Optimal Price Strategy under Price-Matching Policy

The paper explores the optimal price strategy under the price-matching policy. First, the paper formulates the demand function under the price match policy and then discovers the retailer’s best response facing the price-matching pressure. From the theoretical analysis, we discover how the number of retailers plays an important role during the competition. When only two retailers are involved, the final prices may not converge to a single value. However, when more retailers are involved, the final price will converge to a single value. We also use numerical studies to illuminate the change of the prices over the time period, the sensitivity of the final price to the increment/decrement of initial prices. Finally, we provide managerial suggestions to both producers and retailers.

Modified Ramsey Rule and Optimal Carbon Tax

The Ramsey rule is regarded as a convenient vehicle for estimating the social discount rate in general. Carbon pricing is treated as another theory of environmental economics. This study clarifies the theoretical relationship between the Ramsey rule and optimal carbon price, which has been overlooked in the existing research. It succeeds in deriving the optimal carbon price from the modified Ramsey rule in stationary state. Since the Ramsey rule decides the dynamics of an economy and a stationary state is its destination, by using the optimization condition of individual who are assumed to live infinitesimally short life, we can solve the optimal carbon price at stationary state.

A NUMERICAL METHOD FOR DETERMINING THE OPTIMAL EXERCISE PRICE TO AMERICAN OPTIONS

American options can be exercised prior to the date of expiration, the valuation of American options then constitutes a free boundary value problem. How to determine the free boundary, i.e. the optimal exercise price, is a key problem. In this paper, a nonlinear equation is given. The free boundary can be obtained by solving the nonlinear equation and the numerical results are better.

Study on Demand Response of Residential Power Customer

In order to optimize the ladder-pricing scheme in Shanghai, we present a multi-objective optimization model (MOOM). To build this model, first we use price elasticity theory;divide the ladder pricing into peak electricity bill and valley electricity bill in the time dimension to model the single-user demand response. Second based on the single-user demand response model, combined with the overall users’ electricity distribution density function, we build an all-users demand response model. The proposed model has two objectives: minimize energy consumption and maximize residents’ satisfaction. Simulation results confirm that the proposed model can optimize the ladder-pricing scheme.

Centralized Decisions for a Two-Stage Supply Chain with Price-Discount Dependent Demand

In this paper,we study a centralized supply chain for a two-stage with selling price discount.This supply chain consists of a supplier and a retailer. Based on the feature that the product’s selling season is short and the supply chain faces great demand uncertainty. We consider a two-stage scenario where,at the beginning of stage 1,the supplier reserves production capacity based on historic data in advance,stage 2 comes to us after some leadtime,both the supplier and the retailer update the demand information,the retailer then places an order not exceeding the reserved capacity based on the selling-pricing discount dependent demand. We make optimal decisions on the reserved capacity in stage 1,selling price discount and order quantity in stage 2. In this supply chain,the pattern in stage2 is figured out first,and then stage 1 is cleared as well. Then we present a numerical example to give some insights. Finally we get some conclusions.

FALSE FAILURE RETURNS： OPTIMAL PRICING AND RETURN POLICIES IN A DUAL-CHANNEL SUPPLY CHAIN

This paper aim is to examine the optimal pricing and return policies for false failure returns in a dual-channel supply chain. Four prevailing return policies in which a manufacturer both operates an E-shop and sells its product through a brick-and-mortar retailer are analyzed, i.e. （I） the manufacturer handlings E-shop＇s returns, while the retailer addresses brick-and-mortar store＇s returns （NR）; （II） the retailer tackles the whole （both E-shop＇s and brick-and-mortar store＇s） returns （ORR）; （III） the manufacturer tackles the whole returns （ORM）; and （IV） the manufacturer and the retailer are jointly responsible for the whole returns （RRM）. Firstly, the optimal pricing and return policies comparing these four scenarios under uniform-pricing strategy are presented. Our conclusions show that the ORR is an optimal return policy. Compared with the NR, consumers will get a lower product pricing under the ORR and a higher product pricing under the ORM. With regard to the RRM, the product pricing is depended on consumer preference, return-rates of the E-shop and the brick-and-mortar store. Then, the optimal pricing and return policies are analyzed under differential-pricing strategy by conducting two-stage sequential games between the manufacturer and the retailer. The findings show that if consumers in the market prefer to purchase via the E-shop, the ORR is an optimal return policy. Otherwise, the NR is the optimal return policy. Compared with the NR, the ORR retailer＇s product pricing will rely on the retailer＇s and the manufacturer＇s return-costs; the RRM retailer＇s product pricing will depend on the return-costs of the retailer and the manufacturer, the return-rates of the E-shop and the brick-and-mortar store and so on. Finally, the influences of the manufacturer and the retailer establishing a Buy-back contract are discussed. Our results illustrated that the Buy-back contract doesn＇t affect optimal pricing and return policies under both the uniform and the differential pricing strategies.

