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• 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]

This article presents an inventory management model for profit maximization in a Peruvian retail company (FIDEL) using linear programming techniques and the Branch and Bound algorithm. To introduce this paper we refute, through a review of previous studies in the same field, the use of this approach for its effectiveness in business decision-making. Gathering data on costs, demand and the available capital for the company's different product lines was done through the use of interviews. The proposed model aims to maximize revenue based on the quantities of goods purchased, while considering minimum demand and capital constraints. Validation was performed with Lingo software to confirm the consistency and feasibility of the model. The resulting values revealed a significant increase in projected profits compared to actual data. [ABSTRACT FROM AUTHOR]

Shipping lines face various marketing risks, with outcomes that need to be mitigated. A current, serious, example of this has been the COVID19 pandemic and the severe supply chain disruptions that followed it. During such times, unmitigated marketing risks can lead to corporate failure, significant losses, or irreparable adverse impact on a carrier's brand name, due to sailing schedule unreliability. We cast a look at the marketing risks of shipping lines deriving, for instance, from adverse market developments, competitor moves, operational mistakes, unreliability (schedule integrity; supply of containers to customers, etc.), and we propose avenues of risk mitigation. Our methodology includes a combination of literature review and Spherical Fuzzy Theory. Among other conclusions, our results show that it is the 'unexpected' and 'unanticipated' -e.g., a natural disaster; a war; a tsunami; or COVID19-that poses the greatest risk to carriers: Intense competition amongst them, geared to short-term profit maximization, and coupled with the fine-tuning of capacity management within alliances, does not allow them the luxury of affording built-in buffers or slacks that could 'absorb' the unexpected. Not unexpectedly therefore, COVID19 has been the greatest risk factor of all times, mitigated –quite profitably one should add—by the joint capacity management of global shipping alliances. • In-depth literature review on marketing risks in shipping and risk mitigation strategies. • Risks mitigated: adverse market moves; competitor moves; threat of new entry; cybersecurity; unreliability. • Implements the Spherical Fuzzy Methodology in the marketing risk analysis of liner shipping • Highlights the most important marketing risks and risk mitigation strategies in shipping, to ensure resilience against crises. • External developments (e.g., COVID-19) pose the greatest risk due to lack of built-in buffers to absorb the unexpected. [ABSTRACT FROM AUTHOR]

This paper proposes a tractable bi-level model to explore the mode choice of travelers and optimal operation decisions of a public transit operator and a ride-sourcing operator under a multi-modal transportation system. In such a multi-modal system, users may travel by bus, metro, and/or ride-sourcing service. The lower-level network model is utilized to capture the equilibrium mode choices with the consideration of travelers' heterogeneity in terms of the value of time (VOT), where deterministic user equilibrium (DUE) and stochastic user equilibrium (SUE) are formulated and compared. In the upper-level model, subject to the DUE/SUE, the two operators optimize their operation decisions in the notion that the profit is maximized or the total system cost is minimized. The existence and uniqueness/non-uniqueness of the multi-modal mode choice equilibrium are analyzed. The optimal operation decisions are also compared under different operation regimes. Our results show that SUE model provides a more conservative estimate of the ride-sourcing operator's profit and total system travel cost compared to DUE. Besides, the total system cost can be reduced while the profit of the ride-sourcing company can be increased under appropriate operation decisions of the public transit operator and the ride-sourcing operator. Sensitivity analysis further indicates that travelers with longer total travel distance tend to use short-distance ride-sourcing services, which suggests that the ride-sourcing operator should dispatch more vehicles to suburbs located in remote areas to enhance service quality. • A tractable bi-level model for a multi-modal system with ride-sourcing services and multi-class travelers. • The existence and uniqueness/non-uniqueness of the multi-modal travel choice equilibrium are analyzed. • How the operation decision variables might affect users' travel choices and system performance is analyzed. • Total system cost can be reduced and ride-sourcing profit can be increased under appropriate operating strategies. [ABSTRACT FROM AUTHOR]

With the rapid development of modern information society, all types of advertisements emerge in an endless flow, where ambient media that emphasizes the multi-sensory experience of users rise as the times require. In this paper, based on the price stepping algorithm, a new ambient media advertising model based on O2O that integrates offline large-screen resources is proposed to further promote the development of ambient media in modern society. By introducing the price stepping (PPA) algorithm for sales and management analysis and sensitivity evaluation of actual goods, we aim to dynamically solve the marketing and inventory decision problem of environmental media advertising products, and on this basis, we use the price stepping (PPA) algorithm to make sales forecasts for high-cost goods, provide decision support for inventory and theoretical balance, and achieve theoretical maximization of environmental media advertising. The simulation results show that the PPA algorithm provides a more accurate sales forecast for high-cost goods, which in turn provides a joint decision idea to achieve inventory optimization and profit maximization, and provides an optimization method for merchants to balance inventory costs and marketing costs to maximize profits. [ABSTRACT FROM AUTHOR]

The ideal closed-loop supply chain (CLSC) for power battery recycling is "production - transaction - recycling - echelon utilization - remanufacturing". For the purpose of boosting the utilization efficiency of power battery, this paper constructs two models on the echelon utilization of power battery to influence of the government subsidies to the power battery manufacturer or the recycler on CLSC. We focus on exploring the influence of the retailer's green investment in logistics and the sharing costs of manufacturer on the equilibrium results and profits of CLSC. The results show that when the power battery manufacturer's sharing coefficient is maintained in a low range, the investment level of green logistics can be promoted and the profit maximization of the CLSC can be achieved, which has a positive effect on the establishment of a good corporate image and the increase of demand. Furthermore, the power battery manufacturer ought to choose reasonable sharing coefficient to the positive effect of cost sharing on CLSC. In addition to increasing the number of used batteries to be recycled, government subsidies can also enhance the recycled efficiency of power batteries. [ABSTRACT FROM AUTHOR]

This paper presents a multi-objective bi-level framework to model the distribution system operator's (DSO's) behavior in both wholesale and local electricity markets. The upper level is a multi-objective optimization problem from the DSO point of view including operation cost minimization, flexibility maximization, and peak load minimization. To this end, the DSO could not only participate in the wholesale electricity market at both day-ahead and real-time stages, but also is able to purchase power from renewable generations as well as the private owners. In this regards, the DSO could manage fluctuations of renewable power generations through considering flexibility as an objective function along with cost and peak-shaving. The lower level assigns to the private owner's profit maximization through interactions with the DSO. The DSO also exploits the demand response potential by implementing load shifting programs. The obtained results reveal that the proposed model could reduce the peak load by 6.05 MW and enhance the system flexibility level by 88 %. • To propose a bi-level multi-objective framework to model the energy trading mechanism. • The operation cost and system flexibility are considered as objective functions. • The private owner's profit maximization is modeled at the lower level. • The distribution system operator exploits the demand response potential. [ABSTRACT FROM AUTHOR]

