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Volatility in the cryptocurrency market is an extremely important indicator for investors, as it allows them to manage their investment risk and define strategies that will result in profit maximization. Thus, this study focuses on determine which of the GARCH, EGARCH, and TGARCH models is the optimal model that best describes the daily returns' volatility of MATIC, SOL, BTT, and VET – four cryptocurrencies with relatively limited presence in the market compared to Bitcoin. The optimal model is selected by using the AIC statistical quality criterion. For the chosen sample period, empirical evidence suggests that EGARCH(1,1) and GARCH(1,1) are the most suitable models for describing the returns' volatility of MATIC and VET, respectively. In the cases of SOL and BTT, the lack of success in validating all the assumptions needed to apply GARCH models reveals that these models are not the most adequate for describing the cryptocurrencies under study. [ABSTRACT FROM AUTHOR]

\nHIGHLIGHTWith the popularization of electric vehicles and the wide application of power batteries, the explosion and environmental pollution problems caused by power batteries are increasing day by day. Therefore, this study focuses on selecting optimal recycling modes for power battery reverse supply chains under environmental responsibility. Three models were analyzed: manufacturer-led decentralized decision-making, centralized decision-making, and consumer-led decision-making. Results from MATLAB examples indicate that manufacturer-led decentralization suits small enterprises reliant on local third-party recycling. Centralized decision-making is cost-effective for large manufacturers with advanced technology and brand recognition. Consumer-led approaches offer flexibility and adaptability, ideal for innovative firms. Meanwhile, the effective implementation of environmental responsibility not only promotes the active participation of consumers in recycling activities, but also increases the recycling amount of used batteries. While protecting the environment, enterprises also achieve profit maximization, reduce costs and increase efficiency. In this paper, the selection of waste power battery recycling mode under environmental responsibility is studied, and constructs and compares three recycling modes: manufacturer-led, decentralized decision-making and consumer-led.The effects of the level of environmental responsibility on the profits of enterprises and the recovery rate of waste batteries were analyzed.Fulfilling a certain degree of environmental responsibility can help increase corporate profits and the amount of recycling of used power batteries.In this paper, the selection of waste power battery recycling mode under environmental responsibility is studied, and constructs and compares three recycling modes: manufacturer-led, decentralized decision-making and consumer-led.The effects of the level of environmental responsibility on the profits of enterprises and the recovery rate of waste batteries were analyzed.Fulfilling a certain degree of environmental responsibility can help increase corporate profits and the amount of recycling of used power batteries. [ABSTRACT FROM AUTHOR]

This work investigates a two‐tier supply chain in which a supplier adjusts product quality and prices and distributes the products through a retailer or directly to consumers with the reference quality (RQ) effect. The supplier can choose from three, that is, traditional sales (TS), dual‐channel (DC), and online‐to‐offline (O2O), sales modes. In the O2O sales mode, the retailer is allowed to open an online channel. The market demands are determined by the consumer utility consisting of two parts, a part depending on the consumer RQ effect and the other depending on the retail price and product quality. The profit maximization, that is, the Stackelberg game, models involving the consumer RQ effect are developed using prospect theory, wherein the supplier has the first mover advantage. The analytical optimal solutions are obtained through backward induction. The optimal quality and pricing decisions and the optimal profits of the supply chain members are compared among the three sales modes. A practical application is analyzed to verify the theoretical results of the proposed models. The results show that the consumer RQ effect benefits the two supply chain members under certain conditions. The optimal quality and pricing decisions are significantly affected by the consumer RQ effect except those of the supplier under the O2O sales mode. When consumers have the RQ effect, the DC sales mode is the best choice for the supplier, is a win–win mode for the two supply chain members under certain conditions, and benefits the consumers the most among the three sales modes. [ABSTRACT FROM AUTHOR]

In this paper, we study the budget-constrained profit maximization problem with expensive seed endorsement, a derivation of the well-studied influence maximization and profit maximization in social networks. While existing research requires the non-negativity of the objective profit function, this paper considers real-world scenarios where costs may surpass revenue. Specifically, our problem can be regarded as maximizing the difference between a non-negative submodular function and a non-negative modular function under a knapsack constraint, allowing for negative differences. To tackle this challenge, we propose two algorithms. Firstly, we employ a twin greedy and enumeration technique to design a polynomial-time algorithm with a quarter weak approximation ratio, providing a balance between computational efficiency and solution quality. Then, we incorporate a threshold decreasing technique to enhance the time complexity of the first algorithm, yielding an improved computational efficiency while maintaining a reasonable level of solution accuracy. To our knowledge, this is the first paper to study the profit maximization beyond non-negativity and to propose polynomial-time algorithms with a constant bicriteria approximation ratio. [ABSTRACT FROM AUTHOR]

