solar PV forecast and the probability of EV arrival to optimize the operation of an off-grid, solar PV-based commercial CS with a battery energy storage system (BESS) to realize maximum profit. BESS supports the sale of power when the solar PV generation is low and subsequently captures energy from the solar PV when the generation is high. Due to contrasting characteristics of the solar PV and EV charging pattern, strategies to maximize the profit are proposed. One such strategy is to optimally size the BESS to gain maximum profit. A mixed integer linear programming (MILP) method is used to determine the optimal solution.

Abstract. The article is devoted to the study of the effectiveness of factoring management as an innovative product based on the formation of the optimization factoring portfolio of trade enterprises, which will serve as a basis for management decisions on the implementation of this service in business. As a result of the research a number of conclusions and methodical recommendations of practical direction are formulated. The formation of the optimization factoring portfolio is based on the principles of the CAPM portfolio investment model proposed by W. Sharp. The conditions for the formation of portfolio investments, which are reflected in the price model of the capital market, were adapted to the formation of the factoring portfolio. The key conditions of the factoring agreement are: limits on credit risk and a given level of income, that is the company must choose a portfolio with the maximum profitability, which is achieved by minimizing the cost of this service; seek to increase the funding limit for debtors; to remove from the portfolio of debtors of the enterprise with a high risk of non-repayment of debt. We propose to determine the debtors of the enterprise that will form the optimization factoring portfolio through the variables x1 – xn. When x = 1, the debtor is added to the factoring portfolio, and if x = 0, then it is necessary to exclude the debtor from the portfolio. In the language of factoring regression agreements, you can use a continuous linear programming problem in which the variable x will be in the range from 0 to 1, that is funding limits for debtors can be broken. The target function of the factoring portfolio of commercial enterprises is proposed both for «invoice-discounting» (only receivables financing) and for «full factoring», that is full-fledged factoring (the use of additional related factoring services). The profitability of the enterprise from factoring operations depends on the number of debtors in the factoring portfolio. The greater the number of debtors in the portfolio, the higher the profitability of the company from factoring. However, the company needs to consider debtors with high credit risk in order to minimize the non-repayment of receivables. Therefore, debtors with the highest credit risk of factoring should be excluded from the factoring portfolio. To assess the economic efficiency of factoring management of trade enterprises on the basis of the formed optimization factoring portfolios, we propose to use the Cronbach & Gleser method. The proposed proposals for the management of factoring of commercial enterprises, which is based on the formation of factoring portfolios, will regulate debt relations between enterprises with the involvement of third parties, that is, to prevent the occurrence of negative phenomena in the debt relations between commercial enterprises in the future, as well as will help maximize profits in the studied business entities. Further research will focus on adapting the factoring portfolio of commercial enterprises to changes in the value of financial resources under the influence of transformational changes in the economy. Keywords: factoring portfolio, trading enterprises, transformational changes, factoring management, debt relations between enterprises, profit maximization. JEL Classification С44, С65, С61, F65 Formulas: 11; fig.: 0; tabl.: 0; bibl.: 20.

This study examines the roles of resource consumption accounting and competitive prices in attaining sustainable profitability. The objectives were (1) to determine whether the adoption of resource consumption accounting practices yields significant improvements in competitive strategies in a highly competitive situation where activity-based costing has proved to be insignificant, and (2) to ascertain if the positive relationship between competitive pricing and sustainable profitability is increased by the extent to which resource consumption accounting exerts pressure for sustainability profitability. A PLS-SEM procedure was applied in analysing 129 of the top 30 performing companies’ structured questionnaire responses drawn from five industries in Kurdistan from 2021. The empirical results demonstrated that competitive pricing models involving resource consumption accounting systems provide superior price forecasting, error reduction and profit maximisation capabilities than existing energy models. The study’s outcomes highlight that the extent to which resource consumption accounting exerts pressure on sustainability profitability significantly increases the positive relationship between competitive pricing and sustainable profitability. The results of this study advance construct and item development involving competitive pricing and resource consumption accounting while testing relationships to uncover the moderating role of resource consumption accounting in profit maximisation. Thus, energy and non-energy industrial companies must rely on resource consumption accounting to set competitive prices and enhance and sustain their profitability by considering the overlooked energy pricing stochastic parameters and errors amid rising energy shortages and costs.

