Incremental Profit Research Articles

Anti-interference Technology of Intelligent Communication Based on Improved GA and GWO

Chaotic mapping enhances anti-interference in frequency hopping communication by optimizing genetic algorithm population initialization. An intelligent decision engine model employs optimized grey wolf parameters and individual exchange mechanisms, enhancing grey wolf optimization convergence speed for constructing frequency hopping patterns. Experimental data reveals the improved genetic algorithm's efficiency with average run times of 0.312s and 0.057s for adaptive and enhanced versions, respectively. Achieving optimal solution convergence rates of 99.3% and 100%, the enhanced algorithm boosts decision-making accuracy and efficiency. The intelligent decision engine exhibits strong anti-interference capabilities, suitable for −2dB to 10dB signal-to-interference ratios with error rates below 10−6. The improved grey wolf optimization algorithm surpasses traditional approaches, yielding a 9.5% profit increment with a total value of 2310. This technology showcases adept learning and anti-interference capabilities, offering innovative solutions for communication systems and anti-interference technology advancements.

Optimization of L‐sorbose allocation for 2‐keto‐L‐gulonic acid production

AbstractA fuzzy logic‐based optimal scheduling strategy was used to maximize the profitability of the 2‐keto‐L‐gulonic acid (2‐KGA) production process. In this work, profit function is predicted by rolling learning‐prediction (RLP) based on support vector machine (SVM). By combining the optimal scheduling strategy, the batches are online‐classified according to the results of the profit function. The fermentation time of secondary feeding was predicted by fuzzy logic method. Through three consecutive months of experiments, 90 historical 2‐KGA batches were run to verify the optimal scheduling method. The total profit increment of 749,630 PU is obtained for the workshop. The results indicate that 5.25% increase in total workshop profit compared to the empirical model.

Cooperative Recycling Strategy for Electric Vehicle Batteries Considering Blockchain Technology

The rapid growth of new energy vehicle has led to an urgent need for efficiently recycling used power batteries. In addition, battery suppliers can adopt blockchain technology to enhance efficiency in recycling. This article constructs a closed-loop supply chain (CLSC) consisting of a power battery supplier, a new energy vehicle manufacturer, and a third-party recycler, and studies the optimal collaborative recycling mode considering blockchain adoption and recycling competition. Firstly, we identify five alliance recycling models and derive the equilibrium results for each model. Secondly, we use the Shapley value mechanism in cooperative game to allocate alliance profits reasonably. Some interesting conclusions are obtained. (1) The supplier-manufacturer alliance is the optimal recycling strategy under moderate recycling competition, which challenges the common assumption that the major-alliance dominates others in CLSC. (2) Alliances, especially manufacturer-3rd party alliance, are not always beneficial to the CLSC, but are beneficial under low recycling competition and blockchain cost. (3) The proposed four stable profit allocation cases can coordinate the CLSC, where all alliance members achieve higher profits than the decentralized model, with the supplier gaining the largest profit share and the manufacturer obtaining the most profit increment.

Optimal Bidding Strategies for Wind-Thermal Power Generation Rights Trading: A Game-Theoretic Approach Integrating Carbon Trading and Green Certificate Trading

In response to the challenges of low wind power consumption and high pollution emissions from thermal power, the implementation of wind-thermal power generation rights trading is a proactive attempt to reduce wind power curtailment and promote its consumption. This study first regards the alternating bidding process between the two parties as a dynamic game, using the Rubinstein bargaining game model to determine the incremental profit allocation and optimal bidding for both parties in power generation rights trading. Secondly, an energy conservation and emission reduction model is constructed to analyze the benefits from the perspectives of standard coal consumption saving and the carbon emission reduction caused by power generation rights trading. Finally, a combined trading revenue model is established to analyze the final profit of both parties involved in the trading. The results show that the combined trading of wind-thermal power generation rights, incorporating carbon trading and green certificate trading, can effectively promote coal consumption savings in thermal power units and reduce the carbon emissions of the power industry. Moreover, it significantly increases the final profit for both parties, stimulating the enthusiasm of generators for participating in power generation rights trading, and ultimately promoting wind power consumption.