Profit maximization is crucial for both producers and customers in power grids, particularly in deregulated energy markets. By focusing on profit maximization measures, power grids can improve economic viability and sustainability for all stakeholders. The growing demand for electricity in modern civilization poses a significant challenge to the power grid's ability to meet it. To address these difficulties, the power industry is attempting to integrate renewable sources into existing power facilities. Because of its unpredictable character, the presence of renewable energy in the electricity system becomes increasingly important and adds complexity to the grid. Given the intermittent nature of renewable sources, an energy storage device is required in this scenario. During the low-power use phase, the grid has ample power. Meanwhile, energy storage devices can be utilized to store extra power in the low power demand phase while maintaining the safety and stability of the power network during peak demand periods. V2G (Vehicle-to-Grid) technology is commonly employed to address the uncertainty of renewable resources while maintaining system stability. By using automobiles as transportable storage devices, V2G can make the best use of excess electricity during low-energy hours. Wind energy's volatility causes imbalances, lowering system profitability. With wind energy integration, power system profitability can be improved by analyzing uncertainties and employing mitigation strategies. To overcome the issue, the research optimizes the use of V2G and TCSC (thyristor-controlled series compensator). V2G permits bidirectional power flow, lowering imbalance pricing (IP) while considering power demand. TCSC can increase the transient stability of transmission lines, hence reducing line faults. TCSC's involvement in the system increases profitability by lowering congestion expenses. Strategic TCSC placement increases overall profitability. The approach strives to offer efficient, environmentally friendly power at a reasonable price while addressing the needs of both power providers and customers. This study also looks into the effects of V2G mixing on voltage stability, LMP (locational marginal price), and the effectiveness of a TCSC in a deregulated electrical system. MiPower software is used to simulate operation and find the optimal placement of the storage unit within the IEEE 14-bus system. [ABSTRACT FROM AUTHOR]

With the rise of the sharing economy, shared bicycles have become an important component of urban transportation. This paper explores the nonlinear dual oligopoly system for the Cournot model in the bike-sharing market; both operators have maximized profits as their competitive goals. The analysis of pivotal factors influencing passenger preferences, including pricing discounts and comfort levels, is meticulously depicted by a bifurcation diagram. A new chaotic attractor—the shared bicycle attractor—is discovered. The research results indicate that larger discounts and adjustment speeds can cause the system to be in a chaotic state, which is not conducive to the long-term development of operators, although discounts can indeed attract more passengers to a certain extent. On the other hand, the increase in the marginal cost of comfort loss can also make it difficult for enterprises to operate, which requires continuous technological innovation to improve the comfort of cycling. [ABSTRACT FROM AUTHOR]

Purpose: This study examines the willingness of Spanish and Lithuanian grain farmers to adopt a combined approach of preventive site-specific spraying (PSSS) and selective harvesting (SH), two precision agricultural technologies (below referred to as PSSS-SH) aimed at mitigating the risk of mycotoxin contamination in barley and wheat. Methods: Data were collected from 190 commercial grain farmers using a choice experimental survey. The empirical analysis relied on the estimation of mixed logit and integrated latent class models. Results: The surveyed farmers were heterogeneous in their preference for the PSSS-SH technology, with a majority (81%) reporting that they were willing to adopt and pay for the PSSS-SH technology. Furthermore, the farmers' willingness to adopt PSSS-SH technology was influenced by the trade-offs between the potential production, economic and environmental changes. Conclusion: Profit maximization is not the only motivation for a farmer's decision to adopt PSSS-SH, there are also important non-financial benefits that align with the observed choices. Furthermore, the perceived usefulness of the technology, the willingness and readiness to use the technology, and the farmer characteristics (e.g. cooperative membership, employment status, share of household income from grain production and past experience with precision farming technology) were positively associated with uptake of the PSSS-SH technology. Therefore, extension programmes should have a special focus on the perceived usefulness of the technology, the willingness and readiness of farmers to use it, and its unique characteristics. [ABSTRACT FROM AUTHOR]

• deposit-refund system for the circulation of RTIs (Returnable transport Items) to maximize supply chain profitability. • effects of RTI-related parameters to determine optimal policies under different retail price structures are investigated. • results lead to important implications for the design of deposit-refund systems for RTIs. While the use of Returnable Transport Items (RTI) is a common practice in supply chains committed to sustainability. An efficient RTI circulation mechanism must be implemented to ensure the profitability of the approach. Within this context, this paper presents a deposit-refund system (DRS) designed for RTI circulation to maximize supply chain profitability. The profit optimization analysis considered three scenarios: the vendor's profit, there tailer's profit, and the overall system profit. Tailored solution strategies are developed for each case, considering retail price components, and leading to the identification of the optimal deposit amount required to maximize RTI circulation. We further examinehow the retail price affects the profits of supply chain actors relative to the RTI return rate and studystudy how changes in return rates influence the profit patterns of supply chain actors. The results are scrutinized via several sensitivity analyses, leading to important implications for the design of deposit-refund systems for RTI. We for example find that the vendor's wholesale price has a significant effect in establishing the decision-maker's optimal policy in designing a DRS for RTI circulation. In other words, the wholesale price is a key component in assessing the economic viability and determining the deposit amount for RTI. Moreover, through a couple of hypothetical, butrealistic cases of RTI applications considering specific parameter settings, we shown that the vendor's position can be either significant or insignificant dependong on the difference between the value of the RTI and that of the goods it holds. [ABSTRACT FROM AUTHOR]

