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Platform business models are upending the value proposition of the companies in traditional industries, such as healthcare, through competitively pricing value-added services and products. In this study, we consider a unique, but important, on-demand two-sided service platform: a monopolistic online healthcare platform, which not only provides medical services to patients by connecting them with doctors, but also sells medical products to patients. A crucial operational decision for the platform is to set up a pricing scheme to encourage enough doctors and patients to participate on the platform. We consider two common pricing schemes in this study: fixed and linear pricing, in an empirically-grounded optimisation model that considers medicine pricing decisions together with network externalities. Our results show that the fixed pricing scheme almost always dominates the linear pricing scheme by providing the platform with higher expected profit. Our results also reveal the strong interdependence between the optimal doctor service pricing decision and optimal medicine pricing decision. However, these decisions are often made independently by different functional units of a firm, which could cause suboptimal platform financial performance. These results indicate the importance of pricing products and services in an integrated manner for maximising the profit of a product/service platform. [ABSTRACT FROM AUTHOR]

Abstract Background Introducing more generics has been a successful strategy for lowering pharmaceutical prices and expenditure. However, the effect of the strategy depends on the pricing schemes for generics. We aimed to update the South Korean generic markets in terms of effective competition, and to examine the effects of number of manufacturers and price variance on pharmaceutical expenditure. Methods We constructed balanced panel data provided by the Health Insurance Review and Assessment Service covering 726 reimbursed substances from 2019 to 2023. We developed original indicators to analyze the generic markets: the maximum-minimum price variance (MMPV) and the maximum-weighted price variance (MWPV). Panel regression with fixed and time-fixed effects was used. Results Over the study period, the number of manufacturers increased from 17.81 in 2019 to 20.98 in 2020 and then decreased to 18.70 in 2023. The MMPV increased from 204.70 in 2019 to 230.07 in 2022 and then decreased slightly to 225.34 in 2023. The MWPV increased from 59.70 in 2019 to 72.58 in 2023. Two types of segmented markets were noteworthy: low use of low-cost generics with sufficient manufacturers and high use of low-cost generics with insufficient manufacturers. In the fixed and time-fixed effects panel analyses, the MWPV presented a negative association with the number of manufacturers and a positive association with the MMPV. Conclusions A newly introduced tiered pricing scheme, designed to differentiate generic prices, was associated with a decrease in the number of manufacturers and an increase in price dispersion. The pricing schemes for generics should be designed with price variance in mind and limit the number of too many generics in South Korea.

Background: Introducing more generics has been a successful strategy for lowering pharmaceutical prices and expenditure. However, the effect of the strategy depends on the pricing schemes for generics. We aimed to update the South Korean generic markets in terms of effective competition, and to examine the effects of number of manufacturers and price variance on pharmaceutical expenditure. Methods: We constructed balanced panel data provided by the Health Insurance Review and Assessment Service covering 726 reimbursed substances from 2019 to 2023. We developed original indicators to analyze the generic markets: the maximum-minimum price variance (MMPV) and the maximum-weighted price variance (MWPV). Panel regression with fixed and time-fixed effects was used. Results: Over the study period, the number of manufacturers increased from 17.81 in 2019 to 20.98 in 2020 and then decreased to 18.70 in 2023. The MMPV increased from 204.70 in 2019 to 230.07 in 2022 and then decreased slightly to 225.34 in 2023. The MWPV increased from 59.70 in 2019 to 72.58 in 2023. Two types of segmented markets were noteworthy: low use of low-cost generics with sufficient manufacturers and high use of low-cost generics with insufficient manufacturers. In the fixed and time-fixed effects panel analyses, the MWPV presented a negative association with the number of manufacturers and a positive association with the MMPV. Conclusions: A newly introduced tiered pricing scheme, designed to differentiate generic prices, was associated with a decrease in the number of manufacturers and an increase in price dispersion. The pricing schemes for generics should be designed with price variance in mind and limit the number of too many generics in South Korea. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are emerging as one of the pillars for achieving climate neutrality. They represent both a threat and an opportunity for the operation of the network. Used as flexible loads, they can favor the self-consumption of photovoltaic (PV) energy. This paper presents three EV charging tariff systems (TSs) based on the self-consumption of excess PV energy. The TS objectives are to increase the self-consumption rate (SCR) and thus indirectly decrease the charging cost of the EV users. Two of the proposed TSs correspond to an indirect control of EV charging. The third TS is a hybrid system where the charging power is controlled. The TS is designed using a series of rules that consider the momentary PV surplus and the charging power of each EV. The influence of the TS is simulated by considering real data from a PV collective self-consumption project in the Basque Country (Spain). The TS simulations performed with 6 months of data show a 13.1% increase in the SCR when applying the third TS, reaching an average of 93.09% for the SCR. In addition, the cost of EV charging is reduced by 25%. [ABSTRACT FROM AUTHOR]

