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In recent years, energy sharing has attracted a lot of attention. However, the intermediate platforms in centralized energy‐sharing methods cause the rapid growth of communication complexity and the risk of privacy leakage. Unlike complex energy‐sharing market mechanisms, in this paper, a simple and efficient distributed energy sharing for microgrids (MGs) is proposed, where game theory is employed to form flexible prices for prosumers and only local information is used to improve the privacy protection of prosumers. First, prosumers located in different areas are characterized more precisely and a two‐tier carbon emission cost model is built. Next, a game‐theory‐based distributed energy‐sharing model is proposed, where a flexible pricing mechanism is developed to enable prosumers to independently reach price agreements and achieve supply–demand balance within MGs. In the process, optimization models for obtaining equilibrium are formed and only local information is needed. However, solving these optimization models is generally time‐consuming. So, a distributed optimization method based on weighted subgradients is proposed to accelerate the equilibrium‐finding process. Finally, four cases are designed, and simulation results demonstrate that the prosumers' costs of our method are reduced by 7.5%–22.5% compared to the costs obtained by feed‐in tariff. Moreover, in the case of solving the distributed trading model for an MG at a 24‐h time scale, the iteration numbers of our method are only 38.9% and 49.3% of the two traditional solving methods. [ABSTRACT FROM AUTHOR]

The accelerated adoption of Electric Vehicles (EVs) necessitates innovative and effective pricing strategies for charging infrastructure. This study leverages the MATSim (Multi-Agent Transport Simulation) framework to meticulously evaluate the performance of two distinctive EV charging pricing models: time-based charging and a hybrid model integrating both usage-based and time-based components. Driven by the pivotal question of how EV charging should be optimally priced—whether contingent on energy consumption or charging duration—the research endeavors to conduct two simulations. These simulations aim to provide a comprehensive comparative analysis, evaluating metrics such as total power derived from grids, utility revenue, charging station queues and served vehicles. In the time-based charging scenario, EVs incur charges based on their plugged-in duration, reflecting a pricing approach that correlates directly with the time a vehicle remains connected to the charging station. In contrast, in the hybrid model, EVs undergo initial billing based on usage until a predetermined battery charge point is reached, such as achieving a full battery. Subsequently, time-based pricing takes effect until the user disconnects the vehicle. The findings indicate that, in the combined approach, utility owners have the potential to generate more revenue. Conversely, the time-based approach demonstrates a capacity to serve a higher number of electric EVs, with comparable queue lengths observed in both approaches. Importantly, using the results of this paper, policymakers can suggest pricing schemes that maximize benefits for utility owners, reduce queues at charging stations, and ensure the security of power grids. [ABSTRACT FROM AUTHOR]

Price discovery studies of a single asset traded in multiple markets have traditionally focused on assessing the relative price discovery contribution of each market. However, in this paper, we demonstrate that the overall price discovery across all markets can undergo changes even when the relative price discovery of each market remains constant. We propose that this overall change in price discovery can be effectively captured by the fractional parameter in the fractionally cointegrated vector autoregressive (FCVAR) model. In contrast, the widely used cointegrated vector autoregressive (CVAR) model fails to account for this dynamic in overall price discovery. Through a combination of simulation exercises and empirical applications, we show that the FCVAR approach outperforms the CVAR model not only in evaluating the relative price discovery contributions but also, more importantly, in providing a comprehensive measurement of overall price discovery. [ABSTRACT FROM AUTHOR]

In tourist facilities, managing ticket sales can reduce congestion imbalances. This study reports on the results of a pilot experiment conducted at Kaiyukan in Osaka, one of the largest aquariums in Japan. The experiment utilized pre-sale ticket data for controlling admission time intervals, smoothing the number of admissions through dynamic pricing, and predicting visitor numbers. The study reviewed the effectiveness of each of these approaches to alleviate congestion inside Kaiyukan. We then report on a method to predict the number of visitors from the pre-sale conditions of Kaiyukan admission tickets. It was found that setting entry times to 15-minute intervals was most operationally advantageous for the Kaiyukan. Moreover, the behavioral effects induced by variable pricing were more effective when prices were changed based on time slots rather than on a daily basis. Compared to the Holt–Winters' method, we were able to maintain stable prediction accuracy even during consecutive holidays and long school vacation seasons. [ABSTRACT FROM AUTHOR]

In recent years, China’s real estate prices have continued to rise, preventing the bursting of the house price bubble, and the various risks it triggers has become an important issue that governments at all levels have to face. In this paper, the backward sup ADF (BSADF) method was used to dynamically portray the evolution of real estate price bubbles in 31 large and medium-sized cities in China over a period of 11 years, from 2013 to 2021, and the dynamic relationship between the degree of financial support for real estate (hereinafter referred to as financial support) and house price bubbles in cities was investigated by employing a panel vector autoregressive (PVAR) model with panel data. The results showed that: Multiple cyclical bubbles significantly existed in the real estate markets of the cities during the study period, and in general, house price bubbles appeared earlier in the more economically developed regions than in the less economically developed regions and have spread to the less economically developed regions. Financial support contributed to house price bubbles, supporting the theory of excessive financial support. Furthermore, the results of the sub-sample showed that financial support contributed most strongly to house price bubbles in cities in the northern region. [ABSTRACT FROM AUTHOR]

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This study investigates for the first time how public charging infrastructure usage differs under the presence of diverse pricing models. About 3 million charging events from different European countries were classified according to five different pricing models (cost-free, flat-rate, time-based, energy-based, and mixed) and evaluated using various performance indicators such as connection duration; transferred energy volumes; average power; achievable revenue; and the share of charging and idle time for AC, DC, and HPC charging infrastructure. The study results show that the performance indicators differed for the classified pricing models. In addition to the quantitative comparison of the performance indicators, a Kruskal–Wallis one-way analysis of variance and a pairwise comparison using the Mann–Whitney-U test were used to show that the data distributions of the defined pricing models were statistically significantly different. The results are discussed from various perspectives on the efficient design of public charging infrastructure. The results show that time-based pricing models can improve the availability of public charging infrastructure, as the connection duration per charging event can be roughly halved compared to other pricing models. Flat-rate pricing models and AC charging infrastructure can support the temporal shift of charging events, such as shifting demand peaks, as charging events usually have several hours of idle time per charging process. By quantifying various performance indicators for different charging technologies and pricing models, the study is relevant for stakeholders involved in the development and operation of public charging infrastructure. [ABSTRACT FROM AUTHOR]