The controversial impacts of slum tourism have sparked debate and raised questions about its benefits for impoverished communities. The potential positive effects of slum tourism often hinge on "last mile" strategies and the crucial role of local entrepreneurs who manage the visits and interactions in determining the benefits to these areas. Drawing on a blend of Social Entrepreneurship Theory and Economic Development Theory, we explore and compare the contributions of both internal and external slum tourism entrepreneurs. Our findings reveal striking differences in their strategies and orientations. For example, internal entrepreneurs are deeply rooted in the focal slum and prioritize long-term poverty alleviation through the creation of permanent jobs and innovative approaches, whereas external entrepreneurs tend to focus on profit maximization and diversification of their offer outside the slums. However, both internal and external entrepreneurs actively challenge stereotypes, catalyze skills' development, and channel resources back into the slum communities. This research sheds light on the multifaceted impacts of slum tourism entrepreneurship, providing critical insights for future endeavors in community development and slum tourism studies. [ABSTRACT FROM AUTHOR]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper delves into the complex relationship between business leadership, sustainability, and inclusivity, representing a step toward developing a more inclusive leadership approach to sustainable development that fosters shared power relations between business leaders and marginalized members of society. With environmental and social conditions worsening, it is urgent for corporations to move away from the neoliberal profit-maximization models advocated by Milton Friedman and instead prioritize humanity and the environment. This shift requires a fundamental restructuring of businesses to move beyond profit maximization and address societal power imbalances by including all stakeholders. Our inclusive leadership for sustainable development framework, rooted in symbolic interactionism, offers a holistic lens for including marginalized groups. At the microlevel, it focuses on business leaders' personas, characterized by pro-demographic diversity and biodiversity, cognitive complexity for sustainable development, and social empathy, which can potentially create a macro-level impact. These characteristics, accompanied by macro perspectives toward repurposing corporations away from neoliberalism, would be a step forward in cultivating shared power dynamics between business leaders and marginalized communities for the betterment of society. [ABSTRACT FROM AUTHOR]

Historically, advertising allocation decisions have operated mostly at a macro level, comprising determination of the total budget followed by apportionment among media channels. Owing to the rapid and sustained rise of digital media, an additional decision now operates at a micro level, namely, which specific customers to target with advertising. In the macro case, optimal control theory provides a powerful framework for firm profit maximization, specifically allowing for ad response, cost per medium, and discount rate, all in the presence of multiple competing brands. However, optimal control theory has never been applied to the situation of microtargeting individual customers. Consequently, in this study the author shows how optimal control theory can be adapted for application to individual customers. The author estimates a multinomial logit model with individual-specific advertising response parameters. In turn, these parameters are used to determine the optimal number of exposures each customer should receive for each advertising medium. An empirical example demonstrates that using the optimal microtargeting method improves firm profits over existing ad scheduling methods by between 150% and 183%. [ABSTRACT FROM AUTHOR]

The ride-hailing services are booming in our daily lives, but it is unclear that how the platforms should set prices to maximize their profits when facing one kind of rationally inattentive passengers in a two-sided market. To fill this gap, we establish a profit maximization model for the ride-hailing platform based on queuing theory and rational inattention theory and analyze the properties of the model. Numerical examples are presented to demonstrate the impacts of perceived high and low service levels, information cost and prior belief on the optimal price and commission rate of the ride-hailing platform. The results show that (1) for different cities, there is always an optimal pricing strategy to maximize the profit of the platform. (2) To ensure maximum profit, the platform should disclose the service information of ride-hailing as much as possible, but also maintain the unknownness of ride-hailing services appropriately. [ABSTRACT FROM AUTHOR]

The exponential proliferation of renewable energy has resulted in a significant mismatch between power supply and demand, especially during extreme events. This incongruity presents challenges in efficiently harnessing renewable energy and enhancing the resilience of the power grid. To address this issue, this paper proposes shared energy storage (SES) planning based on the adaptive alternating direction method of multipliers (AADMM). The objective is to fully leverage SES, enhance the local consumption level of renewable energy, ensure power grid resilience, and reduce operational costs. First, to ensure the effective utilization of SES while minimizing initial investment and construction costs, a planning model for SES is formulated. Secondly, to maximize the benefits for multiple prosumers within the renewable energy and SES station, a profit maximization model for multiple prosumers is established. Lastly, to guarantee the privacy security of SES and multi-prosumers while optimizing computational efficiency, a distributed computing model for SES based on AADMM is developed. The results of the example show that the proposed model can not only reduce the cost of 47.96 CNY, but also increase the power self-sufficiency rate by 21.86%. In addition, compared with the traditional distributed optimization, the number of iterations of AADMM is increased by 47.05%, and the computational efficiency is increased by 54.67%. In addition, market prices have a great impact on energy trading, and the impact of market pricing on the operation of the park is not considered in our current research. In this case, our future research aims to consider how to price reasonably between prosumers and between prosumers and SES, so as to realize the stable participation of each subject in the energy market. [ABSTRACT FROM AUTHOR]

