Profit Constraint Research Articles

Determining the Appropriate Minimum Effort Levels for Use in Fisheries Dynamic Bioeconomic Models

Managing fisheries to achieve ecological, economic and social sustainability is complex. The use of dynamic bioeconomic models can be and have been used to assist in determining management targets. However, optimizing profits over time can result in large reductions in fishing effort in the short term with adverse social consequences. There exist other benefits from maintaining fishing effort even in adverse conditions (e.g., maintain crew and fleet capacity). For this reason, many bioeconomic models have included some form of minimum effort, catch or short-term profit constraint. In this paper, we consider a range of approaches to assess an appropriate minimum fishing effort, including the estimation of fishery breakeven effort levels, and approaches based on historical fishing levels. These are tested using a bioeconomic model currently used for fishery management. We find that breakeven approaches tend to result in the most conservative effort levels and the highest net present value of profits. In contrast, using a proportion of the moving average of the observed fishing effort results in less conservative change in effort, while resulting in positive changes in the net present value of fishery profits. The approach also has the advantage of being dynamic, adjusting with recent fishery conditions.

Measuring Accessibility and Optimising Logistics of Marginal Land Grass Biomass in the Case of Northern Ostrobothnia, Finland

Abstract Grass biomass originating from marginal agricultural land unsuitable for food production can be utilised in variety of bio-based productions. The key profitability constraints for such productions are regional availability and transport costs. The aim of this paper is to methodologically test open access agricultural field parcel data – enabled by the EU INSPIRE directive – integrated with the road network in measuring biomass accessibility and optimising collection and transport using geographic information system (GIS) technology in two Finnish case areas. The analytical framework applies closest facility and vehicle routing analyses for tractor and truck transport scenarios. The key finding is that the area of field parcels and the amount of biomass accumulating within an observed area are not the only important factors influencing transport efficiency: cost-effectiveness of collection routes can be equally high in regions with scarce and abundant biomass areas.

On the welfare effects of adverse selection in oligopolistic markets

We consider a principal-agent relationship with adverse selection. Principals pay informational rents due to asymmetric information and sell their output in a homogeneous Cournot-oligopoly. We find that asymmetric information may mitigate or more than compensate the welfare reducing impact of market power, irrespective of whether the number of firms is given exogenously or determined endogenously by a profit constraint. We further show that welfare in a setting with adverse selection may be higher than the maximized welfare level attainable in a world with perfect observability.

Research on a mobile terminal-based control platform for the delivery and management of power material transportation

Some of the power material transportation delivery control platforms have the defect of too small throughput in the actual application scenarios, therefore, a mobile terminal-based power material transportation delivery control platform is designed. The hardware part: the mobile terminal is used to arrange the necessary equipment for operation monitoring, such as monitoring screens and servers, and is connected to the industrial control mainframe through a USB connection cable and to GPS devices; the software part is on the basis of the established reserve strategy, the process of power material transport delivery control is improved, the number of profitable stored within the cross item is calculated for each demand entry and the corresponding inventory entry, and the balance profitability constraints are extracted, isolating the base stations and optimizing the system’s intelligent inventory function. Test results: The power material transport delivery control platform in the paper has a higher average throughput than the other two power material transport delivery control platforms: 26.344req/s and 24.495req/s respectively, indicating a higher practical value of the designed power material transport delivery control platform after making full use of mobile terminals.

Determination of Truck–Shovel Configuration of Open-Pit Mine: A Simulation Method Based on Mathematical Model

The truck–shovel system is the most common material transportation system in open-pit mines. The configuration of trucks and shovels directly affects the efficiency and cost of transportation in open-pit mines. Under the condition that the types and quantities of trucks and shovels are known, in order to obtain the optimal configuration scheme in the open-pit mine transportation system this paper presents a method to determine the optimal scheme by conducting experiments based on the simulation truck–shovel system model in Flexsim software. We test candidate configuration schemes that are solved by the mathematical model with daily minimum production and expected profit constraints in the simulation model, and finally obtain the optimal truck–shovel configuration scheme that meets the ore output requirements of each loading point. Through simulation experiments, the daily production of the optimal truck–shovel configuration scheme is 3.75% higher than that of the original mine scheme and the profit is increased by 3.85%. The results show that the open-pit truck–shovel system constructed by Flexsim has great research potential and value for the optimization of truck–shovel configuration schemes.