Due to concerns with air pollution and climate change, governments and transport operators around the world have engaged in transforming their fossil-fueled vehicles into electric vehicles (EVs). It is essential to build a model for the electrifying process to minimize the operation costs. This paper presents a systematic analytical approach for the electrification of a fire ambulance service station. This approach begins with the selection of suitable EVs to replace the current service vehicles. Subsequently, an in-depth analysis is conducted to determine the practical utilization of EVs at the station. The model proposes two charging strategies: immediate charging upon an EVs' return and smart charging. Based on the chosen EVs and charging strategies, a comprehensive assessment of the load profiles for the planned EV charging station is performed. In accordance with the load profiles, a mathematical model to minimize the infrastructure and operation costs of the charging station is proposed. Various pricing schemes are compared to identify the most efficient pricing scheme for the charging station, and economic analyses of the EVs and traditional ambulance vehicles are proposed in this paper. The test results indicate that the progressive pricing scheme is well suited for immediate charging strategies, whereas smart charging should opt for the time-of-use pricing scheme. Selecting the appropriate pricing scheme has the potential to significantly reduce electric energy costs. [ABSTRACT FROM AUTHOR]

Abstract Cloud computing is an evolving paradigm whose adoption has been increasing over the last few years. This fact has led to the growth of the cloud computing market, together with fierce competition for the leading market share, with an increase in the number of cloud service providers. Novel techniques are continuously being proposed to increase the cloud service provider’s profitability. However, only those techniques that are proven not to hinder the service agreements are considered for production clouds. Analysing the expected behaviour and performance of the cloud infrastructure is challenging, as the repeatability and reproducibility of experiments on these systems are made difficult by the large number of users concurrently accessing the infrastructure. To this, must be added the complications of using different provisioning policies, managing several workloads, and applying different resource configurations. Therefore, in order to alleviate these issues, we present Simcan2Cloud, a discrete-event-based simulator for modelling and simulating cloud computing environments. Simcan2Cloud focuses on modelling and simulating the behaviour of the cloud provider with a high level of detail, where both the cloud infrastructure and the interactions of the users with the cloud are integrated in the simulated scenarios. For this purpose, Simcan2Cloud supports different resource allocation policies, service level agreements (SLAs), and an intuitive and complete API for including new management policies. Finally, a thorough experimental study to measure the suitability and applicability of Simcan2Cloud, using both real-world traces and synthetic workloads, is presented.

Cloud computing is an evolving paradigm whose adoption has been increasing over the last few years. This fact has led to the growth of the cloud computing market, together with fierce competition for the leading market share, with an increase in the number of cloud service providers. Novel techniques are continuously being proposed to increase the cloud service provider's profitability. However, only those techniques that are proven not to hinder the service agreements are considered for production clouds. Analysing the expected behaviour and performance of the cloud infrastructure is challenging, as the repeatability and reproducibility of experiments on these systems are made difficult by the large number of users concurrently accessing the infrastructure. To this, must be added the complications of using different provisioning policies, managing several workloads, and applying different resource configurations. Therefore, in order to alleviate these issues, we present Simcan2Cloud, a discrete-event-based simulator for modelling and simulating cloud computing environments. Simcan2Cloud focuses on modelling and simulating the behaviour of the cloud provider with a high level of detail, where both the cloud infrastructure and the interactions of the users with the cloud are integrated in the simulated scenarios. For this purpose, Simcan2Cloud supports different resource allocation policies, service level agreements (SLAs), and an intuitive and complete API for including new management policies. Finally, a thorough experimental study to measure the suitability and applicability of Simcan2Cloud, using both real-world traces and synthetic workloads, is presented. [ABSTRACT FROM AUTHOR]