Sturgeon farming poses an interesting decision: give priority to highly prized caviar or meat. Indeed, sturgeons are mainly reared in aquaculture to produce caviar. However, the economic profitability of a sturgeon farm also depends on the existence of a meat market. Meat is still an important product for sturgeon farmers once the stock of caviar-producing females has been established. This paper takes a case study approach and analyses how an Italian producer addresses the trade-off, adopting a methodology that measures the value of the economic productivity of production inputs, e.g., the measure of the profitability of the inputs, and compares them with the costs of the inputs and the prices of the outputs. This analysis is the first attempt, to our knowledge, to adopt an economic analysis to address this choice problem, based on a very representative case study. This helps monitor whether the farm's production choices are economically efficient and effective and whether and how those can be improved. The results show that the farm maximizes profit but does not reach its optimal production level. This implies that both (1) producing more output and (2) a further switch to caviar production for less meat are economically efficient production strategies. [ABSTRACT FROM AUTHOR]

AbstractThis study addresses a scheduling problem arising from the “Rent Instead of Buy” business model where rental service platforms rent equipment from suppliers to satisfy customer requests in exchange for a lump-sum payment. Suppliers often implement piecewise linear concave charging policies to encourage longer rental duration. This policy prompts the platform to consolidate service periods into longer rental agreements for lower rental rates, potentially necessitating the splitting of a customer request among multiple facilities. We establish an integer programming model to maximize the platform’s profit, which is equal to the total revenue from serving customers minus the total rental and scheduling costs. To solve the problem, we propose two Dantzig–Wolfe decomposition reformulations and design branch-and-price algorithms accordingly. Furthermore, by exploiting the property of the concave rental cost functions, we derive the optimal structure regarding the start and end times of services, significantly reducing the subproblem’s search space. Numerical experiments based on real-life data validate the efficiency of the designed algorithms compared to commercial solvers and highlight the benefits of split services. Sensitivity analysis reveals that as the rental cost function becomes more segmented, more requests are split-serviced, but the times for splitting a request are seldom more than once. [ABSTRACT FROM AUTHOR]

In recent years, as the energy market continues to change, an emerging business model is gradually emerging as an energy provider (EP). The participation of EP as an independent entity in the distribution network investment model not only helps realize a win–win situation for multiple stakeholders but also promotes the innovation and sustainable development of the energy industry. However, in the energy system, EPs, grids, microenergy grids (MEGs), and shared energy storage service providers usually belong to different stakeholders, and their respective pursuit of profit maximization can easily lead to uncontrolled competition, which greatly reduces market efficiency. To address the varying ownership structures within the energy system, a refined bilevel coordinated optimization operation model is introduced, rooted in the master–slave game theory. In this model, the EP assumes the role of the master, while the MEG operator and the shared energy storage operator (SESO) act as slaves. A peer‐to‐peer energy‐sharing mechanism between MEGs and an energy trading mechanism between MEGs and shared energy storage is used to further increase the local consumption rate of new energy. Simulation results demonstrate that the proposed method not only boosts the revenues of an EP, SESO, and MEG operators but also effectively accounts for the MEG's energy storage needs and SESOs' profitability. [ABSTRACT FROM AUTHOR]

Onion (Allium cepa) is a major field vegetable in South Korea and has been produced for a long time along with cabbage, radish, garlic, and dried peppers. However, as field vegetables, including onions, have recently been imported at low prices, the profitability of onion production in South Korea is beginning to be at risk. In order to maximize farmers' profits through onion production, this study develops onion yield prediction models via an ensemble learning-based framework involving linear regression, polynomial regression, support vector regression, decision tree, ridge regression, and lasso regression. The use of nitrogen fertilizers is considered an independent variable in the development of the yield prediction model. This is because the use of nitrogen fertilizers accounts for the highest production cost (13.47%) after labor cost (41.21%) and seed cost (17.42%), and it also directly affects onions yields. For the model development, five research datasets on changes in onion yield according to changes in the use of existing nitrogen fertilizers were used. In addition, a non-linear optimization model was devised using onion yield prediction models for the profit maximization of onion production. As a result, the developed non-linear optimization model using polynomial regression enables an increase in profits from onion production by 67.28%. [ABSTRACT FROM AUTHOR]