Exploring price effects on the residential water conservation technology diffusion process： a case study of Tianjin city

Reforms of the water pricing management system and the establishment of a flexible water pricing system are significant for cities in northern China to tackle their critical water issues. The WATAP （Water conserva- tion Technology Adoption Processes） model is developed in order to capture the water conservation technology adoption process under different price scenarios with disaggregate water demands down to the end use level. This model is explicitly characterized by the technological selection process under maximum marginal benefit assumption by different categories of households. In particular, when households need to purchase water devices in the provision market with the consideration of complex factors such as the life span, investment and operating costs of the device, as well as the regulated water price by the government. Applied to Tianjin city, four scenarios of water price evolutions for a long-term perspective （from year 2011 to 2030） are considered, including BAU （Business As Usual）, SP1 （Scenario of Price increase with constant annual rate）, SP2 （Scenario of Price increase every four years） and SP3 （Scenario of Price increase with affordable constraint）, considering many factors such as historic trends, affordability and incentives for conservation. Results show that on aggregate 2.3%, 11.0% and 18.2% of fresh water can be saved in the residential sector in scenario SP1, SP2 and SP3, respec- tively, compared with the BAU scenario in the year 2030. The water price signals can change the market shares of different water appliances, as well as the water end use structure of households, and ultimately improve water use efficiency. The WATAP model may potentially be a helpful tool to provide insights for policy makers on water conservation technology policy analysis and assessment.

Improved Generative Adversarial Behavioral Learning Method for Demand Response and Its Application in Hourly Electricity Price Optimization

In response to the imbalance between power generation and demand, demand response(DR) projects are vigorously promoted. However, customers’ DR behaviors are still difficult to be simulated accurately and objectively. To tackle this challenge, we propose a new DR behavioral learning method based on a generative adversary network to learn customers’ DR habits. The proposed method is also extended to maximize the economic revenues of generated DR policies on the premise of obeying customers’ DR habits, which is hard to be realized simultaneously by existing model-based methods and traditional learning-based methods. To further consider customers’ timevarying DR patterns and trace the changes dynamically, we define customers’ DR participation positivity as an indicator of their DR pattern and propose a condition regulation approach improving the natural generative adversary framework to generate DR policies conforming to customers’ current DR patterns. The proposed method is applied to hourly electricity price optimization to reduce the fluctuation of system aggregate loads. An online parameter updating method is also utilized to train the proposed behavioral learning model in continuous DR simulations during electricity price optimization. Finally, numerical simulations are conducted to verify the effectiveness and superiority of the proposed method.

SIMULATION OF INVENTORY POLICY FOR PRODUCT WITH PRICE AND TIME-DEPENDENT DEMAND FOR DETERIORATING ITEM

In a declining market for goods,we optimize the net profit in business when inventory management allows change in the selling prices n times over time horizon.We are computing optimal number of changes in prices,respective optimal prices,and optimal profit in each of the cycle for a deteriorating product.This paper theoretically proves that for any business setup there exists an optimal number of price settings for obtaining maximum profit.Theoretical results are supported by numerical examples for different setups(data set)and it is found that for every setup the dynamic pricing policy out-performs the static pricing policy.In our model,the deterioration factor has been taken into consideration.The deteriorated units are determined by the recurrence method.Also we studied the effect of different parameters on optimal policy with simulation.For managerial purposes,we have provided some“suggested intervals”for choosing parameters depending upon initial demand,which help to predict the best prices and arrival of customers(demand).