Renewable energy sources (RES) can provide valuable flexibility potential for multiple markets and grid services in the future. In this paper, the focus lies on the development of algorithms for an optimal dispatch and bidding of a RES-based virtual power plant (VPP) by considering current short-term and balancing market conditions in Austria, as well as a proposed redispatch market. Specifically, different pricing and bidding strategies for a redispatch market are compared and their feasibility is analyzed. The attractiveness of different pricing models and remuneration mechanisms for redispatch, as well as the influence of the redispatch call probability on the bidding behavior of the VPP, is investigated. The simulation of the bidding behavior is carried out using linear optimization techniques. The paper describes the algorithms as well as the assumptions for market rules in redispatch, balancing and short-term electricity markets. The results show that the probability to be activated for redispatch and thus the required incentives depend highly on the location of the plant. Pre-curtailment of the plant to offer positive redispatch is not recommended as the prices would need to be set at up to 200–300% of the day-ahead price. [ABSTRACT FROM AUTHOR]

Problem definition: We study a two-stage supply chain, where the supplier procures a key component to manufacture a product and the buyer orders from the supplier to meet a price-sensitive demand. As the input price is volatile, the two parties enter into either a standard contract, where the buyer orders just before the supplier starts production, or a time-flexible contract, where the buyer can lock a wholesale price in advance. Moreover, we consider three selling-price schemes: Market Driven, Cost Plus, and Profit Max. Academic/practical relevance: This problem is motivated by real practices in the cloud industry. Our model and optimization approach can address similar problems in other industries as well. Methodology: We assume that the input price follows a geometric Brownian motion. To determine the optimal ordering time, we propose an optimization approach that is different from the classic approach by Dixit et al. (1994) and Li and Kouvelis (1999). Our approach leads to deeper analytical results and more transparent ordering policy. Through a numerical experimentation, we compare profitability of different parties under different contracts, pricing schemes, and market conditions.Results: The buyer's ordering policy is determined by a threshold policy based on the current time and input price; the optimal threshold depends on not only the drift and volatility of the input price but also, their relative magnitude. The supplier's optimal procurement time should be determined by analyzing a trade-off between the holding cost of storing the components and the future input-price movement. Managerial implications: Under the Profit-Max and the Cost-Plus pricing schemes, the time-flexible contract is a Pareto improvement compared with the standard contract, whereas under the Market-Driven pricing scheme, the supplier may be better off under the standard contract. Moreover, although the most favorable scenario for the buyer is under the Profit-Max pricing scheme, the most favorable scenario for the supplier oftentimes is under the Cost-Plus pricing scheme. Furthermore, this study provides valuable insights into impacts of various characteristics of the component market, such as the trend and volatility of the input price, on the expected profit of the supply chain and its split between the two parties. [ABSTRACT FROM AUTHOR]

On-demand service platforms (ODSPs), which act as mediators between customers and independent service providers, commonly adopt pay-per-service and membership pricing schemes. The pay-per-service scheme charges customers a fee for each service completion. In contrast, the membership scheme charges customers a periodic fee and allows them to make unlimited service requests for a certain period without any extra payment. In this paper, we develop a model framework to explore the optimal pricing strategy for an ODSP with heterogeneous customer demand frequencies, which turns out to be critically affected by the discrepancy between the patience of the ODSP and users (customers and service providers) regarding cash flow. Specifically, the ODSP is expected to adopt the membership scheme only when users are more patient than the ODSP, and the more patient users are, the more the ODSP prefers the membership scheme. In the extreme case when users are patient enough, the ODSP should forego the pay-per-service scheme altogether. Moreover, we investigate a more flexible pricing scheme, the two-part tariff scheme, that combines the per-service and membership fees. Interestingly, this scheme cannot generate more profit for the ODSP than the pay-per-service or the membership schemes, so it is not a wise strategy for the ODSP. Finally, the impacts of cross- and within-group network effects on the ODSP's decisions are identified. Our analysis reveals that when the cross-group (resp., within-group) network effect is strengthened, the ODSP should raise (resp., reduce) the service fees and wage paid to providers. • ODSPs should adopt the membership scheme only when users are more patient. • The membership scheme is more profitable to ODSPs as users become more patient. • Adopting the two-part tariff scheme is not a wise strategy for ODSPs. • Stronger cross/within-group network effect, higher/lower the service fees and wage. [ABSTRACT FROM AUTHOR]