Product-specified dual-channel retail management with significant consumer service

If the product has been decided for the retail industry, single-channel retail management is enough to obtain profit for the retail management. Though a single-channel is enough, a dual-channel may provide much more benefit than a single-channel. If the single-product is to be specified, then a specific type of dual-channel can be improvised for that. A product-specific dual-channel strategy of a manufacturing firm is observed in this study for consumer non-durable goods. The manufacturing firm utilizes technical support to provide essential consumer services to consumers for non-durable goods. As consumer non-durable goods are fast consuming products, consumers buy these products more than once within a certain duration. This makes the consumer non-durable goods is an essential products within that certain duration. Due to multiple usage within a certain duration, consumers prefers to choose non-durable goods based on the quality and price of those products. The global maximum profit of the dual-channel is found by analytic method and established by numerical solutions. Results prove that internet of things-based blockchain essential consumer service increases the total profit of the manufacturing firm by 29.12% than no essential consumer services-based manufacturing firm. Six numerical experiments and four special cases are provided to establish the theoretical model. The sensitivity analysis is performed using profit increment. Essential consumer service is the most sensitive to the total profit of the manufacturing firm whereas the holding cost is the second most sensitive parameter. More increment in holding cost causes profit to decrease by 16.98%.

Is One Head Better Than Two? Dual Leadership and Firm Performance During the COVID‐19 Crisis

We provide novel evidence on the value of combined CEO and board chair positions (CEO duality) for firms during the COVID‐19 pandemic in 2020. Based on 4,840 firm‐quarter observations from 1,210 unique firms in the US, we show that CEO duality firms outperformed non‐duality firms by a 0.58% margin in quarterly return on assets in 2020, which is equivalent to an incremental annual net profit of US$164 million. A difference‐in‐difference estimation confirms that the benefit of CEO duality is observed only in the COVID‐19 crisis period. Our main finding is robust to potential endogeneity concerns and alternative performance measures. Additional analyses show that the positive impact of CEO duality stems from the mechanisms of operating cost savings and working capital optimization during the crisis. Our finding underscores the benefit of CEO duality when economic uncertainty is high and a speedy decision is important.

Foreign direct investment and domestic innovation: Roles of absorptive capacity, quality of regulations and property rights.

Foreign Direct Investment is theoretically expected to facilitate the transfer of knowledge from the home country to the host country, however, the empirical evidence on the subject is mixed. Some studies have shown that, on one hand, as competition grows, the incentive to innovate reduces with the decrease in monopoly rents (Schumpeterian effect). On the other hand, market competition can also boost investments in R&D activities incentivized by incremental profits (Escape-Competition effect). Therefore, this study aims to explore which of these two effects dominates in the selected group of countries. This study also identifies the moderators of the relationship between FDI stock and domestic innovation. It examines the role of absorptive capacity, quality of regulations, and property rights protection in the innovative activities of the host countries. Generalized Method of Moments is used to estimate the parameters of the multivariate regression equation. The analysis is based on panel data consisting of 49 countries over 14 years. The results show that FDI has a negative relationship with domestic innovation, indicating the presence of the Schumpeterian effect. The extensions of the main models show that FDI positively affects domestic innovation in countries with higher absorptive capacity, the superior quality of regulation, and stronger protection of property rights. This study shows that the positive relationship between FDI and domestic innovation is conditional on the ability to absorb knowledge and quality of governance in the recipient countries.