Game-theoretical Models of a Two-echelon Supply Chain Involving Two Substitutable Products

This paper considers tripartite pricing issues in a two-echelon supply chain involving duopolistic manufacturers and a single retailer.Firstly,a tripartite competitive model is conducted,in which both a Stackelberg game and a Bertrand game occur simultaneously.It is shown that the manufacturer who possesses a higher sales quantity gains more profits than the other one.Secondly,a definition of optimal vertical pricing alliance is proposed when cooperation exists between the retailer and some manufacturer.We conduct two-player games when partial cooperation exists among the three participants.It is demonstrated that the total profit of the alliance is higher than the sum profit of the corresponding two participants in the tripartite competition model,and meanwhile the profit of the manufacturer who is not in the alliance suffers a loss.Further,a criterion is given to judge which manufacturer the retailer will choose to cooperate in order to maximize the total increased profits.From the perspective of game theory,we examine the stability of the vertical alliance with considering the dominance of the retailer.Finally,a numerical illustration is designed to examine the judging criteria of which manufacturer is the member of the optimal alliance under different potential market demands.

Critical peak rebate strategy and application to demand response

Time-of-use (TOU) pricing strategy is an important component of demand-side management (DSM), but the cost of supplying power during critical peak periods remains high under TOU prices. This affects power system reliability. In addition, TOU prices are usually applicable to medium- and long-term load control but cannot effectively regulate short-term loads. Therefore, this paper proposes an optimization method for TOU pricing and changes the electricity consumption patterns during the critical peak periods through a critical peak rebate (CPR). This reduces generation costs and improves power system reliability. An optimization model for peak-flat-valley (PFV) period partition is established based on fuzzy clustering and an enumeration iterative technique. A TOU pricing optimization model including grid-side and customer-side benefits is then proposed, and a simulated annealing particle swarm optimization (SAPSO) algorithm is used to solve the problem. Finally, a CPR decision model is developed to further reduce critical peak loads. The effectiveness of the proposed model and algorithm is illustrated through different case studies of the Roy Billinton Test System (RBTS).

Analysis of a Joint Pricing and Seat Allocation Model in a Hub-to-Hub Airline Network

In airline revenue management,after differentiating products on each itinerary according to various restrictions,management needs to set the prices for the products.A deterministic joint pricing and seat allocation model is proposed.It is reduced to a separable concave programming problem and thus readily solvable.Focusing on a special hub-to-hub airline network,monotonicity of the pricing decisions is explored.Using a nonlinear primal–dual technique,it is shown that some itineraries’optimal prices are decreasing,while some other itineraries’optimal prices are increasing with a capacity change in either a side leg or the middle leg.Such structural properties add important managerial insights into the pricing model for revenue managers in an airline company.

PRICING FOR CORPORATE PROPERTY RIGHTS TRANSFER WITH STOCHASTIC MARKETS

This paper employs the real option theory to develop a pricing model for the transfer of property rights.We list the conditions for the good,intermediate and bad firms respectively,and work out the closed-form solution to the equilibrium transfer price,the optimal transfer timing.Using the comparative static analysis,we find that for good firms the transfer price of the target is increasing in its capital.The higher the capital of the target owns,the faster it will be transferred.For intermediate and bad firms,similar conclusions can be derived.The larger gap between the acquirer＇s size and market power and those of the target,the lower the transfer triggered price.The transfer price goes up as the capital ratio of the acquirer over the target diminishes,while it is decreasing in the amount of the capital the target owns.

Application and evaluation of payment channel in hybrid decentralized ethereum token exchange

Traditional centralized token exchange(CEX)has been suffering from hacking due to the centralized management of users’tokens.In contrast,decentralized token exchange(DEX)maintains users’assets by smart contracts in a decentralized manner,but introduces additional overhead in terms of gas fee and transaction confirmation latency.Hybrid decentralized token exchange(HEX)has been proposed to combine the benefits of CEX and DEX.However,existing HEX is criticized for two issues.First,trading transactions are time-consuming and expensive for frequent token traders.Second,excessive simultaneous transactions might cause the pending transaction congestion in the Ethereum network.In this paper,we propose a payment channel based HEX,which extends existing solutions by adding a new payment channel layer to benefit frequent traders and alleviate the pending transaction congestion.Besides,we propose the very first gas-price vs.transactionconfirmation-latency function to guide Ethereum transaction issuers to choose an optimal gas price that minimizes the overall cost.Extensive simulations are conducted to compare the cost in the proposed HEX with that in the conventional HEX.The results demonstrate the effectiveness of our proposed mechanism in terms of reducing gas fees and transaction confirmation latency for frequent traders as well as the pending transaction congestion in Ethereum.