Energy efficiency is of paramount importance for the sustainability of high performance computing (HPC) systems. Energy consumption limits the peak performance of supercomputers and accounts for a large share of Total Cost of Ownership (TCO). Consequently, system owners and final users have started exploring mechanisms to trade off performance for power consumption, for example, through frequency and voltage scaling. However, only a limited number of studies have been devoted to explore the economic viability of performance scaling solutions and to devise pricing mechanisms fostering a more energy-conscious usage of resources, without adversely impacting return-of-investment on the HPC facility. How to foster green-computing to take advantage of power management knobs? Would today's accounting systems be sufficient or are novel pricing schemes needed? How to model the link between TCO and power management? To answer these questions, we present a parametrized model to analyze the impact of frequency scaling on energy consumption and to assess the potential cost benefits for the HPC facility and users. We evaluate four pricing schemes, considering both facility manager and the user perspectives. We then perform a design space exploration considering current and near-future HPC systems and technologies. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the economic benefits of an energy community investing in small-scale photovoltaics (PVs) when local energy trading is operated amongst the community members. The motivation stems from the open research question on whether a community-operated local energy market can enhance the investment feasibility of behind-the-meter small-scale PVs installed by energy community members. Firstly, a review of the models, mechanisms and concepts required for framing the relevant concepts is conducted, while a clarification of nuances at important terms is attempted. Next, a tool for the investigation of the economic benefits of operating a local energy market in the context of an energy community is developed. We design the local energy market using state-of-the-art formulations, modified according to the requirements of the case study. The model is applied to an energy community that is currently under formation in a Greek municipality. From the various simulations that were conducted, a series of generalizable conclusions are extracted.

Cloud computing has been a rising trend in the business world. In this study, we consider two most important pricing schemes offered to sustained customers by major service providers in the cloud industry: the reservation‐based scheme (the R‐scheme) by Amazon or Microsoft, and the utilization‐based scheme (the U‐scheme) by Google. We consider a duopoly model with heterogeneous customers characterized by the mean and the coefficient of variation of their usage. We show that under either pricing scheme, the effective price is essentially an increasing function of the coefficient of variation of usage, and thus both schemes aim for rewarding stability in usage. However, when the providers adopt different schemes, we show that customers with lower demand volatility would prefer the R‐scheme, while those with higher demand volatility would prefer the U‐scheme. Furthermore, we study the impact of evolving market characteristics, including the distributions of market preference, demand size, and demand volatility, as well as the impact of the providers' service levels on their choices of schemes and decisions on the pricing parameters. We find that if the market has a stronger preference for a particular provider or that provider has a higher service level than its competitor, the provider is more likely to adopt the R‐scheme, while its competitor's adoption of a scheme depends on the extent of the price competition. Specifically, when the diversity of customer preference becomes higher (lower), the price competition becomes softened (intensified), and the competitor is more likely to adopt the R‐scheme (U‐scheme, respectively). [ABSTRACT FROM AUTHOR]

This paper presents a real-time greedy-index dispatching policy (GIDP) for using plug-in electric vehicles (PEVs) to provide frequency regulation services. A new service cost allocation mechanism is proposed to award PEVs based on the amount of service they provided, while considering compensations for delayed-charging and reduction of battery lifetime due to participation of the service. The GIDP transforms the optimal dispatch problem from a high-dimensional space into an 1-D space while preserving the solution optimality. When solving the transformed problem in real-time, the global optimality of the GIDP solution can be guaranteed by mathematically proved “indexability.” Because the GIDP index can be calculated upon the PEV’s arrival and used for the entire decision making process till its departure, the computational burden is minimized and the complexity of the aggregator dispatch process is significantly reduced. Simulation results are used to evaluate the proposed GIDP, and to demonstrate the potential profitability from providing frequency regulation service by using PEVs. [ABSTRACT FROM AUTHOR]