In last-mile delivery logistics, peer-to-peer logistic platforms play an important role in connecting senders, customers, and independent carriers to fulfill delivery requests. As the carriers are not under the platform's control, the platform has to anticipate their reactions while deciding how to allocate the delivery operations. Indeed, carriers' decisions largely affect the platform's revenue. In this paper, we model this problem using bilevel programming. At the upper level, the platform decides how to assign the orders to the carriers; at the lower level, each carrier solves a profitable tour problem to determine which offered requests to accept, based on her own profit maximization. Possibly, the platform can influence carriers' decisions by determining also the compensation paid for each accepted request. The two considered settings result in two different formulations: the bilevel profitable tour problem with fixed compensation margins and with margin decisions, respectively. For each of them, we propose single-level reformulations and alternative formulations where the lower-level routing variables are projected out. A branch-and-cut algorithm is proposed to solve the bilevel models, with a tailored warm-start heuristic used to speed up the solution process. Extensive computational tests are performed to compare the proposed formulations and analyze solution characteristics. Funding: The research of E. Fernandez was partially funded through the Spanish Ministerio de Ciencia y Tecnología and European Regional Development Funds (ERDF) [Grant MTM2019-105824GB-I00]. The research of C. Archetti, M. Cerulli, and I. Ljubić was partially funded by CY Initiative of Excellence, France [Grant "Investissements d'Avenir ANR-16-IDEX-0008"]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0129. [ABSTRACT FROM AUTHOR]

This study combines multi-item Just-in-Time solid transportation problems, budget constraints, and carbon emissions with potential profit maximization. Carbon emissions and other greenhouse gas emissions from various modes of transportation cause air pollution and harmful changes in climate. Realizing the impact of time in real-life transportation problems, here we introduce the Just-in-Time concept that helps to find the optimal network for transporting goods from origin to destination on time. The model of multi-item Just-in-Time solid transportation problem based on carbon emission is formulated, and then an algorithm is developed under some priority-based approaches to obtain an optimal solution. To test and justify the efficiency of the proposed study, we include an example having two-fold priority: (i) first priority is to deliver goods within the scheduled time, and (ii) second priority is to maximize profit. Finally, the article ends with the conclusions and future study. [ABSTRACT FROM AUTHOR]

To analyze performance in cloud computing, some unpredictable perturbations that may lead to performance degradation are essential factors that should not be neglected. To prevent performance degradation in cloud computing systems, it is reasonable to measure the impact of the perturbations and propose a robust configuration strategy to maintain the performance of the system at an acceptable level. In this article, unlike previous research focusing on profit maximization and waiting time minimization, our study starts with the bottom line of expected performance degradation due to perturbation. The bottom line is quantified as the minimum acceptable profit and the maximum acceptable waiting time, and then the corresponding feasible region is defined. By comparing between the system's actual working performance and the bottom line, the concept of robustness is invoked as a guiding basis for configuring server size and speed in feasible regions, so that the performance of the cloud computing system can be maintained at an acceptable level when perturbed. Subsequently, to improve the robustness of the system as much as possible, discuss the robustness measurement method. A heuristic optimization algorithm is proposed and compared with other heuristic optimization algorithms to verify the performance of the algorithm. Experimental results show that the magnitude error of the solution of our algorithm compared with the most advanced benchmark scheme is on the order of 10−6, indicating the accuracy of our solution. [ABSTRACT FROM AUTHOR]

Copyright of Direito e Práxis is the property of Editora da Universidade do Estado do Rio de Janeiro (EdUERJ) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Most definitions of algorithmic bias and fairness encode decision-maker interests, such as profits, rather than the interests of disadvantaged groups (e.g. racial minorities): bias is defined as a deviation from profit maximization. Future research should instead focus on the causal effect of automated decisions on the distribution of welfare, both across and within groups. The literature emphasizes some apparent contradictions between different notions of fairness, and between fairness and profits. These contradictions vanish, however, when profits are maximized. Existing work involves conceptual slippages between statistical notions of bias and misclassification errors, economic notions of profit, and normative notions of bias and fairness. Notions of bias nonetheless carry some interest within the welfare paradigm that I advocate for, if we understand bias and discrimination as mechanisms and potential points of intervention. [ABSTRACT FROM AUTHOR]

Supply chain network design and resilient supplier selection are important in supply chain risk management to deal with various operational and disruption risks. In this paper, we develop a robust mathematical bi-objective, multiproduct model to consider resilient suppliers and uncertainty in supply chain network design across a multi-period and multi-products simultaneously, and this study offers optimal solutions for resilient supplier selection and order allocation. First, we show a Mixed-Integer Linear Programming (MILP) model with two objective functions. The first objective function maximizes the total profit, while the second maximizes the total supplier resilience score. Fuzzy SECA was used to obtain the five resiliency criteria weights and the resilience scores for the objective function. We can rank the resilient suppliers using the fuzzy SECA method. We proposed an approach for coordinating production planning, supplier selection, and order allocation. Thee-constraint method was used to obtain optimum amounts of decision variables to maximize the profit for a real case study. Finally, a Pareto solution analysis was done to determine the tradeoff between robustness and resilience. The results show how uncertainty parameters in the supply chain can affect the objective function. Furthermore, this paper shows that with a supplier resilience score of 4000, the first objective function of the model presents the highest value. Therefore, at this point, we can have a resilient supplier with maximum profitability. [ABSTRACT FROM AUTHOR]

