Social Welfare Maximization Research Articles

Space Booking with Multistage Online VCG Mechanism: A Simulation Study Toward Practical Application

Most current reservation systems include only time slots as reservation information. Customers need only to make a reservation by choosing a proper time. The value generated by a reservation is unknown until the customers are served. Therefore, an important shortcoming of current reservation systems is that they require limited information from customers, engendering a loss of social welfare. We surmise that an innovative reservation system including space value can produce more desired outcomes by considering various factors, such as social distancing during a pandemic. Based on that supposition, we adopt the Vickrey–Clarke–Groves (VCG) mechanism to achieve social welfare maximization and strategy-proofness. To offset VCG mechanism shortcomings in practical applications, we converted the VCG mechanism to a multistage online VCG mechanism that can do timely processing of customers’ reservations and can thereby maximize social welfare. The multistage online VCG mechanism generates higher social welfare than most current reservation systems, which use a first-come, first-served mechanism. Additionally, we demonstrate that, by adjusting the VCG mechanism bid number to a practical level, the reservation process can be improved considerably with a probability of more than 99% for satisfying the strategy-proofness of the VCG mechanism. Funding: This work was supported by JSPS KAKENHI [Grant 22H01710] and the Mohammed bin Salman Center for Future Science and Technology for Saudi–Japan Vision 2030 at the University of Tokyo [Grant MbSC2030].

Real-time pricing based on convex hull method for smart grid with multiple generating units

With the growing demand and complex supply structure for electricity, higher requirements are proposed for generating units to regulate the peak load, resulting in significant increases in the startup cost. These issues bring urgent need to develop new pricing mechanisms. The real-time pricing (RTP) is an ideal pricing mechanism for clipping peak load and balancing supply and demand. This paper designs an RTP mechanism for the smart grid which integrates multiple generating units on the supply side and distributed renewable energy generation devices and energy storage systems on the demand side. Focusing on the interests of both supply and demand sides, a social welfare maximization model that incorporates startup costs of generating units is formulated. To tackle the discontinuity in the proposed model, the convex hull method is utilized to transform the primal model and derive the convex hull price which is widely regarded as an optimal price that can reflect the aggregate cost of generating units but has rarely extended to RTP. The related dual theory indicates that the convex hull price can be obtained by the Lagrange multiplier of the proposed social welfare maximization problem associated with the supply–demand balance constraint. Moreover, a distributed iterative algorithm based on the subgradient projection method is employed to solve the dual problem. Simulation results validate rationality and effectiveness of the proposed pricing mechanism.

Social welfare maximization in deregulated power market incorporating wind power plants using metaheuristic algorithm

The increasing integration of renewable energy sources (RESs), particularly wind power plants (WPP), into deregulated power markets introduces complexities in optimizing social welfare (SW). This article proposes a recent metaheuristic algorithm to address this challenge and maximize SW while accounting for the presence of WPP and the inherent uncertainty associated with wind power forecasting. The proposed algorithm optimizes generation scheduling and demand-side bidding strategies in the deregulated power market to maximize SW while ensuring economic efficiency. To validate the effectiveness and robustness of the proposed algorithm, MATLAB simulations are conducted on IEEE 30 and IEEE 118-bus systems. The results demonstrate that the proposed algorithm provides promising solutions for maximizing SW, especially in the context of incorporating WPP. This research contributes to the advancement of power market optimization methods and promotes the seamless integration of RESs, fostering a more sustainable energy future.

A reinforcement-probability Bayesian approach for strategic bidding and market clearing for renewable energy sources with uncertainty

