Profit Maximization Research Articles

Utilization method of low-grade thermal energy during drilling based on insulated Drill pipe

Downhole heat damage during drilling seriously restricts the exploration and development of deep oil and gas reservoirs and geothermal resources. The petroleum industry focuses on controlling the downhole heat damage during drilling while utilizing low-grade thermal energy and making it profitable. Therefore, a techno-economic coupling evaluation model is developed for a low-grade thermal energy utilization system developed for use during drilling along with a wellbore heat transfer model. Subsequently, based on the model, the influence of different lengths and installation positions of insulated drill pipe (IDP) on the downhole temperature is studied. Then, on the premise of satisfying the wellbore cooling target, the system is economically evaluated based on factors such as production time, government subsidy, and electricity price, and the corresponding measures are proposed to make the system profitable. Finally, a set of efficient IDP-based low-grade thermal energy utilization methods is developed. The case study presented herein shows that the IDP length that allows the system to reach the maximum profit in the 10th year is 4720 m for a horizontal well with a depth of 6200 m when the government one-off subsidy rate is 30% and electricity price is increased by 30%.

Comparison of Stock Prediction Using ARIMA Model with Multiple Interventions of Step and Pulse Functions

Stock price predictions based on technical analysis using historical data help investors determine the optimal time to buy or sell shares with the aim of achieving maximum profits. The aim of this research is to compare the results of Kimia Farma's share price predictions using the ARIMA model with intervention analysis of two variables at once, namely the pulse function and the step function. This is the novelty of this research. The data used in this research is daily data on Kimia Farma shares from the period 16 April 2018 to 14 April 2023. The best model produced is ARIMA (0,1,1) with intervention, shown by a MAPE value of 0.3356% and an RMSE of 0.3356%. 4.03. Kimia Farma's share price prediction for the next five days is 906.5548; 905.7875; 905.0206; 904.2542; 903.4882 rupiah. An increase in share prices occurred after the intervention in the period 15 April 2023 to 19 April 2023. Keywords: ARIMA; intervention model; step and pulse function; kimia farma

Efficiency wages with motivated agents

Many jobs serve a social purpose beyond profit maximization, contributing positively to society. This paper uses a modified principal-agent gift-exchange game with positive externality (prosocial treatment) to study how workers' prosocial motivation interacts with the use of efficiency wages in stimulating effort. We find that prosocial motivation and efficiency wages are independent in stimulating effort: compared to a standard gift-exchange game (GE treatment), the presence of the externality shifts the agents' effort choice function upwards without affecting its slope. Thus, if principals were profit-maximizers, wage offers should be the same in both treatments. However, principals offer higher wages in the prosocial treatment. We show that this is due to principals in the GE treatment highly underestimating agents' reciprocity and thereby offering wages below the profit-maximizing level. Results from robustness-checks further suggest that our findings are unlikely to be driven by a simple efficiency effect.

Maximizing EV profit and grid stability through Virtual Power Plant considering V2G

The electrification of transportation through the widespread adoption of electric vehicles (EVs) has raised substantial concerns within the realm of power grid operations. This concern predominantly stems from the elevated electricity demand brought about by the surging population of EVs, consequently exerting strain on the power grid infrastructure which can be reduced with vehicle-to-grid (V2G) technology integration. To address this issue, this paper delves further into the realm of grid integration by introducing a Virtual Power Plant (VPP) concept to enhance the synergy between EVs and power grid. This study aims to compare different realistic objectives, ranging from total active power loss and voltage drop minimization to EV profit maximization and then optimize the balance between the distribution grid power quality and VPP profit through bi-level modeling. The presented model is devised as mixed-integer quadratically constrained programming (MIQCP) and incorporates Temporal Convolutional Network (TCN) based forecasting to handle the uncertain behavior of the residential loads using historical data. The experiments are conducted in IEEE 33-Bus and real-world 240-Bus distribution networks. The results indicate that enabling bidirectional power flow between the grid and VPP can yield significant profits for EV users while only marginally impacting the active power loss, approximately around 5%. This validation underscores how V2G not only presents various advantages for power system operators but also benefits EV users simultaneously.