The challenge of designing a Closed-Loop Supply Chain (CLSC) under conditions of uncertainty and partial disruptions is complex and demanding. The concept of a CLSC involves integrating reverse logistics into the traditional forward supply chain to establish a sustainable and environmentally friendly system. However, uncertainties and partial disruptions create significant obstacles to achieving an efficient and dependable CLSC. In order to address these challenges, the concept of chance constraint is introduced, allowing for the consideration of probabilistic uncertainties in decision-making. The goal is to develop a robust CLSC model capable of effectively managing uncertain parameters such as demand, rate of return, and product quality. The Markowitz method is utilized to address uncertainty in the objective function by combining the mean with a coefficient of standard deviation. The study's results demonstrate that incorporating uncertainty into the model leads to increased profitability compared to the deterministic model. The uncertain model is more responsive to demands and considers the dynamics of confidence inventory, leading to improved decision-making. Strategic decisions, such as the number of production, distribution, and destruction facilities, remain consistent in both models. However, the capacity of destruction centers in the uncertain model is slightly smaller due to the consideration of uncertain product quality. Furthermore, incorporating uncertainty into the model has contributed to enhancing the model's clarity and facilitating improved decision-making. This increase in profitability can be attributed to the model's heightened responsiveness to demands, as well as its dynamic approach to managing confidence inventory. [ABSTRACT FROM AUTHOR]

Copyright of Efil Journal of Economic Research / Efil Ekonomi Araştırmaları Dergisi is the property of Efil Journal of Economic Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Ongoing seismic events in global society have increased demands on organizations to change their focus on profit maximization alone to becoming a social enterprise that follows humanistic management (serving the common good) principles. Coincidentally, business schools are under pressure to teach humanistic management principles in their curriculum to enable the future work force to become agents of world benefit. Data analytics offers a means to introduce these principles to undergraduate business students. The analytics module described in this paper introduces undergraduates in an introductory Information Systems course to humanistic management. It discusses the use of humanistic management analytics (HMA), and describes an assignment to design, develop and use a HMA dashboard. Through video, lecture, case study and assignments, students learn the value of incorporating humanistic management principles to analytics and human resource functions that embody concepts of data analytics for social good. [ABSTRACT FROM AUTHOR]

Problem definition: We investigate how the characteristics of consumers and a service firm influence the firm's optimal pricing and promised delivery-time decisions as well as the optimal investment in the quality of delivery reliability information available to consumers. Methodology/results: We use utility, queuing, and choice modeling theories to model consumers' behavior and to find solutions to the firm's profit maximization problem. Managerial implications: The optimal strategy is to disclose either error-free delivery reliability information or no information at all. We also delineate conditions for each of the two strategies to dominate. Funding: This research was supported by the General Research Fund (GRF) of the Hong Kong Research Grants Council under Research Project LU13500822. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0223. [ABSTRACT FROM AUTHOR]

To optimize energy structure and efficiently utilize renewable energy sources, it is necessary to establish a new electrical power–gas mutual transformation virtual power plant that has low-carbon benefits. To promote the economic and low-carbon operation of a virtual power plant and reduce uncertainty regarding the use of new energy, a multi-timescale (day-ahead to intraday) optimal scheduling model is proposed. First, a basic model of a new interconnected power–gas virtual power plant (power-to-gas demand response virtual power plant, PD-VPP) was established with P2G and comprehensive demand response as the main body. Second, in response to the high volatility of new energy, a day-ahead to intraday multi-timescale collaborative operation optimization model is proposed. In the day-ahead optimization period, the next day's internal electricity price is formulated, and the price-based demand response load is regulated in advance so as to ensure profit maximization for the virtual power plant. Based on the results of day-ahead modeling, intraday optimization was performed on the output of each distributed unit, considering the cost of the carbon emission reductions to achieve low-carbon economic dispatch with minimal operating costs. Finally, several operation scenarios are established for a simulation case analysis. The validity of the proposed model was verified via comparison. [ABSTRACT FROM AUTHOR]

Copyright of Die Urologie is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The volume of trade between two regions is shaped by the cost of trade, which is a function of infrastructural and political barriers. Governments can invest in the upgrade of their country's logistics infrastructure to mitigate these barriers, decrease trade costs, and increase domestic trade volumes; but they need to decide which infrastructural project(s) to invest under limited budget. A bilevel, bicriteria optimization model is proposed to overcome this challenge. The inner model anticipates total trade flows among all regions of the country with a profit maximization lens of the transporters. The outer model selects the projects based on minimizing total investment costs and maximizing total trade flows. The novelty in this approach is in incorporating the implicit 'trade barriers' through a 'trade cost model' borrowed from the economics literature. The proposed framework is implemented in a case study originating from the Canadian Northern Corridor concept which aims to boost domestic trade between the provinces and the territories of Canada. The numerical study indicates that investments to upgrade and increase connectivity in the logistics infrastructure of western Canada should be prioritized over others. [ABSTRACT FROM AUTHOR]