Residential water demand management using price and non-price measures to conserve water has gained considerable international attention from water utilities over the last few decades. The objective of this paper is to explore the effectiveness of different pricing schemes on water conservation. In this study, we compare the ‘conservation-orientedness’ of two pricing schemes. These are a uniform pricing scheme and an increasing block tariff scheme (IBT) structure. A quasi-experimental method is used for this purpose involving 150 suburbs in the Brisbane City Council (BCC) in Queensland, Australia for a 4-year period between 2005 and 2008. Our results show there are more conservation benefits associated with an IBT pricing scheme than a uniform pricing scheme. [ABSTRACT FROM AUTHOR]

As the electricity system shifts from a conventional operating system into a smart grid system, the paper proposes an efficiency-enhancing mechanism comprised of communication-based demand response (CBDR) and a customer-friendly inclining block tariff (IBT) that takes into consideration customers’ income and consumption profiles. [ABSTRACT FROM AUTHOR]

Energy management of residential consumers plays a very important role in the achievement of aim of energy saving, lesser cost of electricity for the residential consumers and highest possible satisfaction of the consumers. In this paper, a common energy scheduler for the multiple users has been designed for energy scheduling of residential consumers having smart appliances. Mathematical modelling of different types of appliances has been done to achieve the required results. Time of use tariff and day ahead real time pricing tariff have been used for energy scheduling. A wind energy source has been considered to reduce the burden of energy demand on the grids and to analyse the effect of renewable energy sources in a small scale energy management problem. A delay factor has been also included in the main objective function of the problem to maintain the satisfaction level of the consumers by reducing cost of waiting. The final designed problem is solved by mixed integer linear programming technique using GAMS software. The time taken for the solution is very less. In this paper, to increase the energy efficiency and the accuracy of the required results, the selected time horizon of 24 h is divided into 96 small time slots, each of 15 min duration. Achievement of the aim of the paper, that is reduction in the cost of electricity and attainment of the highest possible satisfaction level of the users, has been verified by the simulation results.Practical application: Our work is completely applicable in the practical field. This work can be used for the energy scheduling of home appliances. [ABSTRACT FROM AUTHOR]

With the advancement of smart grid technology, the consumers get the opportunity to participate in various demand response (DR) programs. They can reduce their electricity bill by participating in DR programs. Along with the consumers, the power utility companies also get benefits due to the reduction in high energy peaks on the demand curve. In this paper, we propose an energy scheduling model for the scheduling of smart appliances at home. For the scheduling of appliances, two different dynamic pricing schemes are selected, i) time of use scheme, ii) real time pricing scheme. Along with this, a small renewable energy source in form of rooftop photovoltaic panels is also included to analyse its effect on energy scheduling solution. Finally, the scheduling problem is solved by mixed integer linear programming (MILP) technique. The CPLEX solver of GAMS software is used to apply MILP technique. A case study by considering different cases is done to analyse the effectiveness of formulated model and selected solution approach for the scheduling of the appliances. The simulation results by considering both the pricing schemes have been achieved and compared to get the better idea of the pricing schemes on the energy scheduling results. [ABSTRACT FROM AUTHOR]

A well-known mechanism for demand response is sending price signals to customers a day ahead. Customers then postpone or advance their usage of electricity to minimize cost. Setting up price functions that adapt the customers’ load to availability is a big challenge. This paper investigates the feasibility of finding day-ahead price functions to induce a desired load profile of domestic electric water heaters (DEWHs) minimizing their electricity cost for demand response. Bilevel optimization is applied for a single DEWH using a simplified linear model and full knowledge. This leads to a solvable bilevel problem and allows understanding optimality of price functions and resulting heating profiles. It is shown that with the resulting price functions the DEWH may select many significantly different heating profiles leading to the same cost. Thus the price does not uniquely induce the desired heating profile. The acquired knowledge forms the basis for a procedure to create price functions for controlling the load profile of many DEWHs. [ABSTRACT FROM AUTHOR]