The novel energy storage serves as a crucial infrastructure and a key enabling technology in shaping a new power system and advancing the green and low-carbon energy transformation. It stands as a vital support for achieving the dual-carbon strategy. In response to the high cost of deploying distributed energy storage and the potential for fraudulent behaviors in transactions, this research proposes an optimized operational strategy for fraudulent balancing in electricity trading considering the neighborhood scene public energy storage, taking into account potential fraudulent behaviors by market participants. Firstly, this research establishes a comprehensive framework for the joint operation of neighborhood scene public energy storage and multiple microgrids (MMG), developing the operational cost models for both MMG and public energy storage unit (PESU). Secondly, drawing upon the Nash bargaining theory, a cost minimization model is formulated to guide the collaborative operation of MMG and PESU. In addition, a transaction model is constructed to maximize profit distribution while considering fraudulent behaviors among market members. Finally, design a numerical example and solve it using the alternating direction method of multipliers (ADMM), the calculations suggest that the implementation of peer-to-peer (P2P) energy trading can increase the overall benefit by 65.45%, while the incorporation of PESU can boost the overall benefit by 49.93%, the fraudulent balancing model guarantees a sense of fairness in energy transactions. Addressing the potential obstacles to model expansion, further strategies involving the method of secondary distribution and setting an initial quotation range have been proposed to balance the fraudulent benefits among various entities and enhance participant enthusiasm. The fraudulent balancing strategy presented in this paper facilitates the achievement of fraud-balanced trading and maximization of profits among multi-energy entities, demonstrating considerable economic efficacy and high potential for widespread adoption. • Puting forth an innovative model for public energy storage suitable for neighborhood scenarios. • Demonstrating the fraudulent balancing point based on market fraudulent behaviors. • Putingforth an optimizing operational strategy for fraudulent balancing in energy cooperation. [ABSTRACT FROM AUTHOR]

In this paper, we analyse the interactions between Mobility Service Providers (MSP) in a multi-modal system. We formulate the problem using a bi-level structure extending Multi-modal Network Design (MND) principles. The upper level models the profit maximization objectives of multiple MSPs operating and offering different services to the users. At the lower level, users are divided into heterogeneous classes based on their travel characteristics, and assigned to a multi-modal super-network characterized by non-separable cost functions to account for interactions between modes. The problem is formulated as an Equilibrium Problem with Equilibrium Constraints (EPEC), which is characterized by a Variational Inequality (VI) Problem at the lower level, and a Nash equilibrium condition at the upper level. We apply an iterative solution algorithm that combines the classical Diagonalization method with an adaptive Extragradient Method to solve this complex problem and test it on several examples, demonstrating the validity and interpretability of the model and its results. These computations illustrate the competitive landscape faced by MSPs and the ranges of solutions where MSPs can be profitable, for small and medium sized examples. • Define super-network embedding class-specific multi-modal trip chains as hyperpaths. • Formulate upper-level equilibrium where players can compete or cooperate. • Solve Equilibrium Problem with Equilibrium Constraints, with lower level as a V.I. • Small and medium sized examples illustrate possibility for non-unique EPEC solutions. [ABSTRACT FROM AUTHOR]

This paper analyses the impact of the use of industrial robots on labor income shares at both the theoretical and empirical levels. On the theoretical side, the role of induced technological progress, the creation of new tasks, and the penetration of industrial robots on labor income shares are systematically explored by incorporating industrial robots into task models and endogenizing the task induced technological progress. Empirically, industrial robot penetration at the regional level in China is constructed from the industrial robots data released by the International Federation of Robotics (IFR), which is matched with Chinese A-share listed companies in 2011–2019, and the causal strategy of Bartik-style instrumental variables is used to analyze the impact of the penetration of industrial robots and task induced technological progress on the share of labor income. The study shows that the penetration of industrial robots significantly increases the share of labor income, but the task induced technological progress reduces the share of labor income, and that the impact of the penetration of industrial robots varies significantly external financing dependence, ownership, and regions. From the perspective of the impact mechanism, robot penetration increase the levels of inputs complementarity and hence reduce the elasticity of substitution between robots and labor. Meanwhile, robots can effectively increase the wage level of the labor force, thus increasing the labor income share. The findings of this paper provide a decision-making reference for how industrial robots can better serve human beings in the new era so that workers can share the fruits of economic development. • It presents a theoretical model integrating robots into profit-maximizing tasks to assess their impact on labor income share. • Robot penetration affects labor income share across external financing dependence, ownership, and regions. • Increased robot penetration raises the labor share through effects on the elasticity of substitution and wages. [ABSTRACT FROM AUTHOR]

[Display omitted] • A VPP model, trading energy with consumers and with the grid in regulated markets, is studied. • An exhaustive search framework is proposed for simultaneous investment costs minimization and profit maximization of VPPs. • The proposed framework aims to find optimal configuration of power plants' capacities/sizes aggregated in VPPs. • Energy management of VPP's output heat and power is solved on hourly resolution with Genetic Algorithm. • The proposed framework enabled the system to achieve 11 years payback period compared to 13 years of the baseline case. This research analyses the case of a Virtual Power Plant (VPP) in regulated electricity markets, trading energy with the consumers and the grid under a Power Purchase Agreement (PPA). The VPP propagates the deployment of solar PVs while balancing its intermittency with a dispatchable power plant, which is assumed in this research to be a CCHP, supplying cooling, heating, and power. The VPP also integrates energy storage systems for a comprehensive assessment. Traditionally, the VPP concept has not been introduced in regulated markets, but it is widely researched in deregulated markets where VPPs trade energy with the electricity grid for profit maximisation. In regulated markets, a special architecture is proposed for a VPP that mediates between residential compounds and electricity grids for profit maximization and energy demand coverage, thus converting the compound into a power generator with minimum dependence on the grid for its energy demand. In the literature on aggregated energy systems in regulated markets, it is usually overlooked to perform detailed energy modelling and optimisation on an hourly level. Only basic rule-based frameworks for energy management are proposed. In this research, it is initially assumed that since the VPP integrates multi-energy components supplying heating, cooling and electricity, optimization of the output of each component for a common profit maximization, is necessary. However, in VPP-related literature, the capacity of each component, which is a main input for energy modelling, is traditionally assumed and not assessed. Therefore, the research aims to explore how to find the optimal capacity configuration of the residential VPP that achieves optimal profit. The paper analyses an iterative exhaustive search framework, integrating the 2-levels of energy optimisation (hourly profit maximisation objective) and capacities optimisation (Life cycle CAPEX & OPEX minimisation). Compared to baseline cases, where only energy optimisation is performed, and capacities are assumed and not assessed in terms of capital investment, the proposed framework achieved a higher annual profit by 3.1 % and a payback period of 11 years. The results also provide comprehensive 3D charts drawing the relations between the achieved profit against capacities configurations, thus allowing high-level decision-making. The results also prove the hypothesis that hourly energy optimisation should not be performed without investment cost assessment and that targeting the minimization of investment costs will indirectly benefit the achieved profit. [ABSTRACT FROM AUTHOR]