Real Competition and Profit Rate Equalisation: Theory and Evidence from the Norwegian Economy

The existence of long-run profitability differentials in market economies is a fundamental question that all traditions of economic thought since the times of the classical economists have dealt with in some form or another. In consideration of the modern synthesis of the classical views on competition into what has come to be known as the Theory of Real Competition, this article presents both a consistent theoretical basis from which to argue in favour of the tendential elimination of profitability premia across industries, as well as empirical evidence pointing to such conclusion from the Norwegian economy. The data, which covers 26 industries across nearly five decades (1971–2017), is used to estimate average and incremental total long-run profit rate differentials following two distinct methodologies. The first method has been commonly used in the prior literature, and the second has been newly systematised by this study’s author. Both methods are critically scrutinised, and their results compared to allow for a more robust conclusion. The findings suggest that average profit rates exhibit a weak tendency towards equalisation in Norway, and that such tendency is much stronger for incremental profit rates.

Model and economic analysis of hydrogen consumption from hydropower considering storage capacity

Abstract Due to the characteristics of clean and efficient hydrogen energy and its wide application, hydropower hydrogen production has become one of the potential technical means to reduce the new energy abandonment rate, and its economic benefits are analyzed in this paper. After exploring the operation characteristics of hydropower plants, the paper examines the characteristics of water and energy abandonment in combination with the calculation method of hydropower plant power abandonment, mathematically models the relevant equipment of hydropower hydrogen generation and consumption mode, and establishes a hydrogen storage and consumption model considering the capacity of storage. Based on the economic index of hydropower hydrogen production, the storage capacity of the hydropower hydrogen consumption mode is configured by combining the two-layer planning algorithm. Finally, a simulation experiment is carried out to analyze the hydropower hydrogen consumption model established in the paper and explore the optimal storage settings for hydropower hydrogen consumption. The results show that the optimal energy storage capacity of the demonstration base is 1MWh kWh cost of electricity is 0.113 yuan/kWh respectively, the whole life cycle NPV is the largest, is 7.39×106 yuan, the daily profit including hydrogen sales, electricity sales, and energy sales is about 463,110,000 yuan, and the incremental daily profit is about 144,980,000 yuan. Based on this study, we aim to promote the sustainable development of the new energy economy.

The Determinants and Effect of the Incentive Intensity: Empirical Evidence from Japan

This study examines various determinants of incentive intensity and the moderating effect of risk aversion on the relationship between incentive intensity and organisational performance. Prior studies have reported mixed results concerning the determinants of incentive intensity, and few studies have examined the moderation effect. We analysed empirical data from a cross-sectional survey of 600 Japanese organisations that manage foreign subsidiaries. The principal and agency structure can be seen in both participants (HQ and a foreign subsidiary) with the agency problem. The first determinant is environmental uncertainty, which is investigated in two types of uncertainties: market and general business environmental uncertainties. The findings suggest that general business environmental uncertainty is negatively associated with incentive intensity, but the negative effect of market environmental uncertainty on it depends on the prospect of incremental profits. The rest of the determinants are derived from the incentive intensity principle, including some features of management accounting systems. In this study, the effects of the determinants are supported as expected in principle. In particular, the incentive intensity is influenced by the prospect of incremental profits, an agent’s risk preference, and their responsiveness to incentives. As for the moderation effect, the positive effect of the incentive intensity on the performance is decreased by an agent’s risk aversion. Our empirical results explain mixed evidence in previous studies and are consistent with the agency theory.

Construction of a multi-technology fusion e-commerce data transaction optimization model based on federated learning

Abstract The constraints of data protection make the data confined to different enterprises and organizations, forming many “data islands” and making it difficult to bring out the important value it contains. In this paper, we use federated learning technology as the service foundation and introduce differential privacy, federation chain, interstellar file system, and trusted execution environment to construct a multi-technology fusion e-commerce data transaction method. The three concepts of budget feasibility, individual rationality, and incentive mechanisms are applied to the data transaction scenario to design smart contracts. At the same time, the incentive mechanism is created by combining the trusted execution environment and Shapley value, and the transaction process model is optimized. Simulation comparison is carried out based on the public dataset of the Taobao e-commerce platform, and the experimental results show that MTFDT can realize the accurate evaluation of the model training effect, and the incremental profit stabilization point of the data buyer and seller is around 0.4, which improves the fairness of benefit distribution.