A profit-cost ratio maximization approach for a manufacturing inventory model having stock-dependent production rate and stock and price-dependent demand rate

This paper discusses an optimal managerial approach regarding stock control in a manufacturing-inventory scenario. The selling price and inventory level in showrooms may impact customers’ demand. The fall in selling price creates additional demand, while shown inventory also positively enhances demand. In this paper, demand is influenced by the selling price during the productive phase and the displayed stock in idle time. The significance of selling price and stock on profit goal may not be inherited from that of the demand function. The production rate varies negatively against the inventory on hand. Instead of taking cost minimization or the profit maximization objective, this paper executes an optimization approach on the profit-cost ratio function, sharpening the manufacturer’s goal. The numerical solution and sensitivity analysis on optimal outcomes succeed the analytical solution in Mathematica software. Numerical results indicate that the profit-cost ratio rises with selling price, suppressing the negative impact of the selling price on demand. Also, the profit-cost ratio shows a concave curve for the production cycle, ensuring a global maximum for the objective function.

Do Industries and Corporations of this Generation Align with the Thoughts of Milton Friedman on Corporate Social Responsibility?

This research paper delves into the alignment of modern corporate social responsibility (CSR) practices with Milton Friedman's theory that a corporation's primary responsibility is profit maximization for its shareholders. It examines the pharmaceutical, banking, and food and beverage sectors to assess Friedman's theory's relevance in the current business environment. Contrary to Friedman's 20th-century view that dismissed broader CSR obligations, the 21st century has witnessed a shift towards recognizing corporate duties toward society, the environment, and a broader stakeholder group. The study scrutinizes how these sectors balance profit goals with social responsibility, including sustainable investing, responsible lending, sustainable sourcing, waste management, and nutritional transparency. Employing a mixed-method approach with data from corporate reports and sustainability indices, preliminary findings indicate a spectrum of adherence to Friedman’s profit- centric model versus a comprehensive CSR approach. Results show that while profitability remains central, many corporations have integrated CSR into their business models to varying degrees, influenced by a complex mix of factors. This nuanced exploration provides insights into the evolving nature of CSR, offering a critical perspective on Friedman's relevance today and the degree to which corporations have embraced broader social responsibilities. It lays the groundwork for future research on the interplay between corporate profitability, shareholder value, and social responsibility, contributing valuable information for stakeholders, policymakers, and researchers interested in corporate ethics and sustainability.

Research on the Decision Making of Value Chain Reconstruction of Chinese Port Enterprises under the Background of Free Trade Zone Policy

This paper aims to solve the decision-making problem of value chain reconstruction of Chinese port enterprises under the background of the Free Trade Zone policy. Based on value chain theory and system dynamics method, this paper constructs a simulation model that can simulate the value-added change process of port enterprises under different combination input conditions. Furthermore, it conducts simulation case studies. The research indicates that the Free Trade Zone policy has a significant promoting effect on the restructuring of port enterprise value chains and the transformation and upgrading of enterprises. Moreover, considering the impact of the Free Trade Zone policy and limited resources, the overall benefits to port enterprises from combined investments are superior to those from single-factor investments. According to the value chain theory, the business segments of a port are decomposed into ancillary value activities, basic value activities, and external value activities. The investments in these three types of value activities play roles, respectively, in enhancing the operational efficiency of port enterprises, expanding the business scope of port enterprises, and strengthening the core competitiveness of port enterprises. From the overall perspective of the system, Shanghai Port can obtain the maximum operating profit when the endogenous factor input rate is 13%, the basic factor input rate is 4%, and the exogenous factor input rate is 13%. The findings of this research provide a decision-making reference for Chinese port enterprises to realize value reconstruction, transformation, and upgrading in the context of the Free Trade Zone policy.

A carbon neutral supply chain management by considering emission-risk minimization and green purchasing through optimal decision-making

This study addresses critical gaps in supply chain management (SCM) by integrating emission-risk minimization (ERM), green purchasing (GP), and profit maximization (PM). The research focuses on the optimal behaviors of manufacturers, agents, and retailers within the SCM framework to achieve carbon neutrality and reduce carbon dioxide emissions (CO2e). This study considers Guangdong province, China, a region facing challenges in optimizing energy systems and meeting CO2e reduction targets. Simulation-based optimization techniques within mathematical models are employed. A design of experiment (DOE) method was used to explore the dynamics of key variables in the SCM environment. Results reveal optimal behaviors for manufacturers, agents, and retailers, demonstrating the ideal values for profit and pricing decisions. Manufacturers optimize production quantity, achieving CO2e reduction and PM through ERM. Agents exhibit a strong commitment to GP practices, enhancing PM and carbon-neutral goals. Retailers get more PM than manufacturers and agents, contributing to a clean environment. Interestingly, retailers make contributions to the clean environment without considering ERM and GP in SCM. The study contributes novel insights by addressing the identified gap in SCM research, emphasizing the joint consideration of ERM, GP, and PM. This research assists manufacturers, agents, and retailers in terms of PM for economic objectives. It cleans the environment through carbon-neutral SCM in society.