• The taxpayer optimally chooses avoidance and evasion in a dynamic setting. • Avoidance only depends on its probability of success, its cost, and the tax rate. • Since tax audits are unable to deter avoidance, ad-hoc policies are needed. • Deterring avoidance might increase evasion to the point of reducing revenues. • A Laffer curve for revenues may arise due to opposite avoidance and evasion effects. We study an intertemporal utility maximization problem where taxpayers can engage in both tax avoidance and tax evasion. Evasion is costless but is fined if discovered, while avoidance is costly but might be successful (i.e. deemed legitimate) with a given probability (β) upon audit. We find that traditional deterrence instruments (fine and frequency of audit) reduce optimal evasion but, in contrast with results in a static framework, they have no impact on optimal avoidance. In fact, tax avoidance depends negatively on its marginal cost and positively on both its probability of success (β) and the tax rate. We show that non-compliance behavior may result in a Laffer curve for fiscal revenues and that the revenue maximizing tax rate is lower the higher β. We characterize the optimal level of β by taking into account different government objectives: minimizing evasion, minimizing non-compliance (evasion plus avoidance), or maximizing revenues. Our results suggest that specific policies (e.g., tax simplification) need to be implemented to deter avoidance and we illustrate their impact on evasion. [ABSTRACT FROM AUTHOR]

Urban mobility services face the challenge of planning their operations efficiently while complying with user preferences. In this paper, we introduce a new mathematical model called a choice-driven dial-a-ride problem (CD-DARP) which is a generalization of the dynamic DARP where passenger behavior is integrated in the operational planning using choice models and assortment optimization. We look at two types of mobility services, private and shared. Our problem extends the dynamic DARP by (i) changing its objective function to profit maximization, where both cost and revenue are variables, and (ii) incorporating assortment optimization with routing decisions in a dynamic setting. We propose a pricing scheme based on a choice model designed to offer service alternatives at the time a customer makes a request. We introduce a tailored algorithm to efficiently solve the dynamic CD-DARP. Computational results indicate that our proposed approach outperforms dynamic DARP in terms of reducing routing costs and improving the number of customers served. • A new framework is introduced for DDARP incorporating customer choice. • The routing cost is reduced while satisfying customers' requests. • CD-DARP provides more flexibility for the operator to efficiently manage the fleet. • We evaluate the proposed model on synthetic as well as New York taxi data. • Flexible time windows helps improving the service level. [ABSTRACT FROM AUTHOR]

• This article provides a unique way of representing economic enhancement for the distribution side & transmission side. • This article proposed a novel scheduling approach for the profit maximisation for the distribution side and maximisation of the social welfare for the transmission side, considering all the system constraints. • This paper introduces a novel multiobjective optimisation approach for showing the economic benefit for the distribution side & transmission side, this optimization also shows a faster approach compared to the conventional optimisation. Introducing renewable energy sources (RES) has enhanced the economic benefits in the energy market. However, due to the unpredictable nature of RES, optimal usage of these sources is essential from an economic point of view. To obtain the maximum benefit, this paper presents a novel way of power scheduling named reconfigurable scheduling (RGMGS) for the distribution side (DS) and transmission side (TS) at the same go. The proposed method has shown a significant improvement in the profit (PR) for DS and social welfare (SW) for TS simultaneously. Both cases have shown a 10 %-15 % improvement using the proposed method; this scheduling has also reduced the power loss in the DS by 20 %. Moreover, the proposed scheduling has also improved the Locational Marginal Prices (LMP) in TS by 3 %-4 % per hour, facilitating effective economic transactions and contracts. To get the best-optimized results, a novel multi-objective improved time-varying acceleration coefficient particle swarm optimisation (ITVACPSO) has also been proposed, which has shown a better and faster result compared with the conventional optimisation tools PSO, TVAC-PSO & Genetic Algorithm (GA). The modified IEEE 69 bus & modified IEEE14 bus systems have been considered for validating the proposed approach. [ABSTRACT FROM AUTHOR]

Designing an electricity price mechanism that efficiently enables users to manage their self-production and consumption behavior to accommodate renewable energy and regulate peak load is a hard task with the emergence of prosumer with photovoltaic and energy storage. To address this issue, we propose an iterative two-layer optimization to investigate customized price of electricity for different types of users in the presence of photovoltaic uncertainty. By taking into account power flow restrictions, the model is used to explore the optimal time-of-use, demand, and on-grid prices for both electricity consumers and prosumers. The consumer and prosumer peak-flat-valley periods are initially divided in the upper layer. Then, a model for distribution system operator profit maximization is proposed to minimize operation cost, power loss, and peak–valley difference. A stochastic optimization model is presented in the lower layer for consumer and prosumer to hedge against the risk of uncertain photovoltaic with the aim of minimizing electricity tariff, demand tariff, and life cycle cost of energy storage. Numerical experiments conducted on modified 15-bus and 69-bus distribution systems with different types of users show the effectiveness of the iterative two-layer optimization for customized price of electricity in peak-shaving and valley-filling while ensuring the interests of distribution system operator and users. Results show that the total profit increases by more than 4% and the net load fluctuation variance reduces higher than 20% compared with the traditional pricing method. • An iterative two-layer optimization is proposed to investigate CPoE. • Different types of users are considered in the presence of PV uncertainty. • Total profit increases by more than 4% compared to other pricing methods. • Net load fluctuation variance reduces higher than 20%. [ABSTRACT FROM AUTHOR]

Many retailers are small and medium enterprises (SMEs) with limited access to capital to finance their operations. Here, we consider a supply chain consisting of a capital-constrained retailer and a manufacturer and examine the effect of equity and debt financing schemes on their operational decisions. Using a game theoretical approach, for the risk-neutral scenario, we take expected profit maximization as the participants' objective to examine the equilibria of their operational and financing decisions. We also examine the condition under which the manufacturer and retailer adopt equity or debt financing to earn additional profits. We find that when the retailer is highly capital-constrained, she benefits from either financing scheme, but the manufacturer prefers to offer equity financing over debt financing. Otherwise, if the retailer is less constrained, debt financing is preferable. We also extend our analysis to risk-averse participants using the conditional value-at-risk (CVaR) criterion. We show that the decreasing monotone property of the order quantity in the wholesale price, known in the risk-neutral setting, no longer holds. Indeed, the relationship depends on the participants' risk aversion level; if the manufacturer is highly risk-averse, it chooses to provide equity financing to enhance its CVaR performance; otherwise, it offers debt financing. Our findings provide implications for decision-makers in choosing the optimal financing options and making the associated operations and financing decisions when considering risk attitudes. • We examine conditions for participants to adopt equity (EF) or debt financing (DF). • The manufacturer prefers EF if the retailer is highly capital-constrained. • Risk aversion level affects participants' operational and financial decisions. • The highly risk-averse manufacturer prefers EF to enhance its CVaR performance. • We provide implications for optimizing their financing and operations decisions. [ABSTRACT FROM AUTHOR]