Industrial Data-Driven Processing Framework Combining Process Knowledge for Improved Decision-Making—Part 2: Framework Application Considering Activity-Based Costing Concepts

Operating time series data collected and stored in historian must be managed to extract their full potential. Part 1 of this paper proposed a structured way (a sophisticated approach) to process industrial data; this first part explains in detail the data processing framework used as the basis for the costing analysis present in the second part of this series. The framework considers the analysis scope definition, data management steps, and operating regimes detection and identification. The added value of this proposed framework is demonstrated in Part 2 via the use of cost accounting for operational problem-solving (debottlenecking), i.e., its practicality is validated via its application alongside a cost analysis on the brownstock washing department of a kraft pulp mill. The traditional debottlenecking approach assumes a single operating condition considering that operating regimes allow for a much more sophisticated debottlenecking study of the washing department. With the use of operations-driven cost modeling (contingent on activity-based costing concepts) and processed time series data corresponding to steady-state operation, incremental profit can be assigned to each operating regime in order to identify the most cost-efficient one. The overall objective of this two-part series is to convert processed industrial steady-state data and cost information into knowledge that can be used to optimize the washing department of a chemical pulp mill. More specifically, different operating regimes are assessed, and the most suitable operating strategy is defined. The application of activity-based costing on a large amount of historically processed industrial data led to the improvement in the operation. The identified optimal way to operate (pulp throughput, pulp conductivity, defoamer and bleaching chemical quantity, etc.) led to a profit of CAD 49 M per year. Lastly, a contribution analysis of the regimes based on PCA highlighted how the process was operated when the preferred performances happened.

Incremental Profitability Evaluation of Vehicle-to-Grid-Enabled Automated Frequency Restoration Reserve Services for Semi-Public Charging Infrastructure: A Case Study in Belgium

The current paper defines a framework for the introduction of automated frequency restoration reserve services, enabled by vehicle-to-grid technology, into the business model of an entity owning and operating a network of semi-public Electric Vehicle Supply Equipment. It assesses the profitability of this introduction by performing a case study based on the real-life electric vehicle charging data from the EVSE network located in a hospital parking lot. From the results of the study, it is clearly visible that the introduction of vehicle-to-grid-enabled automated frequency restoration reserve services has a significant positive incremental profitability; however, this is heavily dependent on the plug-in ratio of the charging network, determined by electric vehicle users’ behavior.

Product Customization Schemes and Value Co-Creation with Platform-Driven C2M Model

Taking advantage of the market, e-commerce platforms can help manufacturers produce tailored products to meet customer needs, referred to as the Customer-to-Manufacturer (C2M) model. This paper examines how a manufacturer can collaborate with an e-commerce platform to offer custom products and achieve value co-creation. Implementing the C2M model is not viable when product valuation or consumers’ fit sensitivity is low. We specify four optimal customization schemes for implementing the C2M model. We find that there exist three effects when a manufacturer transits to implementing the C2M model, i.e., the price-premium effect, market-expansion effect, and the substitution effect. We reveal that implementing the C2M model will increase retail price (price-premium effect) but may not increase market demand (market-expansion effect). Under some conditions, implementing the C2M model will require the manufacturer to only produce custom products and stop producing standard products (substitution effect). Although the maximum total profit can be achieved from a supply chain perspective, the manufacturer gets its preservation profit while the platform takes up all the incremental profit, which leads to unsustainable cooperation. We propose a cooperative scheme considering the platform’s and the manufacturer’s resource contribution when offering custom products. Our managerial insights will facilitate the stakeholders in implementing product customization with the C2M model.