Optimal economic and environmental arbitrage of grid-scale batteries with a degradation-aware model

Energy arbitrage is a potential revenue stream for battery operators with access to variable electricity prices. However, the power shifted by grid-scale energy storage has the potential to influence the production mix in real time, impacting the carbon emissions of the electricity system. Little research is available on the CO2 emissions induced by arbitrage operations, and studies that consider arbitrage-related CO2 emissions often neglect battery degradation. To address this gap, this study proposes a novel modeling and assessment framework based on mixed-integer linear programming to analyze the trade-offs between profit and CO2 emissions of battery arbitrage operations as well as the impact of degradation on arbitrage profit and emissions. We present the results in terms of Pareto-optimal solutions that identify maximum profit and minimum CO2 emissions. We illustrate our model through a case study in Germany and we show that performing maximum-profit arbitrage increases the system emissions by up to 7.5 tCO2 per MWh of storage capacity (or about 12% of battery life cycle emissions per year). 60% of the added emissions can be avoided by sacrificing only 1.5% to 2.7% of the net arbitrage profit, and CO2-neutral operation can be achieved by sacrificing about 7% of the profit. Our findings also highlight the importance of modeling battery degradation, as degradation-unaware arbitrage models may lead to a substantial profit loss (potentially to negative profits) and higher CO2 emissions (up to +260%) with respect to degradation-aware models.

PERANCANGAN DIGITAL MARKETING SEBAGAI STRATEGI PENGEMBANGAN USAHA SONGKET CEK ONAH PALEMBANG

Cek Onah songket business is one of the Palembang songket weaving craftsmen. So far, the products have only been marketed through word of mouth marketing and participating in exhibition activities as a promotional medium, so that the market share has not reached the expected target, and it is necessary to do online marketing as a promotional medium in order to introduce Cek Onah's songket products to the wider community. Instagram is a platform that can be used as a business development through digital marketing. This service aims to design digital marketing through Instagram and publish the results of Instagram content design as a promotional media for Cek Onah's songket business owned by Mrs.Hj.Cek Onah. The target achieved is to produce online promotional media in the form of Instagram post content such as feeds, video reels, and instagram stories to develop and reach a wider business market and can use it as an effort to increase digital marketing of songket woven fabric products so that sales turnover increases and maximum profits are achieved.

THE INFLUENCE OF PRODUCTION COSTS, PROMOTION AND SALES COSTS ON COMPANY PROFITS (Empirical Study of Manufacturing Companies in the Food and Beverage Sector Registered at BEI Period 2018-2021)

Net profit is the limited measure of a company's success. The main purpose of establishing a business or company is to make a profit. Planning and controlling profits is one of the roles of company management that is really needed, to generate maximum profits. The following are the factors that influence making a profit, namely production costs, promotion costs, and sales. This research aims to determine the influence of production costs, promotion and sales costs on company profits. In this research, the sample used is food and beverage sector manufacturing companies listed on the Indonesia Stock Exchange for the 2018-2021 period. The analysis method uses multiple linear regression analysis methods with a significance level of 5% or 0.05. The research results show that the production cost variable with a significance value of 0.000 < 0.05, the promotion cost variable with a significant value of 0.003 < 0.05 and the sales variable 0.000 < 0.05 affect the profits of manufacturing companies listed on the Indonesia Stock Exchange, this can be seen from the results of the t-test. The results of the f- test show that production costs, promotion costs and sales have a joint effect on company profits, the R2 value is 79,3%.
 