With the participation of renewable energy sources (RESs) in the electricity market, the inherent uncertainty brings new challenges for strategic bidding and market clearing in the electricity market. In this paper, the market structure is formulated as a non-cooperative game among power suppliers, consumers, and the market operator. A reinforcement-probability Bayesian approach is proposed to obtain the optimal bidding strategy of RESs and achieve maximal social welfare at the market clearing, considering uncertain cost types of RESs and incomplete market information. First, a reinforcement-probability model is presented to obtain the optimal agent-type-strategy combination considering the uncertainty of cost type selection. A agent-type-probability algorithm is proposed for the model to update the mean of probability density function of cost types according to the expected profit. Second, an incremental-cost-based distributed algorithm is proposed to solve the market clearing problem, which accelerates the convergence rate without violating the system constraints via feedback from the scheduled power. Finally, a Bayesian probability model is proposed for RESs to learn the hidden information of the market and transform the incomplete information electricity market into a complete one. A Bayesian Nash equilibrium is reached in the model. To coordinate the information among market participants, the output of the Bayesian probability model is calculated by the Hysteretic Q-learning algorithm and used as the reward to update the agent-type-probability in the reinforcement-probability model. An illustrative example is presented to demonstrate the effectiveness of the proposed approach, which shows that the proposed approach has a better convergence performance and has a 2–6 times faster learning speed compared with the policy gradient and deep Q-network.

DPS: Dynamic Pricing and Scheduling for Distributed Machine Learning Jobs in Edge-Cloud Networks

5G and Internet of Things stimulate smart applications of edge computing, such as autonomous driving and smart city. As edge computing power increases, more and more machine learning (ML) jobs will be trained in the edge-cloud network, adopting the parameter server (PS) architecture. Due to the distinct features of the edge (low-latency and the scarcity of resources), the cloud (high delay and rich computing capacity) and ML jobs (frequent communication between workers and PSs and unfixed runtime), existing cloud job pricing and scheduling algorithms are not applicable. Therefore, how to price, deploy and schedule ML jobs in the edge-cloud network becomes a challenging problem. To solve it, we propose an auction-based online framework DPS. DPS consists of three major parts: job admission control, price function design and scheduling orchestrator. DPS dynamically prices workers and PSs based on historical job information and real-time system status, and decides whether to accept the job according to the deployment cost. DPS then deploys and schedules accepted ML jobs to pursue the maximum social welfare. Through theoretical analysis, we prove that DPS can achieve a good competition ratio and truthfulness in polynomial time. Large-scale simulations and testbed experiments show that DPS can improve social welfare by at least <inline-formula><tex-math notation="LaTeX">$95\%$</tex-math></inline-formula>, compared with benchmark algorithms in today&#x0027;s cloud system.

Generic technology R&D strategies in dual competing photovoltaic supply chains: A social welfare maximization perspective

In the context of global warming, photovoltaic (PV) industry is growing rapidly to advance carbon neutrality. A new generation PV technology based on perovskite solar cells has attracted high attention from the industry due to its advantage of low cost and high photoelectric conversion efficiency, and has gradually carried out relevant technology research and development (R&D), as well as small and medium-sized product trials. However, there are still some generic technical issues with perovskite solar cells, such as the stability of large area preparation, efficiency over large areas, and long service life under high efficiency. Collaborative R&D around generic technologies among module suppliers across PV supply chains will help create complementary synergies and accelerate breakthroughs in generic technologies. To explore R&D strategies and subsidy policies for the generic technology in competing PV supply chains, twelve game-theoretical decision models considering cooperative/non-cooperative R&D with/without government subsidy under four scenarios are developed, analyzed and compared respectively. The research results indicate that: (i) in all four scenarios, the adoption of a centralized operational strategy is more effective in terms of government subsidies and the operational performance of the PV supply chains than a hybrid or decentralized operational strategy. (ii) the government should adopt an appropriate subsidy intensity for the dual-competing PV supply chains and set a “threshold” subsidy policy. (iii) the positive externality of R&D effort has a positive spillover effect on profits, social welfare and other indicators. The positive externality of R&D effort for the generic technology can be better exploited by the two PV supply chains jointly deciding on the level of R&D expenditure and by the government providing financial subsidy support. This study is a pioneering study of dual competing PV supply chains in terms of cooperative R&D of generic technology, which is very limited in the PV supply chain management and subsidy policy research literature. The findings and some unnoticed issues in this paper provide a basis for future PV supply chain research.