Market-based hosting capacity maximization of renewable generation in power grids with energy storage integration

In an attempt to achieve net zero, the operation and planning of the energy system face techno-economic challenges brought by integrating large-scale distributed energy resources (DERs) with low carbon footprints. Previous work has analyzed the technical challenges including hosting capacity (HC) for DERs. In light of the deregulation of the power industry and the transition to power system with renewables at its center, this article takes the lead to maximizing renewable integration in power grids from a market viewpoint. It solves a significant problem brought forth by the fall in electricity prices, resulting from increasing renewable penetration that jeopardizes investment cost recovery and prevents sustainable grid integration of renewables. To this end, a novel bi-level optimization model is formulated, where the upper-level problem aims to maximize the HC of renewables ensuring the recovery of investment, and the lower-level problem describes the market clearing process considering network constraints. The optimal solution of devised bi-level problem can be found after reformulating it to a single-level mixed-integer linear problem (MILP) using the strong duality theorem and a special ordered set-type 1 (SOS1) founded linearization approach. Case studies confirm the significance of the devised model and quantitatively analyze the impact of different network capacities, renewable subsidies, and energy storage, respectively, on the market-based HC obeying its profitability constraint.

Optimization of Portfolio of federal loan bonds and RE-PO trades

Within the framework of the considered model, the Investor can make transactions for the buying and sailing of federal loan bonds (OFZ), as well as direct and reverse REPO deals secured by OFZ. Transactions are made for liquidity management and increasing interest income. This paper discusses the problem of constructing an optimal portfolio of such transactions. The paper considers the approach for the generation of scenarios for OFZ price changes, the mathematical formulation of the optimization problem, the assessment of its dimension depending on the number of assets and the number of scenarios, numerical experiments on historical data and the construction of an efficient portfolio frontier. The generation of scenarios for OFZ price changes is implemented using historical modeling of a parametric zero-coupon yield curve. The optimization problem criterion is the conditional value at risk (CVAR) risk measure. Constraints on the average return and self-financing of the portfolio are taken into account. As a result, the method of portfolio rebalancing without additional investment, the purpose of which is to minimize risk for a given profitability, is proposed. Numerical experiments are based on historical data about active traded OFZ in 2014-2020. The model is close to the real world: it takes into account commissions, repo discount, bid-ask spreads, trade volumes. Numerical results show that a trading strategy based on the introduced model is more profitable on average than investments in individual OFZ with comparable risk. Note that this effect is shown for strategies with high average profit constraints.

Profit Maximization Model with Fare Structures and Subsidy Constraints for Urban Rail Transit

This paper analyzes government subsidies based on the service design (i.e., headway) and fare structures of an urban rail transit system while considering necessary financial support from the government. To capture the interactions among the operator performance, government subsidies, and passengers in an urban rail transit system, a profit maximization model with nonnegative profit constraint is formulated to determine the optimal fare and headway solutions. Then, the social welfare that results from the operator profit maximization model is analyzed. Finally, a numerical example from Changsha, China, is employed to verify the feasibility of the proposed model. The major results consist of optimized solutions for decision variables, i.e., the fares and train headways, as well as subsidies to the operator. The fare elasticity factor under two fare structures significantly affects fares and demand. As the fare elasticity factor increases, the social welfare gradually decreases and a deficit occurs at low fares and demand, while subsidies rise from 0 to ¥24658.00 and ¥38089.16 under the flat fare and distance-based fare structures.