Fashion sellers are sometimes critiqued for selling low durability products, resulting in waste. Blame is also directed at consumers, who purchase new fashions despite having accumulated a closet full of prior fashions. The "slow fashion" movement encourages sellers to produce higher durable products, thus supporting less frequent purchases by consumers. Using an infinite-time model and considering strategic consumer behavior, including their ability to accumulate a "closet" of varieties over time, we analyze the seller's profit-maximizing price and product-durability decisions. We initially assume a static price but later analyze the potential profit gains from dynamic pricing. When analyzing a heterogeneous consumer market, we initially allow customers to vary (distributed uniformly) in their sensitivity to fashion. Subsequently, we explore alternative distributions for consumers' fashion sensitivity and the correlation between their fashion sensitivities and product valuations. Using this framework, we show how the seller's optimal price and durability decisions yield distinct shopping segments, which we refer to as the minimalist versus trend-chasers. We find that if the degree of fashion uncertainty is moderate, the seller's optimal choice of product durability will support the emergence of both behaviors. As the variety uncertainty expands, if the seller's costs are sufficiently low, it will support a throwaway culture via disposable products. Otherwise, given high costs, the seller optimally targets a slow fashion-type outcome, with consumers targeting reuse (with durability) rather than variety. Our findings shed light on consumers' optimal purchasing behaviors in relation to both market parameters and the firm's pricing and durability decisions. [ABSTRACT FROM AUTHOR]

We consider a dynamic pricing problem in which a firm sells one item to a single buyer to maximize expected revenue. The firm commits to a price function over an infinite horizon. The buyer arrives at some random time with a private value for the item. He is more impatient than the seller and strategizes over the timing of the purchase in order to maximize his expected utility, which implies either buying immediately, waiting to benefit from a lower price, or not buying. We study the value of the seller's ability to observe the buyer's arrival time in terms of her expected revenue. When the seller can observe the buyer's arrival, she can make the price function contingent on the buyer's arrival time. On the contrary, when the seller can't, her price function is fixed at time zero for the whole horizon. The value of observability (VO) is defined as the worst-case ratio between the expected revenue of the seller when she observes the buyer's arrival and that when she does not. First, we show that, for the particular case in which the buyer's valuation follows a monotone hazard rate distribution, the upper bound of VO is exp(1). Next, we show our main result: in a setting very general on valuation and arrival time distributions: VO is at most 4.911. To obtain this bound, we fully characterize the solution to the observable arrival problem and use this solution to construct a random and periodic price function for the unobservable case. Finally, we show by solving a particular example to optimality that VO has a lower bound of 1.136. This paper was accepted by Itai Ashlagi, revenue management and market analytics. Funding: This work was partially supported by the Agencia Nacional de Investigación y Desarrollo Chile [Grants ACT210005 and FONDECYT 1220054], and by the Center for Mathematical Modeling at the University of Chile [Grant FB210005]. The work of D. Pizarro has benefited from the Artificial and Natural Intelligence Toulouse Institute, which is funded by the French "Investing for the Future – PIA3" program [Grant ANR-19-P3IA-0004]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2023.4794. [ABSTRACT FROM AUTHOR]

As an on-demand mobility service, parking reservations can greatly alleviate the issue of parking challenges. There are currently three primary strategies for parking reservation: first-come-first-served, permit reservation, and auction. In contrast to the first-come-first-served and permit-reserved strategies, the auction strategy uses dynamic pricing to allocate parking supplies efficiently based on the auction, which attracts more scholars for the research. However, parking reservations based on the auction process may have an inequity issue because drivers' age, gender, income level, and location of residence fluctuate. This inequity may limit the growth of reserved parking by influencing parking drivers' acceptance of reserved parking. But currently, very few scholars focus on the issue of reserved parking equity, and even fewer measure this nebulous and personal issue. In consideration of this, the Lorenz curve of parking reservation and the vertical equity index of parking reservation are proposed in this paper along with the calculation method for the index, which enables the problem of reserved parking vertical equity to be visualized and made concrete. The numerical experimental method is used to analyze the vertical equity of drivers with varying income levels, utilizing the Vickrey–Clarke–Groves (VCG) auction process as an example. According to the research, loss-averse drivers are more than gain-neutral and gain-seeking drivers when the income levels of the drivers using reserved parking are the same. With the increasing number of high-income drivers involved in the parking reservation, medium to low-income drivers would lose their chances of successful reservations because of their uncompetitive bid price which leads to inequity issues when the number is less than the number of parking spaces. In contrast, the vertical equity index changes more for gain-seeking drivers while being generally steady when loss-averse and gain-neutral drivers participate. For instance, when the profit and loss coefficient is 3 and 15% of drivers with high-income levels use the parking reservation platform, the vertical equity index rises from 0.09128 to 0.45434. The reference price has a moderating influence on the vertical equity index when the number of driver participants at high-income levels remains constant. In general, within a reasonable range, the higher the reference price, the more equitable the parking reservation procedure and the lower the vertical equity index. [ABSTRACT FROM AUTHOR]

The Suez Canal (SC) serves as the shortest maritime transport route from east to west. In the absence of the SC, global trade and transportation costs would increase substantially, impeding the expansion potential of the global economy. The Belt and Road Initiative (BRI) is a key component of China's future international trading network, with significant implications for global seaborne trade. The BRI's two primary pillars are the Maritime Silk Road (MSR) and the Silk Road Economic Belt, both of which have significant infrastructural investments. The MSR connects China to various regions in Asia, Africa, and Europe via the SC, thereby serving as a significant maritime trade route on a global scale, particularly between Europe and China. This is due to the SC's distinctive positioning on the MSR. Consequently, studying the significance of BRI for the SC cargo trade is crucial. This study uses annual data from 1990 to 2022 to examine this dynamic relationship. To account for the interaction effect of the variables, we use the vector autoregressive model and the impulse response function. Model results show that China's seaborne trade will increase SC trade by 23%, and China's BRI investment projects are anticipated to have a 5% significant impact on SC cargo trade and will continue to grow in the medium and long run. According to these findings, the SC has to continue to adopt more flexible pricing and marketing strategies to encourage and attract more customers. As a result, the SC could become a global logistics center and transform from a trade gateway to a global hub if it develops more value-added activities in its adjacent areas and attracts substantial Chinese investments. [ABSTRACT FROM AUTHOR]

In order to improve the operating benefits of the distribution network (DN) and reduce the energy consumption costs of small-micro industrial parks (SMIPs), a two-layer optimal electricity trading method for DN with SMIPs is proposed. First, based on the Stackelberg game, a multi-objective two-layer optimal trading model for DN and SMIP is established. In the upper layer, the DN agent is regarded as the leader, and a trading model is established with the goal of maximizing the profits of agents. In the lower layer, an energy optimization model is proposed for the SMIP operators, which are regarded as the followers, with the goal of minimizing the operating costs. According to the buying and selling electricity prices at the upper and lower layers, a dynamic pricing strategy is formulated. The Karush-Kuhn-Tucker condition (KKT) is introduced to transform the two-layer model into a single-layer model, and based on linear transformations, the model is further converted into a mixed-integer linear programming model. The transformations aim to address the non-linear issues arising from multivariable coupling between the upper and lower-layer trading models. The simulation results show that the trading strategy proposed in this paper can effectively increase the profit of DNs while reducing the operating costs of SMIPs and can provide a reference for decision-making in the electricity market (EM) with the participation of SMIP. [ABSTRACT FROM AUTHOR]