• Develop an eUnit-based p-hub location problem for optimal urban air mobility (UAM) network design. • Linearize the eUnit choice probability to create a mixed integer linear program (MILP). • Provide three design schemes, including revenue, profit, and profit with pricing maximization. • Illustrate the impacts of flight ranges and eUnit constraints on the optimal solution. Urban air mobility (UAM) technology has the potential to revolutionize daily commutes. By leveraging low-altitude airspace, electric vertical take-off and landing (eVTOL) vehicles can provide faster urban transportation between skyports than ordinary surface transport modes. This paper develops a multiple allocation incomplete p-hub location problem for determining the optimal UAM network design under the flying range constraint of eVTOL vehicles. The traditional methods used mathematical programming (MP) to analyse either 1) a deterministic allocation with a flight range limitation or 2) a stochastic allocation using a choice model without a range constraint. We combine the two methods by adopting a recently proposed eUnit choice model in a mixed integer linear programming (MILP) formulation to consider the interaction between travel choice behavior and travel cost incurred by skyport locations and their linkages in the random utility maximization framework. Three schemes of the multiple allocation incomplete p-hub location problem are provided, including revenue maximization, profit maximization, and profit maximization with pricing strategy. Numerical examples are provided to investigate the influence of flight range constraint and the eUnit bound on the solutions, which show significant influence on the UAM network topology and demand allocation. [ABSTRACT FROM AUTHOR]

The synchronization of production capacity with demand plays a significant role in maximizing profit. Firms attempt to align their activities, requiring that businesses be occasionally turned away. A trade-off exists between the revenue gained by a specific order and all related processing costs. We solve an important order acceptance and scheduling problem arising from this business model. A firm that receives multiple orders must determine: (i) whether to accept or reject any order, (ii) how to assign accepted orders to identical parallel machines, (iii) the sequence of accepted orders, and (iv) when to schedule order starting times. The objective is to maximize the total profit, measured as the difference between revenue and tardiness penalty costs. We propose an effective hybrid algorithm (GA-RP-TS) that combines a refining process (RP), a genetic algorithm (GA), and a tabu search (TS) step to tackle this NP-hard problem. The hybrid GA-RP-TS employs GA's strong global search ability, uses RP to improve a solution, and performs exploitation with TS. In particular, the proposed GA-RP-TS balances the intensification and diversification ability of metaheuristics and evolutionary algorithms. We evaluate the performance of the proposed GA-RP-TS by testing benchmark instances. Results show that the proposed GA-RP-TS outperforms the state-of-the-art algorithm in solving large-sized instances. GA-RP-TS obtains 22 new best solutions out of the 54 tested benchmark instances, besides helping prove optimality for two instances. In addition, GA-RP-TS consumes less computation time. Sensitivity analysis shows that the excellent performance of GA-RP-TS comes from the hybridization of its three components, i.e., GA, RP, and TS. • We develop an efficient hybrid heuristic for the order acceptance and scheduling problem. • The heuristic combines the strengths of a genetic algorithm, tabu search, and a refining process. • The performance of the heuristic is demonstrated using benchmark instances. • The heuristic successfully identifies 22 new best solutions. • The heuristic's excellence is attributed to the hybridization of its three components. [ABSTRACT FROM AUTHOR]

Currently, the retail sector is quite competitive. The proper placement of products and giving them shelf space is crucial to the industry's success. On planogram, in order to achieve better visibility, the products may be placed on multiple shelves on multiple orientations. The main purpose of this work is to develop and solve a shelf space allocation problem that takes into account shelf, product, orientation, multi-shelves allocation factors. These reflect the realistic conditions of the retail environment. In this paper, a new approach has been used to transform a non-linear shelf space allocation problem into a linear one. We explore how to adopt a more strategic approach of replacing the product of two binary variables and a product of binary and continuous variables in the constraints imposed by the retailer. This allows achieving the optimal solution of product on shelves allocation with the goal of profit maximization. The experiments were performed with the help of a commercial CPLEX solver. [ABSTRACT FROM AUTHOR]

In this research, we proposed a practical shelf space allocation model with the vertical and horizontal categorization of products. Five groups of constraints, such as shelf, product, multi-shelves, orientation and band constraints, are implemented in the model. We proposed two heuristics to solve the retailer's profit maximization problem. The experiments were performed on small instances, and the solution was compared to the optimal solution found by the CPLEX solver. The experiments proved that heuristics could find the optimal solution in most cases (14/20 for heuristics H1 and 16/20 for heuristics H2) without checking the whole solutions space. The lowest solution quality is 97.68% which shows that the heuristics allow finding high-quality near-optimal solutions. Therefore the steps implemented in the proposed heuristics should be used in more complicated shelf space allocation problems for which it is impossible to find a solution by the solvers. [ABSTRACT FROM AUTHOR]

Online social network has deeply changed our lives, such as the style of communication and business, and hence promotes a lot of researches in social influence. The prior works in social influence mainly consider the influence from the view of individuals. However, in many cases, influencing the most of members of an important group such as the board of directors in a company can bring bigger profit than directly influencing the individuals of the company. We call such high profit group which obeys the vote rule as an union, different from existed targeted influence model, we consider such scenarios to make union acceptable and propose the union acceptable profit problem (UAPM) to choose seeds to maximize the union-acceptable profit, i.e., maximize the probability of the union being acceptable. The objective of profit in UAPM is #P-hard, and not submodularity or supmodularity. To solve the problem, we propose an efficient estimation method for the objective and design a heuristic algorithm and further a data-driven β (1 − 1 ϵ) -approximation algorithm where β is the data-driven parameter which is related to the input data. At last we evaluate the performance of the algorithms we proposed on effectiveness and efficiency by the experiments in real-world social network datasets. • We formulate a new problem of UAPM which proved to be hard to solve. • An efficient estimation method for the objective is proposed based on an optimized sampling algorithm. • We propose a heuristic algorithm and a data-driven approximation algorithm respectively to solve the problem. • Experiments with the real-world databases validate the effectiveness of our algorithms. [ABSTRACT FROM AUTHOR]

• Model monopoly ride-sourcing marketwith human-driven vehicles and autonomous vehicles; • Consider congestion externality under mixed traffic flowof HVs and AVs; • Consider heterogeneity onride-sourcingriders' perceived utility; • Reveal non-monotonicity ofdemand rates with respectto platform's strategies; • Gain managerial insightsinto operational strategies of mixed on-demand ride services; • Explore an extended scenario considering the integrated serviceof HVs and AVs. We model a monopoly ride-sourcing market where the platform adopts the service types of human-driven vehicles (HVs) and autonomous vehicles (AVs). Both congestion externality under mixed traffic flow and heterogeneity on riders' perceived utility of the ride-sourcing service are considered when formulating the mode choice behavior of riders. We analyze the impact of the platform's fleet size and its price for riders on demand rates and riders' waiting time in the market equilibrium state. The analytical results show that the demand rates of mixed on-demand ride service types are not necessarily monotonous to the price for riders or the fleet size, due to the congestion externality and existence of a wild goose chase regime. Under either profit maximization or welfare maximization strategies, numerical results demonstrate that a higher pure AV traffic flow capacity benefits human ride-sourcing drivers and both types of riders. The platform should arrange more AVs than HVs even under the high AV depreciation cost. In a surging demand scenario, the platform should encourage riders to switch from the HV service to the AV service through price regulation. Moreover, an extended scenario considering the integrated service is discussed. The economic analysis and gained managerial insights benefit the on-demand ride services platform's decision making on operational strategies of HVs and investment in AVs with mixed traffic congestion effects. [ABSTRACT FROM AUTHOR]