Financial Prominence and Financial Conditions: Risk Factors for 21st Century Corporate Financial Securities Fraud in the United States

Following the global financial crisis wrought by fraudulent activities of prominent US companies and considering the field’s lack of understanding about precursors to such vast corporate illegalities, this study measured firm prominence and financial conditions to identify corporate-organizational factors related to twenty-first century corporate financial fraud. We compiled data on 250+ US public companies involved in corporate securities frauds identified in 1,000+ Securities and Exchange Commission filings over 2005–2013; we randomly selected a comparable control group of 500+ US public companies from Compustat. Based on logistic multivariate regression analyses, marginal profitability, a strong growth imperative, and firm prominence were significant fraud risk factors. Prominent Fortune 500 firms were more susceptible to marginal profitability and/or strong growth-opportunities as risk factors. Findings were robust to various empirical measures and additional controls for undetected fraud. Anomic growth pressures and profit constraints, especially among America’s most prestigious firms, were linked to corporate securities fraud.

Application Loading and Computing Allocation for Collaborative Edge Computing

The emergence of Mobile Edge Computing (MEC) provides a near-field tasking platform to meet the latency requirements of the growing number of compute-intensive mobile applications. However, physical memory constraints limit the number of application services that can be loaded on the edge server (ES) simultaneously, and the non-uniform distribution of traffic in the mobile network makes it difficult to fully utilize the resources in the edge network. In addition, for MEC platform operators, how to achieve the expected profit of application service providers (ASP) to attract more ASPs is also important. To address these issues, in this paper we propose an ASP profit-aware solution for jointly optimizing application loading, task allocation, and compute resource allocation across multi-ES, minimizing system latency while maintaining ASP profitability. We first formulate the problem as a long-term stochastic optimization problem with ASP profit constraints, transform it into a single time slot optimization problem using the Lyapunov optimization framework, and then, using the power of genetic algorithms (GA), we propose an online heuristic algorithm to obtain a near-optimal strategy for each time slot. Simulation results show that our algorithm is effective in reducing system latency in the long term, while demonstrating performance that ensures more ASPs with desired profits.

Contract vs. recruitment: Integrating an informal waste merchant to a formal collector for collection of municipal solid waste

Due to environmental and social issues caused by unauthorized processing of recyclables, waste merchants, key informal collectors with the door-step collection in China have been integrated into formal collectors. However, how to achieve effective integration is still underexamined. This study puts forward two strategies for integrating a waste merchant from a formal collector’s perspective, namely, contract strategy (CS) and recruitment strategy (RS). A leader-follower game model is developed to explore and compare the suggested integration strategies considering the minimum profit constraint of a waste merchant. On this basis, the feasible boundary for the selection of a reasonable strategy by the formal collector is identified. The results show that the formal collector remains reluctant to integrate the waste merchant through RS or CS if the created value by the recyclables is low. The likelihood of selecting RS increases with the higher reclaimed value. Reduced hassle costs for residents can expand the boundary of selection for RS. Besides, it is observed that CS has a greater chance when the ratio of the collection cost coefficient under RS to CS increases. In particular situations, the formal collector may gain a higher profit using RS, although it may not definitely result in higher collection quantity compared to the situations where CS is applied. Overall, this study provides insights for formal collectors for the selection of an effective integration strategy.

Competition between Public and Private Maternity Care Providers in France: Evidence on Market Segmentation.

The purpose of this paper is to investigate the potential for segmentation in hospital markets, using the French case where private for-profit providers play an important role having nearly 25% of market shares, and where prices are regulated, leading to quality competition. Using a stylized economic model of hospital competition, we investigate the potential for displacement between vertically differentiated public and private providers, focusing on maternity units where user choice is central. Building over the model, we test the following three hypotheses. First, the number of public maternity units is likely to be much larger in less populated departments than in more populated ones. Second, as the number of public maternity units decreases, the profitability constraint should allow more private players into the market. Third, private units are closer substitutes to other private units than to public units. Building an exhaustive and nationwide data set on the activity of maternity services linked to detailed data at a hospital level, we use an event study framework, which exploits two sources of variation: (1) The variation over time in the number of maternity units and (2) the variation in users’ choices. We find support for our hypotheses, indicating that segmentation is at work in these markets with asymmetrical effects between public and private sectors that need to be accounted for when deciding on public market entry or exit.