Users possess the option to rent instances of various sorts, in a variety of regions, and a variety of availability zones, thanks to cloud service carriers like AWS, GCP, and Azure. In the cloud business right now, fixed price models are king when it comes to pricing. However, as the diversity of cloud providers and users grows, this approach is unable to accurately reflect the market's current needs for cost savings. As a consequence, a dynamic pricing strategy has become a desirable tactic to better handle the erratic cloud demand. In this study, a deep learning model was used to propose a dynamic pricing structure that ensures service providers are treated fairly in a multi-cloud context. The computational optimization of DL approaches can be severely hampered by the requirement for human hyperparameter selection. Traditional automated solutions to this issue have inadequate durability or fail in specific circumstances. To choose the hyper-parameters in the Dueling Deep Q-Network (DDQN), the hybrid DL approach in this study uses the concept-based wild horse optimization (WHO) method. A community of untamed horses is evolved, and the fitness of the population is evaluated concurrently to estimate the optimum hyper-parameters. The plan changes the price appropriately to promote the use of underutilized resources and discourage the use of overutilized resources. The evaluation's findings demonstrated that the suggested strategy can lower end-user costs while conducting compute- and data-intensive activities in a multi-cloud environment. The research was concluded by comparing current models after the results were analyzed using various performance indicators. [ABSTRACT FROM AUTHOR]

With the demand response and the massive access of distributed energy to the distribution network, it is possible to solve the transmission congestion problem by coordinating the controllable resources in a transmission network and distribution network. Aiming at resolving the problems of scattered side response resources and difficult-to-negotiate compensation prices, a bi-level optimal congestion scheduling strategy based on flexible load dynamic compensation prices is proposed. Under this strategy, the transmission network layer aims at minimizing the congestion cost and optimizes the adjustment scheme of the generator set and the node price. The active distribution network layer obtains the dynamic compensation price of the flexible load of the distribution network through the load characteristics and the node price. Through the interaction and coordination between the two layers, an optimal congestion scheduling scheme is obtained, and the transmission and distribution jointly solve the congestion problem. Based on the modified IEEE-39 experimental system, the effectiveness of the proposed strategy is verified via a simulation. [ABSTRACT FROM AUTHOR]

We combine nonparametric price response modeling and dynamic pricing. In particular, we model sales response for fast-moving consumer goods sold by a physical retailer using a Bayesian semiparametric approach and incorporate the price of the previous period as well as further time-dependent covariates. All nonlinear effects including the one-period lagged price dynamics are modeled via P-splines, and embedding the semiparametric model into a Hierarchical Bayesian framework enables the estimation of nonlinear heterogeneous (i.e., store-specific) immediate and lagged price effects. The nonlinear heterogeneous model specification is used for price optimization and allows the derivation of optimal price paths of brands for individual stores of retailers. In an empirical study, we demonstrate that our proposed model can provide higher expected profits compared to competing benchmark models, while at the same time not seriously suffering from boundary problems for optimized prices and sales quantities. Optimal price policies for brands are determined by a discrete dynamic programming algorithm. [ABSTRACT FROM AUTHOR]

The global shipping industry still frequently faces the problems of low profitability and excessive capacity despite the claimed benefits of capacity sharing between alliance members. This paper investigates capacity sharing within shipping alliance members and its impacts on social welfare under stochastic demand. The dynamic game models constructed in this study examine four scenarios based on whether the alliance members share their surplus capacities and whether their pricing is flexible (their prices are determined before or after the demand realization). The results suggest that flexible pricing always harms the social welfare while capacity sharing improves the social welfare. Without capacity sharing, the government's subsidy or tax is needed when the carriers' service substitute degree is low or high, respectively. If the carriers agree with capacity sharing, the tax is needed under flexible pricing while neither subsidy nor tax is needed under the inflexible pricing. This paper provides explanations on the low profits of the shipping lines with their pricing and alliance strategies. Moreover, the policy implications identified in this study can provide support for governments to make their regulation on carriers. [ABSTRACT FROM AUTHOR]

This paper aims to make contributions to balancing the passenger flow among high-speed railway trains of common lines as well as raise revenue via a behavioral-based optimal dynamic pricing. Based on an HSR trip choice survey, a set of Nested-logit (NL) models is built to analyze passengers' preferences for different factors under various scenarios. The upper level belongs to the departure time period while the lower level belongs to specific trains with different levels of service. Given the conditions of fixed transportation capability, an optimization model is built to optimize the pricing among trains of common lines. The NL model results show that the time values of round trips are greater than that of one-way trips, and the time values of business trips are greater than that of leisure trips. Besides, the optimization model is verified to be capable of raising revenue, improving passenger flow equilibrium, and inducing extra passenger demand. [ABSTRACT FROM AUTHOR]

In this article, we study the contextual dynamic pricing problem where the market value of a product is linear in its observed features plus some market noise. Products are sold one at a time, and only a binary response indicating success or failure of a sale is observed. Our model setting is similar to the work by? except that we expand the demand curve to a semiparametric model and learn dynamically both parametric and nonparametric components. We propose a dynamic statistical learning and decision making policy that minimizes regret (maximizes revenue) by combining semiparametric estimation for a generalized linear model with unknown link and online decision making. Under mild conditions, for a market noise cdf F (·) with mth order derivative ( m ≥ 2 ), our policy achieves a regret upper bound of O ˜ d (T 2 m + 1 4 m − 1 ) , where T is the time horizon and O ˜ d is the order hiding logarithmic terms and the feature dimension d. The upper bound is further reduced to O ˜ d (T) if F is super smooth. These upper bounds are close to Ω (T) , the lower bound where F belongs to a parametric class. We further generalize these results to the case with dynamic dependent product features under the strong mixing condition. for this article are available online. [ABSTRACT FROM AUTHOR]

The study investigates the effect of price support policies on market price distribution and its dynamics in the Indian wheat market. The analysis uses a quantile autoregression model that provides a flexible representation of price dynamics and the 2001–2020 monthly wholesale market price data. The analysis is conducted conditional on the net stock level held in the previous period. The results reveal that the net purchase by the government prevented very low market prices for wheat but resulted in price spikes. It has a price‐enhancing effect as well. The associated moments of price distribution show that public stockholding reduced variation in market price distribution. However, the government's release of stock did not prevent price rises. Findings show that dynamic adjustments tend to be qualitatively different across regimes. Government intervention in the grain market reduced stability through dynamic adjustments in wheat market prices. The results have policy implications for India and other countries in Southeast Asia in the context of the WTO's negotiations on public stockholdings and using public stockholdings as an instrument in addressing price volatility and food shortages. [ABSTRACT FROM AUTHOR]