The three-sided market of on-demand delivery

On-demand delivery services are three-sided markets that enable interactions between customers and suppliers with the help of crowdsourced drivers. Customers and suppliers may pay a commission to gain access to the service and drivers are granted a wage for providing their delivery service. This study characterizes the properties of three-side on-demand delivery markets, and proposes pricing strategies that enable the platform to manipulate the market towards profit or social welfare maximizing outcomes. We consider earning-sensitive independent drivers, price-and-time-sensitive customers, and price-sensitive suppliers. By assuming that all players are heterogeneous in their valuation of the service, we model their numbers as endogenously dependent on the price, wage, and commission of the platform. We also use the continuum approximation of the vehicle routing problem to derive customer waiting times. Our analyses show that suppliers internalize a portion of the driver wage, and the customers internalize the supplier commission. However, the platform requests lower commissions and offers a higher wage in social welfare maximization compared to profit maximization, which yields a negative profit for the platform in social welfare maximization. Moreover, we validate the results of the analytical model with numerical experiments on more realistic cases.

DETERMINATION OF INDIVIDUALS’ FREEDOM RANGE RELYING ON THE COMPARATIVE STUDY OF HARM PRINCIPLE IN WESTERN AND ISLAMIC LEGAL SYSTEMS

One of the most respected principles of human societies is the freedom of individuals’ will in fulfilling their desired interests. Therefore, not only individuals but also public authorities cannot prevent the freedom of individuals from benefiting social and economic interests under any pretext. However, in some cases, the freedom of individuals in obtaining the greatest benefit, conflicts with each other, in such a way that the absolute freedom of someone to obtain more benefit, in some cases, makes harm the others. Therefore, it is very important to determine the limits and loopholes of individuals freedom and to establish a balance between it and the harm that may be caused to others due to this freedom. as this determination can have a wide impact on the comprehensive development of a state and the people living on it, Due to this necessity, many schools of thought throughout the history have commented on this issue including Islamic and Western schools of thought. The philosophical thoughts of these two schools, one of which is included in the Islamic rule of "no harm" and the other in the Western rule of "harm," has led this article to investigate the points of commonality and differences between these two rules in the shared interaction between them. This paper finally reached the conclusion that from the perspective of legal science, the freedom of individuals in fulfilling their interests is of special importance to ensure the maximum social welfare of any society and its people. So governments, not only should not hinder people's activities, but also In their management, should first seek to combine the conflicted interests of individuals with each other and if it is not possible, in the second step, they should give priority to fulfilling the interests that have the most public function for the most people in the society.

Ride-sourcing market equilibrium analysis and optimization under immediate matching strategies

Supply/demand characteristics and price strategies in the ride-sourcing market are affected by matching strategies. In particular, immediate matching strategies are widely adopted to tackle supply–demand imbalance issues. This paper studies a two-sided ride-sourcing market under two immediate matching strategies: driver-first-come-first-serve and passenger-first-come-first-serve. In the former, the system is considered over-saturated with passengers, such that any driver is matched immediately upon posting their availability. Therefore, the market would be devoid of waiting drivers. In comparison, the latter strategy is oversaturated with drivers, such that any newly arriving passenger is immediately matched, and there are no waiting passengers in the system. We first derive generalized supply and demand functions and prove that the immediate matching strategies are optimal to maximize the number of completed trips under certain conditions based on equilibrium analysis. Then, we characterize the market equilibrium and discuss the properties of the wild-goose-chase phenomenon under two immediate matching strategies. We optimize the ride-sourcing platform's strategies to achieve a joint profit of platform and driver maximization (monopoly optimum), social welfare maximization (social optimum), and second-best optimum (entailing a reservation profit for the platform). Numerical experiments are conducted to demonstrate the effects of user heterogeneity, and the properties of pricing strategies under both matching strategies on the monopolistic and social optimal solution. The gained managerial insights provide suggestions for decision-makers to coordinate supply and demand in the ride-sourcing market.

Performance-based transit subsidy allocation scheme to fulfill multiple policy criteria

ABSTRACT Performance-based subsidy allocation schemes are typically intended to satisfy a single policy goal (e.g. social welfare maximization) and often do not include objective approaches to quantifying bus service efficiency. This paper proposes a framework for network data envelopment analysis and introduces an innovative transit subsidy allocation scheme with a fixed budget that fulfills multiple policy criteria. The proposed basic scheme minimizes the maximum deviation from efficient allocation in proportion to the operating scales across all routes in a two-stage production system. Multiple policy criteria are then added to the basic scheme as additional goal components. The proposed scheme is evaluated by using an empirical dataset of 344 subsidized intercity bus routes collected from 20 Taiwanese bus operators. The results demonstrate that the subsidies allocated on the basis of the proposed scheme can satisfy the policy criteria incorporated into the scheme. The proposed modeling framework is flexible, and therefore, local governments can easily incorporate policy criteria into the scheme according to their own interests and needs.