The relationship between privatization and corporate taxation policies

We investigate how the corporate (profit) tax rate affects the optimal degree of privatization in a mixed duopoly with a minimal profit constraint for the private firm. We show that the profit tax rate directly affects the behavior of the partially privatized firm, and therefore affects the behavior of the private firm through strategic interactions. Regardless of whether the constraint is binding, the optimal degree of privatization increases with the corporate tax rate. The reason is that an increase of corporate tax rate reduces the profits flowing to foreign investors, which mitigates the welfare losses of privatization. Furthermore, the optimal degree of privatization decreases (increases) with the foreign ownership share in the private firm if the constraint is ineffective (effective). This result suggests that a minimal profit constraint can be crucial in the optimal privatization policy.

An Individual Risk Model for Premium Calculation Based on Quantile: A Comparison between Generalized Linear Models and Quantile Regression

This article deals with the use of quantile regression and generalized linear models for a premium calculation based on quantiles. A premium principle is a functional that assigns a usually loaded premium to any distribution of claims. The loaded premium is generally greater than the expected value of the loss and the difference is considered to be a risk margin or a safety loading. The failure of a right charge of individual risk rate exposes the insurer to adverse selection and, consequently, to deteriorating financial results. The article aim is to define the individual pure premium rates and the corresponding risk margin in balance with some profit or solvency constraints. We develop a ratemaking process based on a two-part model using quantile regression and a gamma generalized linear model, respectively, in order to estimate the claim’s severity quantiles. Generalized linear models focus on the estimation of the mean of the conditional loss distribution but have some drawbacks in assessing distribution moments other than the mean and are very sensitive to outliers. On the contrary, quantile regression exceeds these limits leading to the estimate of conditional quantiles and is more accurate for a better measurement of the variability of a risk class. The proposed methodology for premium calculation allows us to find further limits of the generalized linear model as the solution we found, under specific assumptions for a generalized linear model, is equivalent to the application of the expected value premium principle. Finally, the methodology we suggest for the two-part quantile regression allows reducing the practical issue of overmodeling and over-parameterization with respect to other proposals on the same topic.

Hydrogen Storage Optimal Scheduling for Fuel Supply and Capacity-Based Demand Response Program Under Dynamic Hydrogen Pricing

As the emerging technology offers more economic and efficient mechanisms for hydrogen production, fuel cell electric vehicles (FCEVs) are expected to be deployed more extensively in the near future. Proliferation of hydrogen fueling stations throughout the transportation network and justifying their economic viability are key factors to the success of the FCEVs. In today’s deregulated market environment, many governments are encouraging private investors to invest in key infrastructures including the hydrogen fueling stations. To that end, this paper proposes a new model for optimal scheduling of privately owned hydrogen storage stations to both serve the transport sector and the electricity market operator. The model mainly aims to: 1) exploit the lower electricity market prices to reduce the power purchase cost and 2) contribute to the capacity–based demand response program to further enhance the economic feasibility of the investment. The profitability constraints and dynamic hydrogen pricing mechanisms are incorporated into the optimization process to guarantee the economic feasibility of the investment. Through such constraints, hydrogen sale prices would dynamically change to maintain system profitability at the lowest possible hydrogen price. Numerical studies reveal that the stacked profit from the two aforementioned sources of revenue under the proposed model would lead to a stronger rate of return.