• We broaden the content of the profit maximization problem of the product company based. • We proposed a Two-stage Pricing with Discount Model (TPDM). • We proposed a Two-stage with Discount Greedy algorithm (TSDG) and evaluated its performance. The rapid development of online social networks (OSNs) has changed the way people communicate and has brought many new opportunities for Internet product marketing. For a product company, the profit gained is directly related to the price of the product and the number of products used. Designing an effective marketing strategy is crucial to obtaining more profits. However, most existing research only focuses on maximizing product influence, rather than explicitly incorporating pricing factors into the design of marketing strategies. This article studies product marketing strategies and pricing models, and proposes a two-stage pricing discount model (TPDM). The TPDM model divides product marketing into two stages: the original price stage and the discount stage. It also studies the impact of advertising marketing (AM) and word-of-mouth marketing (WM) on the number of products used. Based on the TPDM model, a two-stage discount greedy (TSDG) algorithm is proposed to realize product pricing for product companies. At last, we use several real social network data sets for comparative experiments. The experimental results show that the TSDG algorithm divides the product marketing into two stages and pricing can tap the economic benefits of the product, so that more users can use the product. Compared with other algorithms, it not only increases the profit of the product company by more than 20%, but also improves efficiency. [ABSTRACT FROM AUTHOR]

The retailers are interested in different profit maximization approaches, which help them to perform daily shelf arrangement tasks, improve customers' satisfaction and avoid out of stocks on the shelves. Most of the retail literature proposes basic models which don't reflect complicated merchandising rules. The aim of the paper is to develop a shelf space allocation model which investigates vertical shelf levels, horizontal shelf division into segments, and product item allocation rules such as cappings and nestings. A genetic algorithm has been developed to implement the model. The efficiency was estimated with the help of CPLEX solver. The computational experiments show that the proposed approach allows getting the results of sufficient quality for different problem sizes in a short running time without requiring large computing resources. GA and CPLEX found statistically the same profitable solution within the same computational time. However, GA found a better solution in situations where the commercial solver can't perform calculations during the increased computational time and stopped after a couple of minutes. This proves the reason for GA implementation for shelf space allocation problems. [ABSTRACT FROM AUTHOR]

Electric scooter (e-scooter) sharing systems provide on-demand electric scooter rental services. E-scooters are equipped with swappable batteries managed by staff who visit each scooter and replace depleted batteries with charged ones. The e-scooters in this system are free-floating; they can be located anywhere without having to be returned to designated stations. Due to this characteristic of the scattered location of e-scooters, operation decisions with associated staff routing are more challenging than station-based services. Thus, it is critical to implement the efficient management of e-scooter redistribution and charging decisions with proper staff routing to successfully prepare for user demand within a limited operation time while minimizing the total operating cost. To this end, we introduce a battery swapping and vehicle rebalancing problem with staff routing for e-scooter sharing systems, formulated as a mixed integer programming (MIP) model. To derive solutions efficiently for a large-scale instance in practice, we propose a clustered iterative construction approach where the problem is decomposed into two phases. The first phase clusters regions using an approximation of intra-region and inter-region operation costs with the minimum spanning tree approach. The second phase efficiently derives multiple candidate regional sequences by our partial permutation procedure and following the sequences, iteratively solves a significantly reduced size of the problem to construct operation assignments. Our numerical experiments on the generated instances and real-world instances demonstrate that the proposed two-phase algorithm shows significantly superior performance in practically large scale instances than the benchmarks without clustering or the iterative procedures of our algorithm. • We consider a e-scooter sharing system with free-floating property. • We construct a profit maximization model incorporating operations for each scooter. • We develop a novel solution approach to handle a large-scale problem efficiently. • Numerical results demonstrate significant advantage of our approach over others. [ABSTRACT FROM AUTHOR]

In social advertising, the social platform host may run marketing campaigns for multiple competing clients simultaneously. In this case, each client comes up with a budget and an influence spread requirement. The host runs campaigns by allocating a set of seed nodes for each client. If the influence spread triggered by a seed set meets the requirement, the host can earn the budget from the corresponding client. In this paper, we study the problem of Profit Maximization, considering that different seeds incur different costs. Given all the clients' requirements met, we aim to find the optimal seed allocation with minimum cost. Under the competitive K-LT propagation model, we show the Profit Maximization problem is NP-hard and NP-hard to approximate with any factor. To find a feasible solution, we propose an effective algorithm that iteratively selects a candidate set and obtains an approximate allocation. The experimental results over a real-world dataset validate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

• Framework including the interactions between the users and the operator's decisions. • Linear representation of choice models for mixed-integer linear programming. • Proof-of-concept including a mixed logit model in a profit maximization problem. • Evidence of the tractability of the approach for small size problems. In many transportation systems, a mismatch between the associated design and planning decisions and the demand is typically encountered. A tailored system is not only appealing to operators, which could have a better knowledge of their operational costs, but also to users, since they would benefit from an increase in the level of service and satisfaction. Hence, it is important to explicitly allow for the interactions between the two in the model governing the decisions of the system. Discrete choice models (DCM) provide a disaggregate demand representation that is able to capture the impact on the behavior of these decisions by taking into account the heterogeneity of tastes and preferences of the users, as well as subjective aspects related to attitudes or perceptions. Despite their advantages, the demand expressions derived from DCM are non-linear and non-convex in the explanatory variables, which restricts their integration in optimization problems. In this paper, we overcome the probabilistic nature of DCM by relying on simulation in order to specify the demand directly in terms of the utility functions (instead of the choice probabilities). This allows us to define a mixed-integer linear formulation that characterizes the preference structure and the behavioral assumption of DCM, which can then be embedded in a mixed-integer linear programming (MILP) model. We provide an overview of the extent of the framework with an illustrative MILP model that is designed to solve a profit maximization problem of a parking services operator. The obtained results show the potential of the proposed methodology to adjust supply-related decisions to the users. [ABSTRACT FROM AUTHOR]

• An integrated model to jointly determine line configuration, line frequency and passenger assignment on a urban rail transit (URT) network is proposed by taking interplay between operator-oriented cost and passenger-oriented cost into account. It specifically considers (i) transfer waiting time, (ii) the use of the same tracks for trains belonging to different lines, and (iii) capacity restriction of trains. • The line planning problem in an URT network with passenger path assignment is formulated as a mixed-integer non-linear programming (MINLP) model with linear constraints. • An outer approximation method is developed to convert the original MINLP to mixed-integer linear programming (MILP). The experiment shows that approximation is actually very tight with a gap under 5.6 × 10–6. • A real case study based on the Hong Kong MTR network is conducted to test the performance of our approach. We also propose a method to determine the appropriate coefficients for the model and test the impacts of different candidate line pool size and the number of paths considered on the model. This paper considers a line planning problem in an urban rail transit (URT) network with passenger path assignment. The line planning problem involves both line configuration and frequency setting for the optimization of a combination of operator-oriented objective (minimization of operating costs or maximization of profits) and passenger-oriented objective (minimization of total travel times). The interplay between the line planning and the passenger assignment is explicitly taken into account and the problem is formulated as a mixed-integer non-linear programming (MINLP) model with linear constraints. In order to take advantage of existing commercial mix-integer linear programming (MILP) solvers, we propose an outer approximation method to linearize the objective. Finally, the model and solution algorithm are illustrated with a simplified version of the Hong Kong Mass Transit Railway (MTR) network. [ABSTRACT FROM AUTHOR]