Learning algorithms can take a substantial amount of time to converge, thereby raising the need to understand the role of discounting in learning. In "Dynamic Pricing and Learning with Discounting," Z. Feng, M. Dawande, G. Janakiraman, and A. Qi examine the impact of discounting on learning by examining two classic dynamic-pricing and learning problems studied in Broder and Rusmevichientong (2012) and Keskin and Zeevi (2014). In both settings, the retailer initially does not know the parameters of the demand model. Given a discount factor, the retailer's objective is to determine a pricing policy to maximize the discounted revenue over a selling horizon. The authors establish lower bounds on the regret under any policy and propose new asymptotically optimal policies that take the discount factor into consideration. They numerically examine the regret under the proposed policies and the existing policies in the aforementioned two papers. In many practical settings, learning algorithms can take a substantial amount of time to converge, thereby raising the need to understand the role of discounting in learning. We illustrate the impact of discounting on the performance of learning algorithms by examining two classic and representative dynamic-pricing and learning problems studied in Broder and Rusmevichientong (BR) [Broder J, Rusmevichientong P (2012) Dynamic pricing under a general parametric choice model. Oper. Res. 60(4):965–980] and Keskin and Zeevi (KZ) [Keskin NB, Zeevi A (2014) Dynamic pricing with an unknown demand model: Asymptotically optimal semi-myopic policies. Oper. Res. 62(5):1142–1167]. In both settings, a seller sells a product with unlimited inventory over T periods. The seller initially does not know the parameters of the general choice model in BR (respectively, the linear demand curve in KZ). Given a discount factor ρ, the retailer's objective is to determine a pricing policy to maximize the expected discounted revenue over T periods. In both settings, we establish lower bounds on the regret under any policy and show limiting bounds of Ω (1 / (1 − ρ)) and Ω (T) when T → ∞ and ρ → 1 , respectively. In the model of BR with discounting, we propose an asymptotically tight learning policy and show that the regret under our policy as well that under the MLE-CYCLE policy in BR is O (1 / (1 − ρ)) (respectively, O (T)) when T → ∞ (respectively, ρ → 1). In the model of KZ with discounting, we present sufficient conditions for a learning policy to guarantee asymptotic optimality and show that the regret under any policy satisfying these conditions is O (log (1 / (1 − ρ)) 1 / (1 − ρ)) (respectively, O (log T T)) when T → ∞ (respectively, ρ → 1). We show that three different policies—namely, the two variants of the greedy iterated least squares policy in KZ and a different policy that we propose—achieve this upper bound on the regret. We numerically examine the behavior of the regret under our policies as well as those in BR and KZ in the presence of discounting. We also analyze a setting in which the discount factor per period is a function of the number of decision periods in the planning horizon. Funding: Z. Feng received support from the National Natural Science Foundation of China [Grant 72201256]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2023.2477. [ABSTRACT FROM AUTHOR]

In "Joint Inventory and Pricing for a One-Warehouse Multistore Problem: Spiraling Phenomena, Near Optimal Policies, and the Value of Dynamic Pricing," Lei, Liu, Jasin, and Vakhutinsky consider a joint inventory and pricing problem with one warehouse and multiple stores with lost sales. The retailer makes a one-time decision on the amount of inventory to be placed at the warehouse at the beginning of the selling season, followed by periodic joint replenishment and pricing decisions for each store throughout the season. The authors first analyze the performance of two popular and simple heuristic policies that directly implement the solution of a deterministic approximation of the original stochastic problem. They show that simple reoptimization of the deterministic approximation may worsen the performance by causing a "spiraling up" movement in expected lost sales quantity. The authors further propose two improved heuristic policies with provably near-optimal performance. In particular, the first policy achieves the best possible performance among all policies that rely on static pricing, and the second policy outperforms the first one because of its use of carefully designed dynamic pricing scheme. We consider a joint inventory and pricing problem with one warehouse and multiple stores in which the retailer makes a one-time decision on the amount of inventory to be placed at the warehouse at the beginning of the selling season, followed by periodic joint replenishment and pricing decisions for each store throughout the season. Demand at each store follows a Poisson distribution, and unmet demand is immediately lost. The retailer incurs the usual variable ordering, inventory holding, and lost sales costs, and the retailer's objective is to maximize the expected total profits. The optimal control (or policy) for this problem is unknown and numerically challenging to compute. We first analyze the performance of two popular and simple heuristic policies that directly implement the solution of a deterministic approximation of the original stochastic problem. We show that simple reoptimization of the deterministic problem may yield a very poor performance by causing a "spiraling down" movement in price trajectory, which, in turn, yields a "spiraling up" movement in expected lost sales quantity (i.e., the expected lost sales quantity continues to increase as we reoptimize more frequently). This finding cautions against a naive use of simple reoptimizations without first understanding the dynamics of the model. We then propose two improved heuristic policies based on the optimal solution of a deterministic relaxation of the original stochastic problem. Our first heuristic policy computes static prices and order-up-to levels for the warehouse and stores and then replenishes each store at the beginning of each batch of periods. Our second heuristic policy builds on the first heuristic and dynamically adjusts prices over time based on realized demands. We show that both policies have near-optimal performance when the annual market size is large with the second policy outperforming the first one. Finally, we also prove a fundamental theoretical lower bound on the performance of any policy that relies on static prices. This lower bound highlights the true value of dynamic pricing, whose effect on performance in our setting cannot be duplicated by simply implementing a more sophisticated replenishment policy. Funding: Y. (M.) Lei is partially supported by the Natural Sciences and Engineering Research Council of Canada [Fund 1378108, Grant RGPIN-2021-02973]. S. Liu is partially supported by the Natural Sciences and Engineering Research Council of Canada [Fund 513987, Grant RGPIN-2022-04950]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2022.2389. [ABSTRACT FROM AUTHOR]

This research delves into the empirical performance of deterministic option pricing models in the dynamic financial landscape of India. The primary focus is on uncovering pricing discrepancies and discerning whether these disparities arise from inherent limitations in the theoretical foundations of the models or are influenced by the trading behaviors of market participants. The investigation centers on the analysis of call and put option contracts for the Nifty Index and Bank Nifty Index, both extensively traded on the National Stock Exchange (NSE) of India. The study's findings highlight that models developed to address the theoretical constraints of the benchmark Black–Scholes model demonstrate noteworthy performance. However, the complexity of these models does not consistently translate into enhanced pricing efficiency. Notably, the Black–Scholes and Practitioner Black–Scholes models exhibit superior performance across various moneyness-maturity categories. Furthermore, the research underscores the substantial impact of option contract liquidity on the efficiency of the pricing models. Specifically, highly traded at-the-money and out-of-the-money option contracts exhibit a higher level of pricing accuracy. [ABSTRACT FROM AUTHOR]