Copyright of International Journal of Professional Business Review (JPBReview) is the property of Open Access Publications LLC and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, we present a model detailing the benefits of two competing firms in a duopoly market, where profit maximization is linked to their production levels using the Cournot method. Our primary objective is to develop a collaborative strategy within the framework of open innovation to optimize their profits. Furthermore, we analyze how these firms can integrate an additional source of revenue in the form of intellectual property, without negatively impacting their open innovation strategies. To achieve this, we conducted a dynamic study of these strategies by introducing this intellectual property, to assess the impact of its components, such as patent licensing fees and royalties, on the equilibrium of strategies adopted by these firms. Our aim is to provide recommendations for optimal management of this intellectual property, thus enabling firms to fully leverage its benefits while preserving their competitive position in the market. [ABSTRACT FROM AUTHOR]

In pursuit of profit maximization through practices that promote sustainable development, companies increasingly use environmental, social, and governance (ESG) criteria to guide investment. However, there are challenges in implementing these practices across diverse sectors, such as the construction industry, especially in emerging countries with limited related studies. This study validated the challenges of implementing ESG in the construction industry in an emerging country like Brazil, considering its relationship with the United Nations Sustainable Development Goals (SDGs). A literature review identified challenges associated with ESG implementation, followed by the development of a questionnaire based on these challenges. Subsequently, the questionnaire was administered to professionals in the construction industry and validated using the Lawshe method. Twenty-seven challenges were identified, of which twelve were validated, including the lack of standardized performance indicators, regulatory guidelines associated with ESG practices, organizational resistance, and insufficient transparency in non-financial indicators. This study provides guidance for overcoming challenges in the successful adoption of ESG in the construction industry. It also identifies the most-impacted SDGs and lays the foundation for future actions promoting sustainable development in the construction industry in emerging countries. [ABSTRACT FROM AUTHOR]

Corporate sustainability development has emerged as a significant concern globally. However, there is an inherent conflict between the corporate's goals of maximization of profit and achievement of sustainable development. The increasing environmental responsibility offers a solution to reconcile this contradiction. Therefore, this paper aims to empirically investigate the influence of corporate environmental responsibility on corporate sustainable development and the mediator effect of corporate environmental strategy based on the nature of strategy that it can transform temporary and one-sided-focused activities into chronical and holistic actions. The study conducted two stages of data collection, gathering a total of 324 data sets from China in 2022, which were subsequently analyzed. The findings demonstrate that corporate environmental responsibility has a significant positive impact on corporate sustainable development, with corporate environmental strategy playing a crucial intermediary role. And this research offers theoretical contribution by extending literature on sustainable development and provides significant managerial implications. [ABSTRACT FROM AUTHOR]

To be more realistic, this paper considers the pursuit of maximizing relative profits by firms, establishes a Cournot duopoly game model with quantum entanglement, and investigates how quantum entanglement influences decisions made by the firms. Following that, the Jacobi matrix and Jury criterion are used to examine the local stability of the Nash equilibrium. Additionally, the impact of various model parameters on system stability is investigated by implementing a numerical experiment. It has been found that limiting the adjustment speed and squeezing parameter to an appropriate range contributes to the long-term stability of the market. The coexistence of attractors is then fully investigated by means of the basin of attraction. The coexistence of attractors provides insights into the complexity and diversity of dynamic systems, shedding light on their nonlinear nature and sensitivity to initial conditions. Finally, the critical curve and the noninvertible map allow us to examine the global dynamics of the system. [ABSTRACT FROM AUTHOR]

Despite Greece's centrality in Eastern Mediterranean history and politics, the evolution, characteristics, and rationale behind the country's relations with the Arab world have yet to be identified. This article examines post-World War II Greek foreign policy towards the Arab world across four key periods (1945–80; 1981–89; 1990–2018; and 2019 onwards). It builds on a historical institutionalist approach to argue that Greece's relationship with the Arab world has remained a pillar of the country's diplomatic strategy, albeit instrumentalised in terms of Greece's two main foreign policy goals in the post-World War II era: maintaining the country's Western orientation and navigating the vicissitudes of Greek-Turkish relations. Thus, the Arab world has traditionally been approached by Greek policymakers in a profit maximization manner that sought to either amplify Greece's relationship with Western powers or respond to Turkish initiatives in the region. Aiming to provide the first systematic overview of Greek diplomatic strategy towards the Arab world, the article highlights the importance of path dependence in evaluating Greek foreign policy initiatives towards the Middle East. It also seeks to contextualize Greece's current attempts to forge a proactive role across the region by providing necessary historic nuance and a comparative perspective. [ABSTRACT FROM AUTHOR]