• This study integrates carsharing and parking sharing services. • This study examines both profit maximization and social optimum cases. • This study compares bundled sharing services against carsharing-only service. • This study establishes conditions where bundled sharing services can increase profit. To improve the appeal of carsharing, we propose an integrated operation scheme of carsharing and parking-sharing services, where a carsharing operator rents parking spaces from private owners to provide convenient parking options to carsharing users. We examine how the operator's profit and social welfare differ under the existing carsharing-only service scheme and a bundled carsharing and parking-sharing service scheme. In particular, multiple groups of decision makers, i.e., suppliers of shared parking spaces, platform operators, carsharing users and private vehicle users, and interactions among them are modeled in the context of an integrated sharing platform. The properties of carsharing user and private-vehicle traveler choice equilibrium are discussed. The profit-maximizing and social-optimal platform pricing and supply strategies are explored. Numerical examples illustrate that the bundled carsharing and parking-sharing service scheme holds the potential to improve the operator's profit and social welfare. [ABSTRACT FROM AUTHOR]

This article examines the institutionalization of organic agriculture in Finland's agricultural policy. Organic agriculture is one of the main alternatives for conventional food production, promoting values that go beyond the focus on productivity and profit. Yet, numerous studies have raised concerns over the changes institutionalization has produced in its transformative capacity. It has been claimed that institutionalization results in the economization of organic agriculture, whereby the goals of this alternative mode of production are displaced by market coordination and profit maximization. However, studies have also called for more detailed analyses, which would account for e.g. national variations in the development of organic agriculture. To further this discussion, we apply conventions theory to analyze how policy documents, strategy papers, and studies have justified the institutionalization of organic agriculture in official policy in Finland. Conventions theory has the benefit of examining both the moral arguments and the knowledge production related to this. From this perspective, we investigate how organic values have been shaped and how policies have attempted to resolve tensions between conflicting justifications. The results show that compromises between organic values and economic rationales have been used to promote organic agriculture, which has also promoted economization. However, the results also indicate that in addition to economization, organic values have faced other challenges. The value of organic agriculture has been questioned based on, for example, scientific measurements of organic products, and by comparisons to Finnish conventional production. Both challenges have questioned the significance of organic agriculture in terms of its health and ecological benefits. The results therefore indicate that more contextualized analytical models are needed when examining the institutionalization of organic agriculture. • Compromises between economic and value-oriented motives in institutionalization. • Uncertainties related to research knowledge and national contexts. • Methodological sensitivity to compromise settings. [ABSTRACT FROM AUTHOR]

• We show many follow-on patents that cover marketed drugs originated from partners. • Maximizing profit requires pharmaceutical firms manage partners' follow-on patents. • We offer several strategies to manage external patents in open innovation projects. • We found that some therapeutic areas utilize more follow-on patents than others. Proper implementation of lifecycle management strategies is important for profit maximization in the pharmaceutical industry. This article investigates the ownership of follow-on patents for approved drugs in the USA. We discover that a considerable number of follow-on patents are originally developed by, and issued to, external partners and not drug sponsors. We also find that the use of follow-on patents is more common in some therapeutic areas than in others. Based on these findings, we develop lifecycle strategies to effectively manage and use external patents in open innovation projects. These strategies include proper license contract design, post-FDA-approval acquisition and open innovation. [ABSTRACT FROM AUTHOR]

• Renewable energy communities (REC) are starting to shift political power structures. • These shifts are visible through increases in capacity, coalitions and social norms. • Electricity decision-making is rescaling from centralised levels toward the local and city level. • Where governments are not supportive, this rescaling can be subversive and uncoordinated. • Decentralised ownership may either transform economic and political structures, or conform to existing patterns. The energy transition is increasingly high stakes, defined by the participation of new actors, and – above all – political. Renewable energy communities (RECs) are electricity system actors defined by local control and motivations beyond profit maximization. There has been much speculation about the potential impact of RECs on political systems, but little empirical investigation. This study examines (a) if RECs are shifting larger political power structures, (b) the mechanisms through which shifts are occurring, and (c) the implications of shifts for the future. It compares across the Netherlands, United Kingdom and Ontario, Canada. Findings reveal that political power is shifting but outright policy wins are rare. RECs are gaining capacity, building political coalitions and benefiting from changing norms. However, they are challenged by pressures from incumbents to maintain centralised ownership and control. Energy action, both sanctioned and subversive, is rescaling toward local levels. Implications for energy and social policies are significant. [ABSTRACT FROM AUTHOR]

• A choice experiment was performed on third-party developers of RE projects in Sweden. • Wind and solar developers prioritize different stakeholder interests. • Wind developers prioritize the electricity output of projects. • Solar developers prioritize client satisfaction. • Intermediaries did not prioritize their own interests over their clients' interests. A transition towards a low-carbon energy system requires the large-scale diffusion of renewable electricity (RE) technologies. Intermediary actors, such as installers, project developers or consultants, can facilitate this process by supporting RE adopters. Recent studies have also pointed out that there are potential drawbacks associated with intermediary support. Acting as filters and gatekeepers, intermediaries have the complex mission to prioritize between various stakeholder interests, including their own profit maximization. The aim of this research is to understand what stakeholder interests intermediaries prioritize when developing RE projects. Results of a choice experiment with third-party developers in Sweden highlight differences between technologies: wind developers prioritize electricity output, while solar developers prioritize client satisfaction. Despite being for-profit organizations, intermediaries did not prioritize their own interests over the interests of their clients. Solar PV developers prioritize client satisfaction over maximization of electricity output, which may have negative implications for the efficiency of the projects. [ABSTRACT FROM AUTHOR]

This paper explores the connections between indebtedness, discipline and economic growth in the context of rural Colombia. It investigates the expansion of interest-bearing debt and the transformations that this has elicited on the lives of peasants in the country's most important hub of cocoa production: El Carmen de Chucurí. Within a framework combining agrarian studies, political economy and economic anthropology, the study is based on ethnographic data which allowed a nuanced understanding of the phenomena at play. In order to comply with timely debt repayment, peasants have been forced to adopt "maximizing strategies" based on a specific (capitalist) form of economic rationality, and to extend their work routines at the expense of their health. Theoretically, I argue that these transformations of their mindsets and bodies, respectively, can be understood as part of the overarching disciplining effects of debt. In particular, the changes brought about by debt have materialized in a renewed push towards increasing cocoa's productivity and profitability. By focusing on the consequences of indebtedness on the debtors' lives, this paper challenges the conventional idea that economic growth is only the outcome of a "natural desire" for improvement. Instead, rising productivity of cocoa can also be read as the result of a compulsion for growth in order to repay loans. This has important implications for analysing the rise and evolution of capitalism. In advancing some of its key imperatives (i.e. accumulation and profit maximization), debt repayment can be seen as a major channel for the reproduction of capitalism in the countryside. • Expansion of interest-bearing debt has explicit ties with the evolution of capitalism. • Economic growth is not solely the outcome of a "natural desire" for improvement. • Debt is a factor forcing economic growth and not only a cause of stagnation. • Indebtedness shapes rural communities' ways of thinking and behavior in line with a capitalist logic. [ABSTRACT FROM AUTHOR]