Efficient approach for mining high-utility patterns on incremental databases with dynamic profits

High-utility itemset mining (HUIM) is one of the heavily studied fields of data mining, which is due to its high compatibility with real-world applications. HUIM is a process of extracting a complete set of interesting patterns by considering the importance and the quantity of each item. Incremental high-utility itemset mining (IHUIM) further increases the compatibility of HUIM by mining from a data stream instead of a static database. It does this by mining interesting patterns in a single database scan and storing prior knowledge in a compact data structure. However, there is a critical limitation, because the importance of each item has to be guaranteed to remain unchanged throughout the data stream. This limitation hinders the compatibility of IHUIM to the real-world applications, because the importance of an item changes from time to time in the real world. Conventional IHUIM approaches consequently have to use outdated information when mining interesting patterns. This paper proposes a novel problem of mining high-utility itemsets in an incremental and dynamic profit environment in order to account for the fluctuation of the item's importance. Furthermore, a novel approach to mining patterns in this type of environment is introduced using an efficient list structure and tight upper bounds. Experiments on real and synthetic datasets show that the proposed approach performs well compared to the state-of-the-art approaches in terms of the runtime and memory usage, and it scales better than the other approaches.

A robust optimization model for multiple electricity retailers based on electricity trading in the presence of demand response program

This paper focuses on the short-term cooperation between the electricity retailers at distribution network level and proposes an electricity trading based cooperative mechanism to manage the cooperation between retailers. Under the cooperative mechanism, the objective of the retailers who are engaged in cooperation is to maximize their overall profit through appropriate electricity trading. The profit maximization problem for the retailers is formulated as a robust optimization model and solved via heuristic algorithms. The profit increment brought by electricity trading is allocated according to the proposed profit allocation method. The simulation results demonstrate that the proposed mechanism can effectively increase the overall profit of the retailers. Compared with the non-cooperative case, the cooperative case can increase the overall profit of 427.34 $ at best. Moreover, the results demonstrate that the number of the retailers involved in electricity trading has a positive impact on overall profit increment. The results also reveal that each retailer can achieve profit growth even under the most unfavorable conditions by adopting the profit allocation method.

Modelling the effects of metro and bike-sharing cooperation: Cost-sharing mode vs information-sharing mode

Bike-sharing provides a key role in offering a solution to the first- and last-mile problem, and thus extends the service catchment areas of public transport and facilitates low-carbon travel in urban transport systems. Despite the fact that it has become popular for metro and bike-sharing operators to cooperate to improve their services in recent years, the effect of cooperation modes has largely been neglected in the literature. We thus develop a metro-lead Stackelberg game model to investigate the strategic interaction and potential integration between a bike-sharing operator and a metro operator under different cooperation modes; i.e., the cost-sharing mode (CS mode) and the information-sharing mode (IS mode). We analytically and numerically examine the impact of the CS and IS modes on the participants’ pricing and effort decisions. This paper highlights the value of a carefully designed cooperation mode on benefiting operators (profits) and society (demand for green travel). Specifically, the IS mode enables both operators to gain a profit increment, leading to a win-win situation from the profit point of view. This superior performance of the IS mode remains robust, regardless of whether the bike-sharing operator is fairness-neutral or fairness-concerned. The CS mode benefits the metro operator yet hurts the profitability of the bike-sharing operator. Interestingly, the CS mechanism can effectively promote the demand for connecting travel, which might explain the adoption of the CS mode by bike-sharing operators in practice. That is, the increased demand created by the CS mode caters to bike-sharing operators whose primary goal is to capture markets, commonly for new entrants. Essentially, the IS mode also has a positive effect on increasing demand, and the dominance of these two cooperation modes regarding the promotion of demand is sensitive to the cost efficiency of each operator. Additionally, we find that a greater degree of fairness concern weakens (strengthens) the superiority of the IS (CS) mode in both demand promotion and profit improvement. Our results provide novel insights into how cooperation mechanisms impact the integrated operation of metro and bike-sharing systems.