 
 Keywords: Production Costs, Promotion Costs, Sales, Profit

Optimizing Distributed Generation Placement and Profit Maximization in Active Distribution Networks with Harmonics: A GEPSO Approach

Optimizing Distributed Generation Placement and Profit Maximization in Active Distribution Networks with Harmonics: A GEPSO Approach

Enhanced Genetic-Algorithm-Driven Triple Barrier Labeling Method and Machine Learning Approach for Pair Trading Strategy in Cryptocurrency Markets

In the dynamic world of finance, the application of Artificial Intelligence (AI) in pair trading strategies is gaining significant interest among scholars. Current AI research largely concentrates on regression analyses of prices or spreads between paired assets for formulating trading strategies. However, AI models typically exhibit less precision in regression tasks compared to classification tasks, presenting a challenge in refining the accuracy of pair trading strategies. In pursuit of high-performance labels to elevate the precision of classification models, this study advanced the Triple Barrier Labeling Method for enhanced compatibility with pair trading strategies. This refinement enables the creation of diverse label sets, each tailored to distinct barrier configurations. Focusing on achieving maximal profit or minimizing the Maximum Drawdown (MDD), Genetic Algorithms (GAs) were employed for the optimization of these labels. After optimization, the labels were classified into two distinct types: High Risk and High Profit (HRHP) and Low Risk and Low Profit (LRLP). These labels then serve as the foundation for training machine learning models, which are designed to predict future trading activities in the cryptocurrency market. Our approach, employing cryptocurrency price data from 9 November 2017 to 31 August 2022 for training and 1 September 2022 to 1 December 2023 for testing, demonstrates a substantial improvement over traditional pair trading strategies. In particular, models trained with HRHP signals realized a 51.42% surge in profitability, while those trained with LRLP signals significantly mitigated risk, marked by a 73.24% reduction in the MDD. This innovative method marks a significant advancement in cryptocurrency pair trading strategies, offering traders a powerful and refined tool for optimizing their trading decisions.

Hub location with congestion and time-sensitive demand

This work studies the effect of hub congestion and time-sensitive demand on a hub-and-spoke location/allocation system. The Hub Location with Congestion and Time-sensitive Demand Problem is introduced, which combines these two main characteristics. On the one hand, hubs can be activated at several service levels, each of them characterized by a maximum capacity, expressed as the amount of flow that may circulate through the hub, which is associated with a hub transit time. On the other hand, alternative levels are available for served commodities, where each demand level is characterized by its amount of demand, unit revenue, and maximum service time. In this problem the efficiency of a hub-and-spoke system is given by the maximum net profit it may produce. To the best of our knowledge this is the first work where hub congestion and time-sensitive demand are jointly considered.Two alternative mixed-integer linear programming formulations are proposed. They include a new set of constraints, which are necessary to guarantee the consistency of the obtained solutions under the presence of the capacity-type constraints derived from hub service levels and served demand levels. The efficiency of the formulations is analyzed through a set of computational experiments. The results of the computational experiments allow to study the structure of the obtained solutions and to analyze how the different parameters affect them.

Effect of Legume-Legume Replacement Series of Intercropping System on Growth, Yield and Quality of Chickpea

Chickpea is major pulse crop cultivated in India. Area under chickpea cultivation has been increasing day by day. Although area of chickpea is increasing still the productivity per unit area is not increasing. There are many factor responsible for lack of productivity including crop failure. Therefore, increasing productivity and for avoiding the risk associated with complete crop failure, intercropping is the way forward. Intercropping of legume in association with non-legumes helps in utilization of nitrogen being fixed by legumes in the current growing season, but also helps in residual build-up of nutrients in soil. Best utilization of nutrients, moisture, space and solar energy can be derived through intercropping system. Intercropping provides an insurance against calamities and helps in the maximization of productivity and profit by efficient utilization of natural resources. Hence, crop intensification is both in space and time dimensions. Selection of compatible genotype and appropriate planting pattern helps in minimizing the inter-specific and intra-specific competition for resources thereby boosting up the productivity of the system as a whole. Therefore, a field experiment was conducted at College Agronomy Farm, B. A. College of Agriculture, Anand Agricultural University, Anand, Gujarat during two consecutive rabi season of the year 2019-20 and 2020-21. The experiment was laid out in Randomized Block Design (RBD), consisting of nine treatment. viz.; T1: sole chickpea, T2: sole linseed, T3: sole fenugreek, T4: (chickpea + linseed 2:1), T5: (chickpea + linseed 3:1), T6: (chickpea + linseed 4:2), T7: (chickpea + fenugreek 2:1), T8: (chickpea + fenugreek 3:1) and T9: (chickpea + fenugreek 4:2) with four replication. Legume intercropping resulted in better growth due to complimentary actions of the both the crop. The total equivalent yield obtained from intercropping far surpasses the yield of the sole crop in unit area.