Market‐clearing framework of a resilient microgrid with renewable energy considering emission reduction targets

AbstractWith the gradual attention of global climate change, a series of carbon emission plans have been formally implemented in the field of energy. There is a consistent relationship between the carbon market and the electricity market in promoting clean energy development and improving carbon dioxide emission reduction targets. The market design of a low‐carbon power system should be considered as a whole to promote the coordinated development of carbon and power markets. Taking an industrial park in China as the research object, an internal market trading model of an industrial park considering carbon dioxide emission reduction targets is proposed. The proposed trading mechanism considers both carbon dioxide emission reduction targets and social welfare maximization. The proposed market trading model connects day‐ahead and intraday electricity markets, which deal with uncertainty about renewable energy sources.

Combinatorial Auction of Used Cars Considering Pro-Environment Attribute: A Social Welfare Perspective

Air pollution is becoming more and more serious as the number of vehicles increases. To address such problems, many cities have implemented many measures, including the circular economy mode, in which used cars with low carbon emission are becoming important in the sustainable transportation and carbon abatement. Considering multi-attribute demand, this study designed combinatorial auction mechanism for the bidders of automobile enterprises on an online used-car platform to achieve social welfare maximization. Two kinds of attributes were considered, namely, price attribute and non-price attributes; the latter particularly included the pro-environment attribute based on an analysis of complementarity and substitutability. Moreover, the mechanism was proved to satisfy individual rational condition and incentive compatibility condition. Numerical application showed that preference for the pro-environment attribute can better realize social welfare and respond to national energy conservation and emission reduction targets. As a result, from the social welfare perspective, the multi-attribute combinatorial auction can provide a reference for more fair and effective allocation of used cars to bidders and can promote both buyer’s utility and seller’s income.

Managing cloud security in the presence of strategic hacker and joint responsibility

The widespread use of cloud computing has brought cloud security to the forefront. The cloud provider and the firm assume varying degrees of joint responsibility for cloud security with cloud service models including IaaS, PaaS, and SaaS, to defend the strategic hacker. This paper builds a game-theoretical model to study cloud security management, in which we find that ignoring the strategic hacker leads to the dislocation security investment decisions (overinvestment or underinvestment) for the provider and the firm in bilateral refund contracts (BRCs). The strategic hacker’s attack effort is inverse U-shaped with cloud service models, leading to a free-riding problem between the provider and the firm. Furthermore, from the perspective of social welfare maximization, both the provider and the firm would underinvest or overinvest in cloud security. To solve the problem, we propose two new contract mechanisms: one is an internal effort-based contract, in which the provider oversees the firm internally and the compensation rate depends on the firm’s effort once the breach occurs. The other is an external effort-based contract, in which the monitoring agency supervises the efforts of the provider and the firm. We compare the two new contracts with BRCs and obtain the optimal choice for principals.

AC Network-Constrained Peer-to-Peer Electricity Market Model in Low-voltage Power Distribution Networks

The rapid growth of Distributed Energy Resources (DERs) and demand response in modern power grids calls for decentralized electricity trading mechanisms to effectively induce and coordinate responses from massive DERs and load entities. This paper proposes a fully decentralized peer-to-peer (P2P) distribution electricity trading mechanism backboned by a modified version of Alternating Direction Method of Multipliers (ADMM). Comparing with the state-of-the-art P2P electricity markets, the proposed electricity trading mechanism comprehensively incorporates power network constraints formulated by an alternating current power flow model. In the proposed method, nodal voltages are designed as the only public variables shared among participants. Privacy of each participant is thus sufficiently preserved without exposing the bid data to the others. Meanwhile, the proposed P2P method can also ensure the global optimality of social welfare in electricity transactions. Comprehensive case studies are carried out using the IEEE 33-node distribution system to demonstrate the feasibility and efficiency of proposed P2P electricity trading mechanism. The results indicate that the proposed model can efficiently integrates network constraints in the market clearing process while maintaining computational performance, ensuring stable convergence regardless of distribution system congestion. The proposed P2P electricity market can achieve global maximization of social welfare, with a relative difference of no more than 5.0% compared to the conventional centralized market. Even in worst-case scenarios, the relative difference remains within 7.32%. Meanwhile, the method accurately accounts for network losses, guaranteeing the feasibility of trading plans within the physical network.© 2017 Elsevier Inc. All rights reserved.