In this paper, we propose an optimization technique applying an auction-based mechanism for peer-to-peer energy trading in a smart grid. In our proposed system model we have assumed proper infrastructure and communication to allow connected users to trade energy in a peer-to-peer fashion. The mechanism requires complete knowledge of the different constraints tied to the grid infrastructure such as the status of the paths connecting the suppliers and consumers. This view on the underlying infrastructure is imperative not only to prevent power losses and over utilization of the power lines but also to factor the cost of energy transfer in the trading process. We investigate a sealed bid auction model where each player can participate in the bidding process to maximize its benefit according to current and future energy availability, price variations, the distance between traders and energy transfer costs. The evaluation of the proposed model is examined through theoretical analysis as well as experimental results. This work further demonstrates a profit maximization algorithm (PMA) for energy suppliers which generates high profit return. [ABSTRACT FROM AUTHOR]

New product development is an important innovation that should be done to meet consumers' new trends. This paper is prominent as it discusses new product development through the inactive problem perspective in SMEs in Indonesia as a way to explore customers' expectations, which are often challenging to analyze. The capability to develop a new product based on the inactive problem perspective allows SMEs to become pioneers that possess various competitive advantages, including strong brand awareness. This study included 14 samples of SMEs to represent the characteristics of SMEs in Indonesia between 2016 and 2018. We used the particle swarm optimization approach for the decision making of several SME parameters with the profit maximization function. This paper is the first paper to discuss the relationship between the cost of ideas and production costs in the types of SMEs with an inactive problem perspective in Indonesia to obtain maximum profits by considering various factors that affect the costs and revenue of SMEs. This paper also analyzes the cost used for an idea realization to get maximum profit. This study shows that differences were found in each type of SME, and these difference were related to the amount of funds that were used to explore, realize, and make a profit out of a specific idea. [ABSTRACT FROM AUTHOR]

The fisheries in Indonesia is multi-gears fisheries, including in Rembang Regency. Rembang Regency had fishery production of 36,243 tons in 2017 and very dependent on purse seine and Danish seine fisheries. Although they have different fishing operation, but types of fish caught by purse seine and Danish seine are partly of the same species. The interrelation of fish species caught shows the risk of interrelation between purse seine and Danish seine (negative externalities) which can encourage overfishing. The purpose of this research was to make the model of relationship between Danish seine and purse seine fisheries in Rembang Regency with a bio-economic approach. We have modified Gordon-Schaefer model (single gear) to be a multi-gears model that can explain the reciprocal relationship between Danish seine and purse seine fisheries, and also we did optimization of production and profit. This research has proven that Danish seine fishing efforts have a negative impact on the production of purse seine fisheries, and vice versa. The combination of 328 units of purse seine and 304 units of Danish seine will produce optimal aggregate production (62,286 tons per year). While the combination of 176 units of Danish seine and 348 units of purse seine will generate an aggregate profit of IDR 510 billion per year as the win–win solution for both Danish seine and purse seine fisheries. The highest aggregate profit will occur at 370 units of purse seine fisheries and 0 units of Danish seine, which will generate a profit of IDR 535 billion per year. [ABSTRACT FROM AUTHOR]

We study an evolution cross-diffusion problem with mutualistic Lotka–Volterra reaction term to modelize the long-term spatial distribution of labor and capital. The mutualistic behavior is deduced from the gradient flow associated to profits maximization. We perform a linear and weakly nonlinear stability analysis and find conditions under which the uniform profits optimum becomes unstable, leading to pattern formation. The patterns alternate regions of high and low concentrations of labor and capital, which may be interpreted as cities. Finally, numerical simulations based on the weakly nonlinear analysis, as well as in a finite element approximation, are provided. [ABSTRACT FROM AUTHOR]

Given a social network G and a positive integer k , the influence maximization problem seeks for k nodes in G that can influence the largest number of nodes. This problem has found important applications, and a large amount of works have been devoted to identifying the few most influential users. But most of existing works only focus on the diffusion of a single idea or product in social networks. However, in reality, one company may produce multiple kinds of products and one user may also have multiple adoptions. For multiple kinds of different products with different activation costs and profits, it is crucial for the company to distribute the limited budget among multiple products in order to achieve profit maximization. The Profit Maximization with Multiple Adoptions (PM2A) problem aims to seek for a seed set within the budget to maximize the overall profit. In this paper, a Randomized Modified Greedy (RMG) algorithm based on the Reverse Influence Sampling (RIS) technique is presented for the PM2A problem, which could achieve a (1 − 1 / e − ε) -approximate solution with high probability and is also the best performance ratio of the PM2A problem. Comprehensive experiments on three real-world social networks are conducted, and the results demonstrate that our RMG algorithm outperforms the algorithm proposed in [16] and other heuristics in terms of profit maximization, and could better allocate the budget. [ABSTRACT FROM AUTHOR]

Viral marketing has become one of the most effective marketing strategies. In the process of real commercialization, in order to let some seed individuals know the products, companies can provide free samples to them. However, for some companies, especially famous ones, they are more willing to offer coupons than give samples. In this paper, we consider the Profit Maximization problem with Coupons (PM-C) in our new diffusion model named the Independent Cascade Model with Coupons and Valuations (IC-CV). To solve this problem, we propose the PMCA algorithm which can return a (1 3 − ε) -approximate solution with at least 1 − 2 n − l probability, and runs in O (log ⁡ (n p) ⋅ m n 3 log ⁡ n (l log ⁡ n + n log ⁡ 2) / ε 3) expected time. Furthermore, during the analysis we provide a method to estimate the non-monotone submodular function. [ABSTRACT FROM AUTHOR]

• A model-based multiscale design approach is considered for emulsion design. • The strategy focuses on raw materials and energy efficient use. • The proposed strategy aligns with Goal 12 of the UN's 2030 Agenda. • Consumer preference maximization may not necessarily correlate with higher profits. • Profit maximization properly balances consumer preference and economic performance. There is an increasing demand for sustainable and cost-effective chemical products, such as eco-friendly emulsified products, contributing to a resource-efficient economy and a reduction in our ecological footprint. This new paradigm challenges the chemical industry due to a lack of accurate predictive models and understanding of emulsified systems. This study presents an optimization strategy to design cosmetic emulsions through a multiscale approach, which represents an opportunity to boost and improve the design of these products. The study employs a model-based design approach considering operating conditions, product formulation, economic performance, consumer preference, and the emulsions properties at different scales. At the macroscopic scale, rheological and textural properties are considered and coupled with a Population Balance Model, which estimates the drop size distribution (i.e., microscopic scale). The optimal emulsion formulation is established for consumer preference and product profit maximization scenarios. According to the economic model, a higher level of consumer preference is expected to lead to increased product sales. However, the results also indicate that consumer preference maximization may not necessarily correlate with higher profits. Thus, the profit maximization scenario offers a better balance between consumer preference and economic performance. Also, experimental validation was performed for the product properties and operating conditions. [Display omitted] [ABSTRACT FROM AUTHOR]