This paper studies the potential impact of 'paid translation crowdsourcing' on translators' status through a corpus study of discourses found in language industry websites. It has been argued in Translation Studies literature that in this model crowdsourcing companies attempt to redefine translation 'professionalism' or 'competence' as a monolithic notion to include a dynamic range of price segments supposedly associated with degrees of translation competence and fitness-for-purpose. The results of the corpus study show that industry websites present a range of 'expertise' or 'skillset' in which different levels of translation competence associated with different content types and fit-for-purpose tiers coexist. The study concludes that published materials in themselves do not display the potential to impact the status of translators negatively, but rather the opposite: the website materials reinforce that for some content types or high-quality levels, high levels of expertise and professionalism are required. Nevertheless, this might not be necessary for all translation projects, content types and-or client needs. [ABSTRACT FROM AUTHOR]

Organizations in the USA are progressively employing AI-driven dynamic pricing as a strategic intervention to flexibly modify their prices based on competition, market demand, and various other factors. This research paper focused on the ethical dimensions of AI-driven dynamic pricing and the crucial interplay between profitability and the establishment of unwavering consumer transparency and fairness. The recommended models for dynamic pricing solutions entailed ensemble learning methods, notably, XG-Boost, Light-GBM, Cat-Boost, and X-NGBoost models. Particularly, the proposed model consolidated the XG-Boost algorithm and the NG-Boost model, resulting in a novel methodology termed the X-NGBoost. To compare and contrast the performance of the proposed models, these algorithms were trained and subjected to the same dataset. The comparison between the models was mainly grounded on the root-mean-square error (RMSE) metric, which was quantified in meters. The results indicated that X-NGBoost had the lowest RMSE on both the testing and training sets, at 4.23 and 5.34 respectively. This indicated that X-NGBoost performed very well on both seen and unseen data. Therefore, from the outcomes it was deduced that, for the provided data set, the X-NGBoost model provided the accurate pricing solution. [ABSTRACT FROM AUTHOR]

Problem definition: Designing effective operational strategies requires a good understanding of customer behavior. The classic economic theory of customer choice has long been the paradigm in the operations literature. However, the rise of online marketplaces such as e-commerce has triggered considerable efforts in academia and industry to develop alternative models that not only provide a good approximation of customer behavior but also are easily scalable for large-scale implementations. In this paper, we consider a multiproduct dynamic pricing problem with limited inventories under the so-called cascade click model, which is one of the most popular click models used in practice and has been intensively studied in the computer science literature. Methodology/results: We present some fundamental results. First, we derive a sufficiently general characterization of the optimal pricing policy and show that it has a different structure than the optimal policy under the standard pricing model. Second, we show that the optimal expected total revenue under the cascade click model can be upper bounded by the objective value of an approximate deterministic pricing problem. Third, we show that two policies that are known to have strong performance guarantees in the standard revenue management setting can be properly adapted (in a nontrivial way) to the setting with cascade click model while retaining their strong performance. Finally, we also briefly discuss the joint ranking and pricing problem and provide an iterative heuristic to calculate an approximate ranking. Managerial implications: Taking into account customers' click-and-search behavior leads to different structures of the optimal pricing policy, and some common insights under the standard pricing models may no longer hold. Moreover, our simulation studies show that pricing under a (misspecified) classic choice model that is oblivious to customers click-and-search behavior can severely impact profitability. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0504. [ABSTRACT FROM AUTHOR]

Firms' inflexibility in adjusting output prices to economic shocks exacerbates information asymmetry with respect to firms' profits, but public information on firms' cost structure mitigates this problem. We construct a novel form of public information from economic statistics disclosed by the government and find that such public information significantly reduces inflexible‐price firms' bid–ask spreads, the probability of informed trading, and analyst forecast dispersions, but these results do not hold for flexible‐price firms. Security analysts seek more cost‐related information during conference calls about inflexible‐price firms, but such a phenomenon is observed less frequently if a firm's input cost is more publicly observable. In addition, stock markets react more strongly to earning news announced by inflexible‐price firms, consistent with our intuition. [ABSTRACT FROM AUTHOR]

I use a structural model with perishability data to analyze the welfare effects of two potential solutions to grocery-retail food waste: dynamic pricing and organic waste landfill bans. I analyze the welfare effects of two potential solutions to excess grocery-retail food waste: dynamic pricing and organic waste landfill bans. I use a structural econometric model with sales, perishability, and marginal cost data from a grocery chain's artisanal bread category. My analysis of these counterfactuals shows that dynamic pricing Pareto-dominates an organic waste ban: Dynamic pricing reduces waste by 21% while increasing the chain's gross margins and consumer surplus by 3% and 0.3%, respectively. In contrast, an organic waste ban, simulated by a ten-fold increase in disposal costs, reduces waste by only 4% and decreases profits and consumer surplus. Therefore, if regulators want to reduce grocery-store waste, they should incentivize chains to adopt dynamic pricing over imposing organic waste bans. History: K. Sudhir served as the senior editor for this article. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mksc.2020.0214. [ABSTRACT FROM AUTHOR]

With the emergence of knowledge economy, knowledge payment has become an essential means of knowledge circulation. This study employs a system dynamics approach and integrates signal theory and customer value perception to investigate the dynamic pricing strategies for various knowledge payment products from the perspective of knowledge producers. First, this study analyzes the categories of knowledge payment products and proposes a theoretical research framework for designing dynamic pricing strategies for different products categories. Second, this study identifies the key factors that impact the profitability of knowledge payment platforms and construct a system dynamics model of product pricing strategies for knowledge payment platforms; next, three typical knowledge payment products on Zhihu Live are chosen as research objects, and system dynamics equations are constructed to simulate the profitability changes of different types of products in different periods. Finally, the results of simulation and sensitivity analysis are combined to select dynamic pricing strategies for different products types. This study offers an innovative exploration of multi‐stage pricing strategies for different types of knowledge payment products, which fills the gap in existing research and provides more scientific suggestions for knowledge producers and consumers in terms of knowledge transactions. [ABSTRACT FROM AUTHOR]