A game theoretic approach for time-of-use pricing with considering renewable portfolio standard effects and investment in energy storage technologies under government interventions

To remain in the competitive market, power companies, in addition to price competition, need to develop technologies to improve the reliability of electricity supply and achieve sustainable development goals. This paper explores energy pricing in an electricity supply chain consisting of renewable and conventional electricity producers and a distribution grid, focusing on improving supply reliability and achieving sustainable development goals. The study employs game theory to model government intervention in the electricity market through subsidization and Renewable Portfolio Standard (RPS) strategies. Three investment scenarios for improving reliability are considered, with producers determining wholesale prices and the distribution grid setting prices for low-and-high-load periods. The renewable producer and the distribution grid manage the reliability level by investing in energy storage technologies in the first and second scenarios, respectively, while it is ignored in the third scenario. The results reveal that optimal subsidy and RPS rates depend on the government's priorities regarding environmental effects, government revenue, and social welfare. Scenario two, is found to be the most favorable for improving supply reliability and achieving social welfare and environmental goals. Additionally, prioritizing social welfare maximization policy is found to be advantageous for producers' profit, the distribution grid, and supply reliability improvement.

The no surcharge rule and its welfare implication

This paper investigates the welfare implications of banning the no surcharge rule (NSR) in credit card markets. Conventional wisdom indicates that banning the NSR may increase or decrease social welfare. This paper finds that lifting the NSR unambiguously decreases social welfare in a closed system and in an open system when Visa and MasterCard privatize. In a closed system, the unambiguous welfare implication is due to the fact that the distortion of Merchant internalization (MI) fully offsets the distortion of market power, and the monopolistic credit card company acts as a social welfare maximizer when it maximizes its own profit. The credit card service is always optimally provided under the NSR, and lifting it, which eliminates the distortion of MI, always leads to an under-provision of credit card service and a decrease of social welfare. In an open system, the credit card service is always under provided under the NSR, because the market power of issuing banks works as another distortion in addition to those in a closed system. Banning the NSR will further drive down the card usage and social welfare decreases.

Dual reference effect and dynamic control of quality improvement and low-carbon effort under nonlinear demand

Businesses often engage in quality improvement efforts to enhance their competitiveness in the market and increase their product sales. However, in a low-carbon economy, it is crucial for enterprises to incorporate green development into their business strategies. This paper uses optimal control theory to study the dynamic control problem of enterprise quality improvement and low carbon input under consumer reference quality and reference low carbon, analyzes and compares the input decisions under two conditions of profit maximization by the enterprise and social welfare maximization by the government, explores the system steady-state equilibrium and performs simulation analysis. It is shown that (i) there is a unique saddle-point equilibrium of the system in both cases and it depends on the discount rate and memory parameters (ii) the higher the consumer loyalty to product quality or low-carbon attributes, the corresponding instantaneous investment rate increases (iii) there is a cross-effect of reference quality and reference low-carbon on the firm's decision, and the impact of product quality on the firm's profit is higher than that of low-carbon attributes (iv) the social welfare maximization conditions are all higher than the investment in the firm's profit maximization conditions. Based on this, enterprises can make investment decisions more rationally, while also helping the government conduct macroeconomic regulation and control.