In this paper we analyze the internet pricing schemes based on bandwidth function diminished with increasing bandwidth utility function with 3 pricing strategies for homogeneous and heterogeneous consumer. The new proposed pricing schemes with this utility function will give the information to the internet service providers (ISP) in maximizing profits and provide better service quality for users. The Models on every type of consumer are applied to the data traffic in Palembang local server. LINGO 11.0 is used to compute the nonlinear programming problem to get the optimal solution. The results showed that for each case based on 3-pricing scheme, ISPs get better profit by choosing all three schemes in consumers type of homogenous case while for heterogeneous cases on willingness to pay and based on demand of the consumers, ISPs can select flat fee scheme to gain higher profit rather than those two other schemes. [ABSTRACT FROM AUTHOR]

The paper explores how agricultural water pricing could contribute to lowering water demand when uses are unobserved (asymmetric information). The topic of the paper is justified by the fact that most water authorities worldwide do not control water uses at the farm scale. The study draws inspiration from the pricing policies of a Reclamation and Irrigation Board in Northern Italy. It analyses the optimal design of current tariff strategies with respect both to the actual regulator's goals and the cost recovery objective of an ideal regulator driven by European Water Framework Directive principles and having full information. The analysis is based on the logic of a Principal-Agent model implemented as a mathematical non-linear programming model. Given the current pricing structure and assuming zero transaction costs, the results show a relevant increase in net benefits for the ideal scenario with respect to the actual one as water use costs increase. Benefits differences between the two scenarios mark a limit in value below which mechanisms able to solve the existing asymmetries between the principal and the agents are economically desirable. The study concludes by showing that the current regulator's discriminatory strategy (pricing structure) would be better justified with higher levels of cost for water use. However, the existence of non-zero transaction costs related to the control of water uses points to the need for further research in order to analyze incentive mechanisms in the absence of water metering. [ABSTRACT FROM AUTHOR]

During the last decade telecommunications market has become a very competitive environment, with a great many vendors, carriers, and services provided. The Intermediaries are one of the main players in the telecom network market, and they may acquire network capacity from a set of telecommunication Backbone Providers that offer different pricing and quality of service schemes and sell purchased capacity to end-users in order to make a profit. Unfortunately, the bandwidth demands of customers are not known in advance. Therefore, Intermediaries may end up having either slack capacity or unsatisfied customer demands. In this paper, to overcome these problems, a two-stage stochastic integer programming approach is proposed. The performance of suggested method is validated on several randomly generated scenarios. In addition, the effects of different bandwidth demand distributions are examined by using well-known measures such as Expected Value of Perfect Information (EVPI) and Value of Stochastic Solution (VSS). The robustness of methodology is evaluated by carrying out sensitivity analysis on different problem parameters. [ABSTRACT FROM AUTHOR]

Abstract: This paper describes a participatory framework used in Alta Verapaz, Guatemala to analyze the future viability of different conservation measures in a cloud forest area. It presents how in the case of conservation initiatives, participatory developed instruments could provide the incentive for farmers to reinforce or introduce conservation measures in their farming systems to preserve habitats, reduce species disruptions and alleviate poverty. A model''s architecture was created with stakeholders of different settings of the region of study. Together, baseline-scenarios and policy-scenarios on land use and income generation were developed. The paper investigates the potential impacts of different policy measures for conservation and poverty reduction through the instrument “payments for environmental services”. Aim was to integrate social, economic and environmental aspects. In the paper, (1) farming systems’ characteristics are described; (2) conservation activities are defined and standardized; and (3) policy recommendations out of different scenarios are given. The paper discusses the possibilities of compensating farmers for the affecting costs of already existing conservation activities by introducing a participatory developed policy instrument that captures the needs of the different farming systems. I suggest that acceptance or rejection of these payments is related to the farm-level strategies for coping with risk. I presuppose that if farmers see an advantage in producing environmental services because it signified an increase in their income and a decrease of risk, they will adopt and augment conservation techniques in their farming systems (). Main findings show that the amount of on-farm production is very significant for the classification of farming systems. Further, scenario for land use and income changes were seen as main drivers for conservation. The paper concludes showing that including in the modeling the economic disparities of the different farming systems and the active participation of stakeholders supports the acceptance and implementation of this policy instrument to promote agricultural landscapes with biodiversity and watershed conservation. [Copyright &y& Elsevier]