To explore the potential benefits of differentiated pricing compared to uniform pricing within supply chain networks, we constructed a two‐period dynamic pricing model for products in this research. In our scenario, the manufacturer produces the original product during the initial period and introduces a new‐generation product in the subsequent period. Simultaneously, the retailer handles the original product during the initial period and transitions to selling the new‐generation product boosted by advertising efforts in the following period. Our analysis of the proposed models yielded three primary insights. First, the adoption of a differentiated pricing strategy may not universally favor supply chain participants. Second, the implementation of a differentiated pricing approach does not necessarily equate to higher wholesale and retail prices in each period. Finally, to optimize overall profits and attain mutually beneficial outcomes, it is imperative for supply chain stakeholder to share decision‐making information. Furthermore, we offer managerial recommendations based on these findings. [ABSTRACT FROM AUTHOR]

We consider a two‐stage dynamic pricing problem for a seller who employs online group purchasing with strategic customers. Customers who arrive in Periods 1 and 2 can decide whether to purchase the product in the current period, delay their purchases to the next period (for those customers in Period 1), or leave the market. The network externality, a typical characteristic of group buying, is captured in our model. That is, more customers who purchase the product would positively or negatively affect the demand. We investigate the impacts of positive/negative externality on the seller's prices, demands, and profits, respectively. By comparing positive and negative externality cases, we find that customers who purchase the product in Period 2 can enjoy a lower price than in Period 1 for negative externality. Interestingly, however, the price in Period 1 might be higher or lower than in Period 2 for positive externality, even if the corresponding demand is lower. Compared to no externality, our result shows that the seller should reduce the price in Period 1 but increase it in Period 2 because this helps increase the total demand and profit in the presence of a positive externality. Nevertheless, when there exists a negative externality, we confirm that the prices in two periods might both decrease for a lower externality, which leads to lower total demand and profit for the seller. Since the seller can decide whether to disclose their reviews or product historical sales to customers, this result can provide practical implications for sellers who adopt a group purchasing strategy. [ABSTRACT FROM AUTHOR]

In the dynamic realm of the platform economy, the intricate relationship between platform charging mechanisms, seller tactics, and consumer behavior is crucial. This study delves into this relationship by constructing a two‐period dynamic game model, examining how different platform charging approaches impact pricing strategies among two distinct sellers, all while considering consumer strategic actions. Our findings reveal that the combination of seller quality differentiation and platform charging methods profoundly shapes consumer strategic actions. Interestingly, strategic consumers do not invariably harm seller profits. In specific settings, such as when using a revenue‐sharing model with slight quality differentiation, high‐quality sellers might even reap greater benefits than when dealing with short‐sighted consumers. Moreover, the decision for sellers to adhere to a specific price isn't always advantageous. For instance, under a revenue‐sharing model with limited competition, a price commitment from a lower quality seller might backfire. Additionally, in scenarios with a fixed‐fee model, there's a noticeable lack of incentive for differentiated sellers to commit to prices. This study accentuates the subtle yet significant influence of consumer strategic actions on seller profits in the platform economy context. It underscores the imperative for both sellers and platforms to remain vigilant and adaptive, ensuring their strategies resonate with the ever‐evolving consumer behaviors. [ABSTRACT FROM AUTHOR]

Approximately 55 percent of the world's 7.3 billion people access the Internet, creating a significant demand in information technology (IT) services for both organizations and consumers. The number of data centers continue to increase to meet this demand, consuming 2 percent of the energy produced worldwide. Many organizations moving towards cloud computing due to its ability to meet users' needs on demand. Greening cloud computing technology has become an important aspect of an organization's design and use of cloud computing. One aspect of greening the cloud is through efficiently using cloud-based resources. In this study, we focus on the resource allocation applications of cloud computing technologies to green an organizations cloud. We design a pricing and allocation mechanism for a private cloud computing service that allows the firm to effectively load balance their cloud computing resources. Our optimal pricing mechanism is a dynamic pricing model, which maximizes the net value of users in a private cloud computing service. Additionally, we create a job allocation algorithm based on our dynamic pricing model to load balance the cloud. We show that balancing the job allocation such that the number of jobs at individual resource servers as close to equal as possible is optimal. Furthermore, simulations were run on the allocation mechanism to examine the effects on the cloud resources while gaining insight to effectively distribution resources to public clouds. Our model can help organizations to efficiently distribute their cloud-based resources, which allows for a greener cloud computing system. [ABSTRACT FROM AUTHOR]

The integration of microgrids into the existing power system framework enhances the reliability and efficiency of the utility grid. This manuscript presents an innovative mathematical paradigm designed for the optimization of both the structural and operational aspects of a grid-connected microgrid, leveraging the principles of Demand-Side Management (DSM). The focus of this work lies in a comprehensive exploration of the implications brought about by the Renewable Generation-Based Dynamic Pricing Demand Response (RGDP-DR) mechanism, particularly in terms of its influence on the optimal microgrid configuration, considering perspectives from end-users and the utility entity. This inquiry is rooted in a holistic assessment that encompasses technical and economic performance benchmarks. The RGDP-induced DR framework adeptly addresses the needs of the consumer base, showcasing notable efficiency and economic feasibility. To address the intricate nonlinear optimization challenge at hand, we employ an evolutionary algorithm named the "Dandelion Algorithm" (DA). A rigorous comparative study is conducted to evaluate the efficacy of four optimization techniques, affirming the supremacy of the proposed DA. Within this discourse, the complexity of microgrid sizing is cast as a dual-objective optimization task. The twin objectives involve minimizing the aggregate annual outlay and reducing emissions. The results of this endeavor unequivocally endorse the superiority of the DA over its counterparts. The DA demonstrates exceptional proficiency in orchestrating the most cost-effective microgrid and consumer invoice, surpassing the performance of alternative optimization methodologies. [ABSTRACT FROM AUTHOR]

A system manager makes dynamic pricing and dispatch control decisions in a queueing network model motivated by ride hailing applications. A novel feature of the model is that it incorporates travel times. Unfortunately, this renders the exact analysis of the problem intractable. Therefore, we study this problem in the heavy traffic regime. Under the assumptions of complete resource pooling and common travel time and routing distributions, we solve the problem in closed form by analyzing the corresponding Bellman equation. Using this solution, we propose a policy for the queueing system and illustrate its effectiveness in a simulation study. [ABSTRACT FROM AUTHOR]

Considering the widening of the peak-valley difference in the power grid and the difficulty of the existing fixed time-of-use electricity price mechanism in meeting the energy demand of heterogeneous users at various moments or motivating users, the design of a reasonable dynamic pricing mechanism to actively engage users in demand response becomes imperative for power grid companies. For this purpose, a power grid-flexible load bilevel model is constructed based on dynamic pricing, where the leader is the dispatching center and the lower-level flexible load acts as the follower. Initially, an upper-level day-ahead dispatching model for the power grid is established, considering the lowest power grid dispatching cost as the objective function and incorporating the power grid-side constraints. Then, the lower level comprehensively considers the load characteristics of industrial load, energy storage, and data centers, and then establishes a lower-level flexible load operation model with the lowest user power-consuming cost as the objective function. Finally, the proposed method is validated using the IEEE-118 system, and the findings indicate that the dynamic pricing mechanism for peaking shaving and valley filling can effectively guide users to respond actively, thereby reducing the peak-valley difference and decreasing users' purchasing costs. [ABSTRACT FROM AUTHOR]