• Dry ports provide significant economic, environmental and social benefits. • Effective dry port network design is critical for effective maritime logistics. • Presents an integrative framework considering reverse logistics in dryport operations. • Developed a Hesitant Fuzzy Step-wise Weight Assessment Ratio Analysis and Complex Proportional Assessment (HF-SWARA-COPRAS) Dry ports are crucial to marine logistics, providing significant economic, environmental, and social benefits. Effective network design is key to their success, and decisions on location and capacity must consider both objective data and expert opinions to avoid costly long-term consequences. This research presents an integrative framework that combines a fuzzy multi-criteria decision-making method with fuzzy stochastic chance-constrained optimization, forms an integrated multi-objective model, and solves it using the epsilon-constraint approach. The demands for imports and exports are presented in a scenario-based manner, and in each scenario, demands are expressed as trapezoidal fuzzy numbers. To guarantee the level of confidence in satisfying demands, a fuzzy chance constraint method has been used and the stochastic objective function has been transformed into a deterministic counterpart using a novel robust optimization technique. The paper is also among the first to consider reverse logistics from inland to seaports through dry ports to highlight their role in marine export. Since the output of the model is highly dependent on the parameter settings, the mechanism of the parameters' impact on the profit maximization objective function is investigated through a full factorial experiment. The results of the mathematical model proposed in this research, which has been tested on an Iranian case study suggest that high-capacity dry ports should be established in the cities of Sirjan and Tehran, while smaller dry ports should be built in the cities of Isfahan, and Ahvaz. Also, the model suggests that the seaports of Chabahar and Anzali should be connected to Iran's national railway network so that the transportation of goods from them to dry ports on the hinterlands in Sirjan and Tehran can be done at a lower cost. [ABSTRACT FROM AUTHOR]

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

This paper proposes a conceptual framework to guide the transformation of undergraduate economics education, anchored in integrated human flourishing and ecological sustainability. The framework moves beyond the dominant focus on utility and profit maximization to encompass holistic goals of universal human development, social equity, and ecological sustainability within planetary boundaries. It outlines current economic problems and links them to key competencies critical to addressing these issues. Accordingly, educational themes and corresponding anchor topics are developed that align with the key competencies to equip students with pluralistic, transdisciplinary perspectives and solution-oriented skills for catalyzing a just economy that sustains the web of life. The framework is based on the multidimensional concept of prosperity and includes three interrelated dimensions: ecological sustainability, social inclusion and equality, and quality of life. To implement this framework, guiding principles and targeted learning outcomes for economics are defined that emphasize systems thinking, personal development, transdisciplinarity, and collective agency for transformative change. The goal is to provide a coherent structure to transform undergraduate economics education and empower students to become agents of social change who can critique and reorient the current economic system towards justice and ecological stewardship. [ABSTRACT FROM AUTHOR]

• Care practices play an important role in defining academic work-life conditions. • Critically examining care practices illuminates harmful academic structures. • Care constitutes a radical politics in and beyond academia. Academic spaces have been critically analyzed for decades now. Researchers have studied the enduring ways in which the institution is both home to, and an effective producer of, inequality and harm. They have also shown how higher education has been significantly transformed by logics of profit maximization and privatization into spaces of progressive precarity, significantly affecting academic work-life relations. In this article, we contribute to this body of work by analyzing the experience of 11 early-to-mid-career scholars who we interviewed in 2019 as part of a longitudinal study examining the work-life trajectories of 'geographers-in-the-making'. Our findings align with the main argument emerging from our ongoing research: that academic structures and exceptionalism, power and positionality, as well as care work and responsibilities all play an important role in defining academic work-life conditions and trajectories. This set of interviews emphasizes how care underlies career progression, wellbeing and health, personal relationships, and strategies for community building. We argue that care constitutes a site of radical politics in and beyond the campus, and that looking at care (and the lack of it) allows for a better understanding of harmful academic structures and the possibilities of unsettling them. [ABSTRACT FROM AUTHOR]

• An LEM model is proposed with objective of system cost reduction and customer profit maximization jointly. • An operating envelope is implemented by considering optimized power export of each prosumers. • All prosumers are segmented on their respective operating envelope into three segments i.e. value segment, mass segment and premium segment. • Based on the segmentation for those who embrace the operating envelope, an incentive for prosumers is proposed. In the future power grid, a local electricity market (LEM) with renewable energy sources and a smart grid will play a key role. Consumers, prosumers and small distributed energy resources on the other hand, play an important role in local energy transactions. In the LEM under study, the system costs are decreased and prosumers are given additional incentives for exporting the power in the grid or LEM. This paradigm is designed to boost the willingness and engagement of customers in local electricity market, so empowering them to maximize their revenues. The suggested work combines customer segmentation and operating envelope to achieve both system cost reduction & customer profit maximization objectives. The optimization challenge also takes into account the performance of the system with and without LEM. A cost comparison of the system between various cases and scenarios yields positive findings. The impact of incentives on prosumers' gross revenue is also examined. A system with 30 local participants makes up the test case system. By comparing the results of various scenarios and cases, minimum cost reduction of 0.2 % and maximum cost reduction of 25.8 % is achieved. While analyzing profit of players, incentive contributes a minimum 7 % and maximum 8 % in total revenue. [ABSTRACT FROM AUTHOR]

As a strategic lever, internal carbon pricing allows enterprises to cope with potential climate-related risks and motivate their low-carbon transformation. But internal carbon pricing, still in its early exploratory stage, lacks standard criteria for optimization. The traditional goal of cost minimization or profit maximization is not directly applicable to internal carbon pricing because internal payment for carbon emissions does not affect enterprises' aggregated costs or profits. How to optimize internal carbon prices to reduce carbon emissions more efficiently becomes an important issue. This study proposed a grouped internal carbon pricing method considering both internal carbon pricing and comprehensive efficiency. We found that there exists an optimal partition of internal units with corresponding differentiated carbon prices. Compared with other internal carbon pricing methods, the average efficiency of differentiated carbon pricing was significantly higher than the average efficiency of uniform carbon pricing. We then applied the method to a large coal producer with 16 subdivisions in China. The results showed that if the enterprise adopted the grouped internal carbon pricing under its current technology level, the carbon emissions would reach an optimal level, and its comprehensive efficiency would be significantly improved. • A corporate grouped internal carbon pricing (ICP) framework was proposed. • The ICP level of a large coal enterprise in China was measured. • The performance differences among different ICP types were studied. • Differentiated carbon pricing types lead to higher enterprise efficiency. [ABSTRACT FROM AUTHOR]