Information goods vendors offer different pricing schemes such as per user pricing and site licensing. Why do competing sellers adopt different pricing schemes for the same information good? Pricing schemes affect buyers' usage levels and thus the revenue generated from different segments of buyers. This can allow competing firms in a duopoly to differentiate themselves by offering different pricing schemes. Such strategic use of pricing schemes can allow undifferentiated sellers to earn substantial profits in a friction-free market for a commoditized information good. These conditions would otherwise lead to the Bertrand equilibrium and zero profits. We show that adopting asymmetric pricing schemes can be a Nash equilibrium for information goods with negligible marginal cost of production. We extend our model to the case of information goods that are horizontally differentiated and show that sellers will offer a single-pricing scheme that is different from competitors when the sellers are weakly differentiated. When the sellers are strongly differentiated, each seller will offer multiple pricing schemes. We show that it can be optimal for a seller to offer multiple pricing schemes—metered and flat fee pricing schemes, even in the absence of transactions costs. [ABSTRACT FROM AUTHOR]

This article analyzes the location equilibrium for firms under monopolistic competition in a linear market à la Hotelling. The central agglomeration of firms is uniquely achieved with sufficiently low transport costs under three pricing schemes: discriminatory, mill and uniform-delivered pricing. [ABSTRACT FROM AUTHOR]

We discuss how a new pricing scheme can be integrated within a communication network. The pricing scheme is based on the availability of end-to-end communications, and is an alternative to congestion pricing, which is not applicable when communication capacity is higher than demand (as happens in most communication backbone networks). We also investigate how, based on this scheme, an optimization algorithm for updating the network topology can be applied. The network update problem is modeled as a combinatorial optimization problem, which is approximately solved using a Genetic Algorithm. The good results obtained in a case study show that the method is robust and can be applied even when end-to-end availability measures can only be computed approximately (in this case, using a Monte Carlo method). [ABSTRACT FROM AUTHOR]

Abstract: The build-operate-transfer (BOT) approach is one of the privatization mechanisms for promoting transportation infrastructure developments by using private funds to construct new infrastructure facilities. In a BOT scheme, it often involves three parties: the government, whose objective is to maximize the benefit defined in terms of social welfare added to the society; the private investors, whose objective is to maximize the profit generated from the investment; and the road users, whose objective is to minimize the inequality of benefit distribution among the road users traveling from different origin–destination pairs. Each of these parties has different objectives that often conflict with each other. In this paper, we develop various optimal road pricing models under demand uncertainty for analyzing the tradeoffs among the three objectives. In addition, a project evaluation framework is developed for assessing the effects of government policy and regulation on the BOT project. Seven cases of the BOT road pricing problem are analyzed: (1) BOT without regulation, (2) BOT with price control regulation, (3) BOT with equity regulation, (4) BOT with construction cost subsidy, (5) BOT with concession period extension, (6) BOT with construction cost subsidy and concession period extension, and (7) BOT with multiple objectives. Numerical results using a real case study of the Ban Pong–Kanchananburi Motorway (BMK) in Thailand are provided to examine the above seven cases. [Copyright &y& Elsevier]

ABSTRACT Two producers delegate sales of differentiated products to common retailers, each with a monopoly position. Each producer can offer either a linear or a two-part tariff. In the single-period game each producer's dominant strategy is to use a two-part tariff. If the two producers' products are sufficiently close substitutes and the discount factor is sufficiently high, both producers offering linear tariffs can be sustained as an equilibrium outcome in an infinitely repeated game. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the economic benefits of an energy community investing in small-scale photovoltaics (PVs) when local energy trading is operated amongst the community members. The motivation stems from the open research question on whether a community-operated local energy market can enhance the investment feasibility of behind-the-meter small-scale PVs installed by energy community members. Firstly, a review of the models, mechanisms and concepts required for framing the relevant concepts is conducted, while a clarification of nuances at important terms is attempted. Next, a tool for the investigation of the economic benefits of operating a local energy market in the context of an energy community is developed. We design the local energy market using state-of-the-art formulations, modified according to the requirements of the case study. The model is applied to an energy community that is currently under formation in a Greek municipality. From the various simulations that were conducted, a series of generalizable conclusions are extracted. [ABSTRACT FROM AUTHOR]