Tanzanian micro and small-scale fishery businesses struggle with limited market share and competitiveness due to ineffective marketing strategies. The purpose of the study was to investigate a blend of marketing strategies enhancing the competitiveness of micro and small-scale fishery businesses. The study utilized a mixed-method research design, combining quantitative and qualitative methodologies. Quantitative data, collected from 188 respondents through simple random sampling and structured questionnaires, underwent descriptive and regression analysis. Qualitative data derived from eight focus group discussions through purposive sampling, was subjected to thematic analysis. The quantitative study revealed a strong positive correlation between traditional marketing strategies and gaining a competitive edge (p<0.000). Also, the study found a similar strong positive correlation between modern marketing strategies and gaining a competitive edge (p< 0.000). The qualitative study revealed that traditional marketing strategies placed emphasis on customer interaction, flexible pricing, product quality, and efficient stock management, while modern approaches prioritized mobile communication, customer relationship management, and social media presence. The findings imply the crucial role of traditional marketing strategies in shaping Mwanza City's micro and small-scale fishery business, while stressing the increasing importance of integrating modern strategies with traditional approaches to ensure sustained competitiveness and success in the local market. The study unveils fresh insights by emphasizing the blending of traditional and modern marketing strategies for gaining a competitive edge in Tanzanian micro and small-scale fishery businesses. [ABSTRACT FROM AUTHOR]

Demand-side management (DSM) schemes play a crucial role in managing renewable energy generation and load fluctuations by utilizing demand–response programmes (DRPs). This paper aims to provide a detailed overview of DRPs that help microgrid operators to keep costs and reliability within acceptable ranges. Additionally, this review paper provides a detailed economic load model for DRPs based on initial load, demand–response (DR) incentive, DR penalty and elasticity coefficients. This article also aims to guide researchers in identifying research gaps in DSM applications in microgrids by comparing various DSM schemes from different countries and regions in terms of DSM strategies, objective functions and optimization techniques. Furthermore, this study analyses the impact of DRPs on microgrid configuration from the perspective of utilities and customers, considering technical and economic performance metrics. As a result, it can be concluded that none of the studied cases provides models or guidelines for choosing appropriate DSM schemes that consider different consumer interests or load-type features. Furthermore, a few researchers have addressed the features of a modern price-based DR strategy, renewable generation-based dynamic pricing DR, which offers higher customer satisfaction than traditional DRPs. [ABSTRACT FROM AUTHOR]

Resource pricing in cloud computing has become one of the main challenges for cloud providers. The challenge is determining a fair and appropriate price to satisfy users and resource providers. To establish a justifiable price, it is imperative to take into account the circumstances and requirements of both the provider and the user. This research tries to provide a pricing mechanism for cloud computing based on game theory. The suggested approach considers three aspects: the likelihood of faults, the interplay among virtual machines, and the amount of energy used, in order to determine a justifiable price. In the game that is being proposed, the provider is responsible for determining the price of the virtual machine that can be made available to the user on each physical machine. The user, on the other hand, has the authority to choose between the virtual machines that are offered in order to run their application. The whole game is implemented as a function of the resource broker component. The proposed mechanism is simulated and evaluated using the CloudSim simulator. Its performance is compared with several previous recent mechanisms. The results indicate that the suggested mechanism has successfully identified a more rational price for both the user and the provider, consequently enhancing the overall profitability of the cloud system. [ABSTRACT FROM AUTHOR]

Despite its potential, the application of dynamic pricing (DP) remains the exception rather than the rule in car parks across Europe. This paper conceptualizes DP as a practice and leverages mechanisms of practice diffusion and organizational implementation to explain its slow diffusion. Among European car park operators the lack of technical and cultural fit of DP is identified as one of the main reasons for the slow diffusion process. It appears that more time is needed for an acceleration of the diffusion process as data management improves and prevalence of increasing digital solutions in a rather analogue industry become more accepted. As parking customers will eventually become more susceptible to digital solutions, more market participants are likely to adopt DP as the driving pricing scheme. [ABSTRACT FROM AUTHOR]

We present a directed search model of intermediaries' dynamic inventory and revenue management. Search frictions hinder instantaneous replenishment, prompting intermediaries to utilize dynamic inventory‐based pricing and ordering strategies. In equilibrium, when inventory is high, an intermediary posts a lower retail price to speed up sales and depresses wholesale price to slow down purchases. We characterize the evolution dynamics of inventory holdings and their steady‐state distribution, and extend the model to multiunit wholesale orders, product differentiation, and heterogeneous intermediaries. Using data from used‐car dealers, we quantitatively evaluate the welfare consequence of used‐car dealers' inventory management practice. [ABSTRACT FROM AUTHOR]

This study examines a pricing model spanning two periods, which incorporates social learning and herding effect, while acknowledging the potential for quality uncertainty to diminish consumers' willingness to pay for a product. The analysis focuses on a monopolistic firm that offers a new experience product over two selling periods, and evaluates the efficacy of price commitment versus dynamic pricing strategies in maximizing profit. The findings indicate that the optimal pricing strategy is contingent upon the interplay of social learning, herding effect, and the degree of consumer uncertainty regarding product quality. Our study reveals that positive social learning can be advantageous for firms. However, contrary to conventional wisdom, negative social learning does not necessarily have a detrimental impact on firms. This is due to the combined effects of the markdown loss and demand uplift. Additionally, our findings indicate that the herding effect can be beneficial for firms that adopt a price commitment strategy, but this may not always hold true for those who opt for dynamic pricing strategy. Our research offers valuable managerial insights for firms seeking to effectively manage the social learning and herding effect in their pricing strategies. [ABSTRACT FROM AUTHOR]