Developing a Crop Water Production Function for Alfalfa under Deficit Irrigation: A Case Study in Eastern Colorado

Recent Colorado, USA water law provisions allow a portion of irrigation water to be leased between agricultural and other users. Reducing consumptive use (CU) through deficit irrigation while maintaining some crop production could allow farmers to earn revenue from leasing water rights. This observational study aimed to determine if deficit irrigation of alfalfa (Medicago sativa L.) can be used to reduce CU, provide parameters for an alfalfa crop water production function (WPF), and evaluate the potential for improved farm income by leasing water. Soil water balance, evapotranspiration (ET), and dry matter yield from eight commercial fields (1.70 to 2.14 ha zones), growing subsurface drip-irrigated alfalfa, were monitored for five seasons (2018–2022) at Kersey, Colorado. Four irrigation treatments [Standard Irrigation (SI) = irrigate when soil water deficit (D) exceeds management allowed depletion (MAD); Moderate Deficit Irrigation (MDI) = 70% of SI; Severe Deficit Irrigation (SDI) = 50% of SI; and Over Irrigation (OI) = 120% of SI] were applied, with two zones per treatment. Reductions in CU ranged from 205 to 260 mm per season. The shape of the alfalfa WPF (dry biomass yield vs. ET) was concave, indicating that water use efficiency (WUE) could be optimized through deficit irrigation. The average WUE was 0.17 Mg ha−1 cm−1 and tended to increase with greater deficits. Deficit irrigation also increased the relative feed value. If conserved CU from deficit irrigation can be leased into a transfer water market, farmers could profit when the water lease revenue exceeds the forgone profit from alfalfa production. We found incremental profit from deficit irrigation and water leasing to be positive, assuming 2020 prices for hay ($230 bale−1) and water prices above $0.50 m−3.

General Motors Optimizes Vehicle Content for Customer Value and Profitability

General Motors (GM) vehicles have more than 100 customer-facing features, known as vehicle content. Decisions about how to package and price these features have a significant impact on our customers’ experiences and on GM’s business results. Vehicle features are assigned as standard, optional, or unavailable on different trim levels, resulting in an enormous combinatorial solution space. Vehicle content optimization (VCO) combines customer market research, discrete choice models, and custom multiobjective nonlinear optimization algorithms to optimize vehicle contenting and pricing decisions. VCO comprehends complex dynamics and tradeoffs and allows GM to optimally balance customer preferences and profitability. After six years of development and multiple proof-of-concept and pilot studies, VCO was officially integrated into GM’s Global Vehicle Development Process in 2014. As of 2021, VCO has been used on more than 85 vehicle programs globally. It has enabled customer-centric product development and more efficient engineering, sourcing, and manufacturing. GM Finance verified that VCO enabled $4.4 billion of incremental profit over the average product life cycle (i.e., six years on average) since 2018, making it a vastly impactful example of operations research and applied analytics.

Vehicle routing problems with drones equipped with multi-package payload compartments

The vehicle routing problem with drones (VRP-D) consists of designing combined truck-drone routes and schedules to serve a set of customers with specific requests and time constraints. In this paper, VRP-D is extended to include a fleet of drones equipped with multi-package payload compartments to serve more customers on a single trip. Moreover, a drone can return to a truck, different from the one from which it started, to swap its depleted battery and/or to pick up more packages. This problem, denoted as VRP-D equipped with multi-package payload compartments (VRP-D-MC), aims to maximize total profit. In this work, an adaptive multi-start simulated annealing (AMS-SA) metaheuristic algorithm is proposed to efficiently solve this problem. Experimental results show that the proposed algorithm outperforms the current state-of-the-art algorithms for VRP-D in terms of solution quality. Extensive analyses have been conducted to provide managerial insights. The analyses carried out show (i) the benefits of using drones equipped with different compartment configurations, (ii) the incremental total profit obtainable using a combined truck-drone fleet rather than a fleet of trucks, (iii) the benefit of swapping drone battery while picking up the items to deliver, and (iv) the impact of the packages load on the consumption energy of battery drone. It is also demonstrated that the different intensification and diversification mechanisms adopted improve the convergence of the traditional SA.