Courier Dispatch in On-Demand Delivery

We study a courier dispatching problem in an on-demand delivery system in which customers are sensitive to delay. Specifically, we evaluate the effect of temporal pooling by comparing systems using the dedicated strategy, with which only one order is delivered per trip, versus the pooling strategy, with which a batch of consecutive orders is delivered on each trip. We capture the courier delivery system’s spatial dimension by assuming that, following a Poisson process, demand arises at a uniformly generated point within a service region. With the same objective of revenue maximization, we find that the dispatching strategy depends critically on customers’ patience level, the size of the service region, and whether the firm can endogenize the demand. We obtain concise but informative results with a single courier and assuming that customers’ underlying arrival rate is large enough, meaning a crowded market, such as rush hour delivery. In particular, when the firm has a growth target and needs to achieve an exogenously given demand rate, using the pooling strategy is optimal if the service area is large enough to fully exploit the pooling efficiency in delivery. Otherwise, using the dedicated strategy is optimal. In contrast, if the firm can endogenize the demand rate by varying the delivery fee, using the dedicated strategy is optimal for a large service area. The reason is that it is optimal for the firm to sustain a relatively low demand rate by charging a high fee for a large service radius: within this large area, the pooling strategy leads to a long wait because it takes a long time for multiple orders to accumulate. Moreover, with an exogenous demand rate to meet, customers’ patience level has no impact on the dispatch strategy. However, when the demand rate can be endogenized, the dedicated strategy is preferable if customers are impatient. Furthermore, we extend our model to account for social welfare maximization, a hybrid contingent delivery policy, a general arrival rate that does not have to be large, a nonuniform distribution of orders in the service region, and multiple couriers. We also conduct numerical analysis and simulations to complement our main results and find that most insights in our base model still hold in these extensions and numerical studies. This paper was accepted by Jeannette Song, operations management. Funding: This work was supported by the Natural Sciences and Engineering Research Council of Canada [Grants RGPIN-2015-06757 and RGPIN-2021-04295]. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2023.4858 .

An Efficient Online Auction for Placing and Pricing Cloud Container Clusters

Containers are more lightweight and agile, which are merging as an alternative to virtual machines to provide efficient cloud computing services. For cloud service providers, there are two main challenges for provisioning container clusters: i) Design an optimal placement scheme while considering the communication between containers; ii) Flexible pricing mechanism, which not only meets the market demand but also maximizes the social welfare or provider's profit. To solve the above challenges, this paper proposes an efficient auction that achieves truthfulness and computational efficiency, targeting social welfare and provider's profit maximization. We first model the problem into a non-conventional integer linear program (ILP), considering both inter-container traffic flow and resources consumed in containers. We then introduce auxiliary variables and reformulate them to a classical packing-type ILP. Second, we apply the primal-dual technique to design an effective online auction algorithm, based on the number of consumed resources to ensure the flexible supply of resources while obtaining maximum social welfare. Third, we use a randomized reduction algorithm to convert the optimization objective to the provider's profit maximization. We prove that the competitive ratio of the two auctions are <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ln \lambda$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(\ln \lambda +\log _{2}\kappa)$</tex-math></inline-formula> , and verify that our auctions perform well in various settings through simulations.

Transnational remanufacturing decisions under carbon taxes and tariffs

Considering the influence of carbon taxes and tariffs on transnational closed-loop supply chains, this paper establishes three remanufacturing modes of an original equipment manufacturer (OEM) in an exporting country. In one remanufacturing mode, the OEM conducts remanufacturing itself, and in the other two modes, the OEM authorizes a retailer in the importing country to engage in remanufacturing. Next, we analyse the optimal pricing and carbon emissions reduction decisions of the OEM and retailer in the different modes and further study how different levels of carbon tax and tariff combinations impact OEM remanufacturing decisions, the social welfare of importing countries and the environment. The results show the following. (1) When the carbon tariff is high, the optimal sales of remanufactured products increase. However, this does not contribute to the continuation of the product system; thus, it is short sighted for the government to hastily set high carbon tariffs to discourage the import of new products to protect domestic enterprises. (2) Carbon tariffs cannot effectively encourage the OEM to invest in emission reduction because carbon tariffs are passed on to consumers in importing countries through price adjustment. However, carbon tax may be an indirect factor affecting the OEM's willingness to invest in emission reduction. (3) The OEM favours allowing the retailer to remanufacture when the carbon emissions of the remanufactured products are similar to those of new products. Co-investment in emission reduction has the potential to align the profit maximization of the OEM with the social welfare maximization of the importing country. (4) It is not profitable for the importing government to set high carbon tariffs to protect its own enterprises.