Cardinality Bundling with Spence–Mirrlees Reservation Prices

We study the pricing of cardinality bundles, where firms set prices that depend only on the size of the purchased bundle, a practice that is increasingly being adopted by industry. The model we study, where consumer choices are discrete, was originally proposed by Hitt and Chen [Hitt L, Chen P (2005) Bundling with customer self-selection: A simple approach to bundling low-marginal-cost goods. Management Sci. 51(10):1481–1493], and it requires that consumers’ preferences obey the Spence–Mirrlees single-crossing property. We correct prior approaches and develop various structural and managerial insights. We develop a fast combinatorial technique to obtain the optimal prices. We extend our analysis to address a quantity discount problem originally proposed in Spence [Spence M (1980) Multi-product quantity-dependent prices and profitability constraints. Rev. Econom. Stud. 47(5):821–841]. We provide examples that demonstrate that the proposed approach of Spence (1980) only identifies local optima without providing guidance on selecting the globally optimal pricing function. Our insights from the discrete model are extended to this context to develop a scheme that provides solutions within an arbitrary prespecified tolerance. Consequently, we also solve the continuous version of the cardinality bundling problem. This paper was accepted by Chris Forman, information systems.

Long-Run Price Elasticities of Demand for Credit: Evidence from a Countrywide Field Experiment in Mexico

Abstract We use randomized interest rates, offered across eighty geographically distinct regions for twenty-nine months by Mexico’s largest microlender, to sketch the adjustment from a price change to a new equilibrium. Demand is elastic, and more so over the longer run; e.g. the dollars-borrowed elasticity increases from $-$1.1 in Year one to $-$2.9 in Year three. Credit bureau data do not show evidence of crowd-out, although this and other null results are imprecisely estimated. The lender’s profits increase, albeit noisily, starting in Year two. But competitors do not respond by reducing rates. These findings, together with other results, suggest that informational frictions are important, and that cutting rates furthered Compartamos Banco’s “double bottom line” of improving social welfare subject to a profitability constraint.

Comparing welfare and profit in quantity and price competition within Stackelberg mixed duopolies

We compare welfare and profits under price and quantity competition in Stackelberg mixed duopolies. Under public leadership, price competition always yields greater profits and welfare than quantity competition. By contrast, under private leadership, the result depends on the nationality of the private firm. When the private firm is domestic (foreign), welfare is greater under quantity (price) competition. However, private firms always earn more under price competition. Introducing the nonnegative profit constraint affects welfare ranking but not profit ranking. These results indicate that profit ranking is fairly robust to the time structure in Stackelberg mixed duopolies, but welfare ranking is not.

The welfare impacts of discriminatory price tariffs

This paper examines the use of asymmetric tariffs as a regulatory instrument. A monopoly setup is adopted in which the firm sells in two markets but price controls are introduced in just one. The regulator's objective is to increase consumer welfare through this price discriminatory practice, with the firm operating under a profit constraint. We consider cases where consumer welfare in the two markets is weighted both equally and unequally and also cases where the cost of supplying the two retail markets is determined in a monopsonistic input market. The results suggest that in certain situations controlling prices in only one market could be a desirable option from a welfare perspective.

The Evaluation of Agile Demand Response: An Applied Methodology

This paper formulates an applied methodology for an agile demand response using mathematical micromodels. The optimal strategy chosen by an aggregator is the maximization of social welfare derived from demand flexibility. The notion of complex demand bidding is already given in the literature, however heretofore it is formulated as the relationship of price with both demand elasticity and marginal cost along with temporal and profit constraints. Although the planning of flexible demand is already handled by using advance learning techniques in literature, herein simple ${Q}$ -learning technique in a decentralized fashion is proposed. Moreover, trade-offs between the proposed complex bidding rules are explored in a day-ahead market context. Due to the given complex bidding rules and principle of learning, the methodology can be easily applied in active distribution network. Several number of houses, equipped with the proposed complex bidding mechanism and decentralized learning capability, has been simulated, thus illustrating the application of methodology formulated herein.