China’s livestock output has been growing, but domestic livestock products such as beef, mutton and pork have been unable to meet domestic consumers’ demands. The imbalance between supply and demand causes unstable livestock prices and affects profits on livestock. Therefore, the purpose of this paper is to provide the optimal breeding strategy for livestock farmers to maximize profits and adjust the balance between supply and demand. Firstly, when the price changes, livestock farmers will respond in two ways: by not adjusting the scale of livestock with the price or adjusting the scale with the price. Therefore, combining the model of price and the behavior of livestock farmers, two livestock breeding models were established. Secondly, we proposed four optimal breeding strategies based on the previously studied models and the main research method is Pontryagin’s Maximum Principle. Optimal breeding strategies are achieved by controlling the growth and output of livestock. Further, their existence was verified. Finally, we simulated two situations and found the most suitable strategy for both situations by comparing profits of four strategies. From that, we obtained several conclusions: The optimal strategy under constant prices is not always reasonable. The effect of price on livestock can promote a faster balance. To get more profits, the livestock farmers should adjust the farm’s productivity reasonably. It is necessary to calculate the optimal strategy results under different behaviors.

This article explores the comparative concept of profit maximization and mas}lah}ah in the process of forming the final price in the production cycle. In conventional economics, profits can be formed through various market models faced by producers to achieve maximum profits. on the other hand, Islamic economics views that producer profits must contain Islamic values. This value is reflected in the production goals, which must contain mas}lah}ah and uphold the ethics and norms of consumers and producers. The ultimate goal of mas}lah}ah is a manifestation of maqasid sharia which is to achieve world happiness and the hereafter (fala

A knapsack problem is to select a set of items that maximizes the total profit of selected items while keeping the total weight of the selected items no less than the capacity of the knapsack. As a generalized form with multiple knapsacks, the multi-knapsack problem (MKP) is to select a disjointed set of items for each knapsack. To solve MKP, we propose a deep reinforcement learning (DRL) based approach, which takes as input the available capacities of knapsacks, total profits and weights of selected items, and normalized profits and weights of unselected items and determines the next item to be mapped to the knapsack with the largest available capacity. To expedite the learning process, we adopt the Asynchronous Advantage Actor-Critic (A3C) for the policy model. The experimental results indicate that the proposed method outperforms the random and greedy methods and achieves comparable performance to an optimal policy in terms of the profit ratio of the selected items to the total profit sum, particularly when the profits and weights of items have a non-linear relationship such as quadratic forms.

In this paper, we consider a heterogeneous mobile cloud computing (HMCC) system that consists of remote cloud servers, local cloudlets, task offloading mobile devices (TMDs), non-task offloading MDs (NTMDs), and radio access networks such as cellular networks and WLANs. TMDs have the capability of task offloading to remote cloud servers or cloudlets, whereas NTMDs are conventional cellular users that do not have such capability. By using stochastic geometry, we analyze the outage probability of task offloading in the MCC system with only remote cloud servers and that in the HMCC with both remote cloud servers and cloudlets. The analysis provides useful information, i.e., how the varying system parameters affect the outage probability. From the analysis, we show that there is an intrinsic limitation in reducing the outage probability in the MCC system due to the outage when accessing remote cloud servers. In addition, we show that the use of cloudlets is a promising solution to overcome this limitation. However, a tradeoff exists in using cloudlets due to their deployment and operation costs. Thus, to address this tradeoff, we also study the optimal cloudlet deployment to maximize the cloud service provider's profit while guaranteeing maximum outage probability requirements.

With the development of cognitive radio networks in recent years, spectrum utilization has been enhanced, as secondary users can lease under-utilized spectrum from the spectrum owners. Spectrum is allocated through auctions in wireless communication networks. The auction can provide benefits for both primary users and secondary users. Existing auction mechanisms for spectrum are mainly based on interference graphs and consider the heterogeneity of spectrums only to a minimal degree. The economic efficiency of the auction is usually neglected due to the focus on improving spectrum utilization. In this paper, we consider a signal-to-interference-plus-noise ratio (SINR) constrained interference model; this model is more realistic as users can simultaneously communicate as long as their requirements SINRs are satisfied. We propose a truthful profit maximization double auction mechanism to improve the benefit of networks with low energy. At the same time, security concerns are guaranteed because buyers and sellers make their true critical decision, i.e., they cannot improve their utility by misreporting their asks and bids. Moreover, our proposed novel auction mechanism is individually rational and budget-balanced. The experiments demonstrate that our auction mechanism efficiently increases the number of winners and improves the auctioneer's profit.