Stephanie Collins' book "Organizations as Wrongdoers: From Ontology to Morality" explores the metaphysical and moral implications of collective agency and group responsibility. Collins argues that organizations are material objects composed of both material and non-material parts, with members being the material parts. She discusses the blameworthiness of organizations from volitionist, attributivist, and aretaic perspectives, highlighting the moral significance of organizational attitudes and character. Collins also examines how organizational wrongdoing implicates members and considers the liability of members in historical or diffuse wrongdoings. Overall, the book offers valuable insights into the understanding of organizations and their ethical implications. [Extracted from the article]

An increasing number of projects are adopting prefabrication to economize on time, labor, and materials in fixed-position layout operations, such as construction, ship building, and aircraft manufacturing. In such contexts, independent contractor and fabricator make interdependent decisions, which calls for prudent supply chain management because performance relies on coordination between their decisions. Many studies have developed integrated systems and propose various algorithms for scheduling efficiency and reliability. Nevertheless, they pay scant attention to conflicting interests amongst independent partners, which may result in subpar performance not only for the supplier but for the contractor as well. Coordination of conflicting interests has been extensively studied in economics and supply chain management; yet, those studies focus on order-quantity decisions under demand uncertainty for profit maximization, while managers in fixed-position operations are more concerned about delivery decisions under scheduling uncertainty for cost minimization. We consider the case of construction and explore a contractual scheme that aligns the agents' decisions for coordination in a construction supply chain. Specifically, we propose a supplier rebate for coordination: the supplier grants a rebate if the contractor accepts the shipment in accordance with the delivery schedule that the contractor initially chose. We show that the optimal rebate fully coordinates the supply chain to minimize the joint supply chain costs. Thus, both the contractor and supplier benefit from the coordination by negotiating a mutually acceptable way to allocate the savings in joint costs between them. We further show that the rebate motivates the contractor to enhance its work scheduling. [ABSTRACT FROM AUTHOR]

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The investment horizon plays a crucial role in portfolio selection: For horizons approximately up to a year, one can safely employ the mean–variance (M-V) rule. Moreover, if investment consultants use monthly rates of return to derive the M-V efficient set and the investor horizon is longer but smaller than one year, the economic cost induced by this horizon mismatch is negligible. For longer horizons, the M-V rule deviates substantially from expected utility maximization and the economic cost induced by employing the M-V rule is substantial. For relatively long horizons (say 20 or 30 years), despite the argument that with myopic preference the horizon does not matter, small stocks dominate large stocks by the maximum geometric mean (MGM) rule and, in practice, also by expected utility for all economically relevant preferences, as there is almost first-degree stochastic dominance (AFSD). [ABSTRACT FROM AUTHOR]

This paper is focused on researching the behaviour of heating companies in connection with current developments in the electricity market and flexibility in the context of market behaviour. The work assesses the increase in profitability through the creation of a technical–economic model using an objective function with profit maximization. The objective of the paper is to present the procedure and methodology for creating a model using the basic scheme of production processes integrated into the system platform. The result of the work is a comparative analysis of modelled cases of implemented operation deployment according to a defined period and modelling modes on selected time series. The description of individual outputs demonstrates the economic advantage of using combinations of modes of combined electricity and heat production, and non-combined electricity and heat production, including the use of heat-suppression mode because of overproduction of electricity. [ABSTRACT FROM AUTHOR]

Economic growth and excessive fossil energy consumption have direct effects on environmental destruction and greenhouse gas increments. The existing appropriate pattern for economic performance increase as well as pollution emissions abatement is a basic issue in industry activities. In this paper, a data envelopment analysis (DEA) model is introduced for estimating the directional marginal profit maximization of supply chain divisions based on wasted energy and power losses. The purpose of this study is to estimate the directional marginal productivity in the supply chain, which enables us to find the optimal direction of efficient divisions on the frontier. This makes the allocation of resources create a marginal profit increase and the pollution emissions be abated simultaneously. Indeed, the proposed model considers the synergistic effects of each input on MP estimation in predetermined directions. The model is able to estimate the marginal profit maximization of desirable output and undesirable output decrease for each input simultaneously. The results suggested that the gas field division of one of the supply chains had fundamental capacities for energy production increments and flare gas decrements. Furthermore, the gas field division of this supply chain also had a considerable structure for the marginal profit maximization of outputs based on flare gas decreases. Additionally, the distribution lines of 0.80% supply chains provided wasted energy reduction by adding one extra unit to the line's capacity in the determined direction. Especially, there were supply chains that had investment opportunities for an acceptable abatement of power losses. This not only enables divisions to respond to fluctuations in demand as they produce more energy in critical situations like climate change but also decreases harmful emissions as wasted energy in supply chain divisions. [ABSTRACT FROM AUTHOR]