Contextual dynamic pricing aims to set personalized prices based on sequential interactions with customers. At each time period, a customer who is interested in purchasing a product comes to the platform. The customer's valuation for the product is a linear function of contexts, including product and customer features, plus some random market noise. The seller does not observe the customer's true valuation, but instead needs to learn the valuation by leveraging contextual information and historic binary purchase feedback. Existing models typically assume full or partial knowledge of the random noise distribution. In this paper, we consider contextual dynamic pricing with unknown random noise in the linear valuation model. Our distribution-free pricing policy learns both the contextual function and the market noise simultaneously. A key ingredient of our method is a novel perturbed linear bandit framework, in which a modified linear upper confidence bound algorithm is proposed to balance the exploration of market noise and the exploitation of the current knowledge for better pricing. We establish the regret upper bound and a matching lower bound of our policy in the perturbed linear bandit framework and prove a sublinear regret bound in the considered pricing problem. Finally, we demonstrate the superior performance of our policy on simulations and a real-life auto loan data set. Funding: Y. Liu and W.W. Sun acknowledge support from the National Science Foundation Division of Social and Economic Sciences [Grant NSF-SES 2217440]. Supplemental Material: The supplementary material is available at https://doi.org/10.1287/moor.2023.1369. [ABSTRACT FROM AUTHOR]

We consider a classic joint pricing and inventory control problem with lead times, which is extensively studied in the literature but is notoriously difficult to solve because of the complex structure of the optimal policy. In this work, rather than analyzing the optimal policy, we propose a class of constant-order dynamic pricing policies, which are fundamentally different from base-stock list price policies, the primary emphasis in the existing literature. Under such a policy, a constant-order amount of new inventory is ordered every period, and a pricing decision is made based on the inventory level. The policy is independent of the lead time. We prove that the best constant-order dynamic pricing policy is asymptotically optimal as the lead time grows large, which is exactly the setting in which the problem becomes computationally intractable because of the curse of dimensionality. As our main methodological contributions, we establish the convergence to a long-run average random yield inventory model with zero lead time and ordering capacities by its discounted counterpart as the discount factor goes to one, nontrivially extending the previous results in Federgruen and Yang that analyze a similar model but without capacity constraints. Funding: Research of X. Chen and L. Xin was partly supported by the National Science Foundation [Grant CMMI-1635160]. [ABSTRACT FROM AUTHOR]

Purpose: The rapid ascent of digital technology has consistently altered the landscape of traditional marketing, presenting both novel challenges and opportunities. This review endeavors to dissect and elucidate the intricate relationship between Web-based Augmented Reality (WebAR) and the cornerstone of marketing strategy: the 4Ps 3 Product, Price, Place, and Promotion. Design/methodology/approach: Drawing on a comprehensive analysis of existing scholarly works, industry reports, and case studies, the article illuminates the current manifestations of WebAR within the 4Ps framework, demonstrating its transformative capacity in areas such as product visualization, dynamic pricing displays, virtual storefronts, and augmented promotional campaigns. Findings: Through the synthesis, the article underscores the imperative for marketers to understand, adapt, and innovate in the face of an increasingly augmented digital landscape, even in the absence of quantitative metrics. Originality/value: The findings serve as both a foundational resource for scholars and a strategic guide for practitioners seeking to navigate the confluence of WebAR and modern marketing. [ABSTRACT FROM AUTHOR]

Effective public transportation pricing strategies are critical to reducing traffic congestion and meeting consumer demand for sustainable urban development. In this study, we construct a dynamic game pricing model and a social learning network model for consumers of three modes of public transportation including metro, bus, and pa-transit. In the model, the metro, bus, and pa-transit operators maximize their profits through dynamic pricing optimization, and consumers maximize their utility by adjusting their travel habits through social learning in the social network. The reinforcement learning algorithm is applied to simulate the model, and the results show that: (1) as consumers' perceived sensitivity to different modes of travel increases, the market share and price of each mode of travel adjust accordingly. (2) When taking into account consumers' social learning behavior, the market share of metros remains high, while the market shares of buses and pa-transit are relatively low. (3) As consumers become more sensitive to their perception of each travel mode, operators invest more resources in improving service quality to gain market share, which in turn affects the price of each travel mode. Our results provide decision support for optimal pricing of urban public transportation. [ABSTRACT FROM AUTHOR]

Data centers are characterized by high energy consumption, with operating costs being extremely sensitive to electricity prices. Therefore, modern data centers are often equipped with microgrids for power supply, which adjust their operational strategies based on the given electricity prices to minimize costs. However, existing research has overlooked the flexible pricing potential of electricity retailers, prompting this study to propose a non-cooperative game theory- based optimization method for data center electricity procurement negotiation. A two-layer optimization model is established for data center electricity pricing. The upper layer focuses on electricity price optimization, modeling price negotiation as a Stackelberg game and adopting a weighted average cost approach to describe the electricity procurement prices. The lower layer addresses data center operational optimization, formulated as a nonlinear programming problem. To enable rapid solution convergence, a hybrid problemsolving method combining a genetic algorithm and branch-and-cut algorithm is proposed. Finally, a simulation is conducted using a data center located in California, United States, to validate the proposed method. The results demonstrate that the proposed method can reduce data center operational costs by 3% and increase the revenue for electricity retailers by 17%, achieving a win-win outcome. [ABSTRACT FROM AUTHOR]

More and more microgrids, energy storage systems, and other emerging entities are integrated into active distribution networks. However, a microgrid is characterized by autonomous operation and privacy protection. The rapid development of equipment such as shared energy storage brings strong uncertainty to a traditional dispatcher. The observability and controllability of the distribution system decrease, and traditional regulatory methods are no longer applicable. To deal with the above challenges, a distributed collaborative operation optimization method of multilateral participants is proposed. Guided by the dynamic time-of-use electricity price, the collaborative operation of multilateral participants can be realized. Firstly, the cooperative operation architecture is established considering the dynamic time-of-use electricity price. In this architecture, the residual capacity of shared energy storage is used for arbitrage by storing electricity at low electricity prices and generating electricity at high electricity prices. Then, the optimization operation models of a microgrid alliance, shared energy storage, and an active distribution network are established. The final operation scheme and the dynamic time-of-use price of the distribution network are formulated through the cyclic iteration among the three participants. Finally, a case study is carried out to analyze the optimization effect of each participant with the proposed method. It is found that the overall interests and the interests of each participant can be taken into account effectively and the consumption of renewable energy can be promoted by the method proposed in the paper. In addition, an oscillation phenomenon is found during the distributed collaborative operation, and the strategy to eliminate the oscillation phenomenon is given. [ABSTRACT FROM AUTHOR]

We investigate the dynamic price discovery in the regular bitcoin (BTC) and microbitcoin (MBT) futures at the Chicago Mercantile Exchange. The only difference between the two bitcoin futures is the contract size, with MBT representing 1/50th of BTC. In contrast to recent findings in the literature, we find that BTC dominates MBT futures in price discovery, which can be attributed to the relative liquidity and investor structure in the BTC and MBT futures. In addition, crypto hacking activities can affect price discovery in bitcoin futures as we find higher hack stolen funds reduce (enhance) the price discovery in BTC (MBT) futures. These findings provide practical implications for bitcoin investors and regulators. [ABSTRACT FROM AUTHOR]