DSO Market for Transactive Scheduling of Electric Vehicle Charging Stations in Constrained Hierarchical Power Distribution and Urban Transportation Networks

The trend toward the deployment of electric vehicle charging stations (EVCSs) equipped with on-site distributed energy resources indicates potential transactive energy market (TEM) operations among EVCSs. However, unlike regular prosumers, EVCS operations are influenced by both the power distribution network (PDN) and the urban transportation network. In this paper, we propose a three-stage approach for solving the hierarchical energy scheduling and trading problem of EVCSs. At the first stage, the day-ahead traffic assignment problem of the transportation system is solved to determine individual EVCSs’ charging load. At the second stage, EVCSs solve their profit maximization problem to determine their energy scheduling and market participation decisions. Lastly, at the third stage, TEM among EVCSs is cleared by the distribution system operator (DSO). With the DSO shouldering both grid operator and market operator functions, the DSO problem is formulated as an unbalanced three-phase optimal power flow (OPF) model integrating a double-sided auction mechanism. To further improve the PDN operation, grid features such as network reconfiguration and tap changers are introduced into the OPF model. The effectiveness of the proposed framework is proved through case studies.

Joint Demand Forecasting and Network Slice Pricing for Profit Maximization in Network Slicing

Joint Demand Forecasting and Network Slice Pricing for Profit Maximization in Network Slicing

MAKSIMALISASI KEUNTUNGAN DENGAN KENDALA FAKTOR-FAKTOR PRODUKSI PADA KEDAI KOPI BNO

<p><em>Kedai Kopi BNO is one of the coffee shop in Balikpapan City. However, in the last 3 months (February-April 2021) their income has decreased. It caused by the use of production factors that are less than optimal. The purpose of this study are to analyzed the maximum profit that can be obtained from the results of linear programming analysis, and also to analyzed the sensitivity to changes in variables from the results of linear programming analysis. This research was conducted in May 2021. Data processing is using Microsoft Excel Data Solver software. The result of linear programming analysis is that the maximum profit that can be obtained by Kedai Kopi BNO is Rp11,498,448.98 per month. Utilization of roasting coffee, mineral water, liquid sugar, and labor hours is optimal. Business actors can only change the value of profits and the amount of raw materials between the minimum and maximum limits so that the production activities could continue to run at the recommended optimum level.</em></p>

Leveraging hybrid ANN–AHP to optimize cement industry average inventory levels

In recent years, inventory has been critical due to the production cost and overstock risk related to the expiration date and the fluctuation price risk. This study's minimization of overstock and price fluctuation in the warehouse used a hybridized artificial neural network (ANN) and analytical hierarchy process (AHP) to produce an optimum model. The variables, such as average demand, reorder point, order quantity, factor service level, safety stock, and average inventory level, were used to obtain the optimal condition of the average inventory levels to maximize the profit. Then, the type of inventory system that guarantees the minimum risks in managing the inventory would be selected. The result shows that the data has a mean of 39.2 units, and the standard deviation (SD) was 12.9. This means that the order quantity is 20.2 units, the average inventory level is 57.3, and the average demand is 39. These conditions used the factor z, which is 97% service level. This study concludes that the optimum average inventory level is 91 units, the order quantity is 11 units with the maximum average profit is $1098, and the peak fluctuation condition maximum profit is $1463 when the average inventory level is 7.3, and the inventory policy system used to minimize the risk is the continuous review policy type. The study could be beneficial to reduce production costs and enhance overall profitability and operational efficiency in the sector by mitigating the risks associated with excessive inventory and price volatility while also minimizing the potential for expired inventory.

Investment Portfolio Optimization Using Black-Litterman Model in Smart Carbon Economy Transition

An optimal investment portfolio needs to be formed before an investor invests because it can help investors determine which financial instruments are suitable to choose in order to get the maximum return or profit and the minimum level of risk. In the current situation, where there is an economic transition to a smart carbon economy or low carbon economy, it is necessary to form the optimal portfolio of stocks to facilitate investors in making investments. The purpose of this study is to form the optimal investment portfolio using the Black-Litterman model in a smart carbon economy. The data used is stock data from 24 companies listed on the LQ45 Low Carbon Leaders index for the period 2022-2023. Based on the research results, the Black-Litterman model generates the optimal portfolio with a 0.1% expected return. Thus, the optimal portfolio results with the Black-Litterman model are estimated to generate a profit of 0.1% for smart carbon stock data listed on the LQ45 Low Carbon Leaders index for the 2022-2023 period.