scholarly journals Dynamic pricing with finite price sets: a non-parametric approach

2021 ◽  
Author(s):  
Athanassios N. Avramidis ◽  
Arnoud V. den Boer
Keyword(s):  
Dynamic Pricing ◽  
Demand Uncertainty ◽  
Numerical Study ◽  
Dynamic Program ◽  
Unknown Parameters ◽  
Perishable Inventory ◽  
Price Optimization ◽  
Time Period ◽  
Dependent Parameter ◽  
Expected Revenue

AbstractWe study price optimization of perishable inventory over multiple, consecutive selling seasons in the presence of demand uncertainty. Each selling season consists of a finite number of discrete time periods, and demand per time period is Bernoulli distributed with price-dependent parameter. The set of feasible prices is finite, and the expected demand corresponding to each price is unknown to the seller, whose objective is to maximize cumulative expected revenue. We propose an algorithm that estimates the unknown parameters in a learning phase, and in each subsequent season applies a policy determined as the solution to a sample dynamic program, which modifies the underlying dynamic program by replacing the unknown parameters by the estimate. Revenue performance is measured by the regret: the expected revenue loss relative to the optimal attainable revenue under full information. For a given number of seasons n, we show that if the number of seasons allocated to learning is asymptotic to $$(n^2\log n)^{1/3}$$ ( n 2 log n ) 1 / 3 , then the regret is of the same order, uniformly over all unknown demand parameters. An extensive numerical study that compares our algorithm to six benchmarks adapted from the literature demonstrates the effectiveness of our approach.

OPTIMAL PRICING AND PRODUCTION POLICIES OF A MAKE-TO-STOCK SYSTEM WITH FLUCTUATING DEMAND

2009 ◽  
Vol 23 (2) ◽  
pp. 205-230 ◽  
Author(s):  
Jean-Philippe Gayon ◽  
Işılay Talay-Değirmenci ◽  
Fikri Karaesmen ◽  
E. Lerzan Örmeci
Keyword(s):  
Dynamic Pricing ◽  
Numerical Study ◽  
Optimal Pricing ◽  
Pricing Strategies ◽  
Pricing Policy ◽  
Stock Level ◽  
Production Inventory ◽  
Static Pricing ◽  
Make To Stock ◽  
Production Inventory System

We study the effects of different pricing strategies available to a production–inventory system with capacitated supply, which operates in a fluctuating demand environment. The demand depends on the environment and on the offered price. For such systems, three plausible pricing strategies are investigated: static pricing, for which only one price is used at all times, environment-dependent pricing, for which price changes with the environment, and dynamic pricing, for which price depends on both the current environment and the stock level. The objective is to find an optimal replenishment and pricing policy under each of these strategies. This article presents some structural properties of optimal replenishment policies and a numerical study that compares the performances of these three pricing strategies.

scholarly journals An Application of Reinforced Learning-Based Dynamic Pricing for Improvement of Ridesharing Platform Service in Seoul

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1818
Author(s):  
Jaein Song ◽  
Yun Ji Cho ◽  
Min Hee Kang ◽  
Kee Yeon Hwang
Keyword(s):  
Reinforcement Learning ◽  
Waiting Time ◽  
Dynamic Pricing ◽  
Learning Algorithm ◽  
Residential Areas ◽  
Private Companies ◽  
Reward Function ◽  
Time Period ◽  
Reinforced Learning ◽  
Centrality Analysis

As ridesharing services (including taxi) are often run by private companies, profitability is the top priority in operation. This leads to an increase in the driver’s refusal to take passengers to areas with low demand where they will have difficulties finding subsequent passengers, causing problems such as an extended waiting time when hailing a vehicle for passengers bound for these regions. The study used Seoul’s taxi data to find appropriate surge rates of ridesharing services between 10:00 p.m. and 4:00 a.m. by region using a reinforcement learning algorithm to resolve this problem during the worst time period. In reinforcement learning, the outcome of centrality analysis was applied as a weight affecting drivers’ destination choice probability. Furthermore, the reward function used in the learning was adjusted according to whether the passenger waiting time value was applied or not. The profit was used for reward value. By using a negative reward for the passenger waiting time, the study was able to identify a more appropriate surge level. Across the region, the surge averaged a value of 1.6. To be more specific, those located on the outskirts of the city and in residential areas showed a higher surge, while central areas had a lower surge. Due to this different surge, a driver’s refusal to take passengers can be lessened and the passenger waiting time can be shortened. The supply of ridesharing services in low-demand regions can be increased by as much as 7.5%, allowing regional equity problems related to ridesharing services in Seoul to be reduced to a greater extent.

Dynamic pricing for online hotel demand: The case of resort hotels in Majorca

2019 ◽  
Vol 26 (2) ◽  
pp. 268-283 ◽  
Author(s):  
Aldric Vives ◽  
Marta Jacob
Keyword(s):  
Dynamic Pricing ◽  
Dynamic Models ◽  
Customer Behavior ◽  
Optimal Pricing ◽  
Stochastic Dynamic ◽  
Elastic Demand ◽  
Demand Functions ◽  
Elasticity Of Demand ◽  
Price Optimization ◽  
Resort Hotels

Online customer behavior in terms of price elasticity of demand and the effect of time along the booking horizon are key requirements for the price optimization process that allows hotels to maximize their revenues. In this vein, this study adapts the online transient hotel demand functions to deterministic and stochastic dynamic models—two extended optimal pricing methods existing in the literature—in order to determine the prices that maximize the revenues of two resort hotels located in Majorca. The main findings indicate that (1) seasonality, the number of rooms available, the hotel location, and the tourist profile affect dynamic pricing (DP); (2) the booking horizon limitation leads to larger revenue decreases under elastic demand; (3) higher levels in demand elasticities generally produce lower levels of prices; and (4) the distribution of elasticities across the booking horizon and the natural variability of demand have an impact on DP. Implication for industry revenue managers is that they have to consider the booking horizon duration together with the demand price sensitivity in order to maximize the hotel revenues.

scholarly journals Dynamic Pricing in a Multi-Period Newsvendor Under Stochastic Price-Dependent Demand

Mathematics ◽  
2019 ◽  
Vol 7 (6) ◽  
pp. 520 ◽  
Author(s):  
Mehran Ullah ◽  
Irfanullah Khan ◽  
Biswajit Sarkar
Keyword(s):  
Dynamic Pricing ◽  
Demand Uncertainty ◽  
New Technologies ◽  
Rapid Development ◽  
Life Cycles ◽  
Selling Price ◽  
Market Demand ◽  
Newsvendor Model ◽  
Pricing Policy ◽  
Dependent Demand

The faster growth of technology stipulates the rapid development of new products; with the spread of new technologies old ones are outdated and their market demand declines sharply. The combined impact of demand uncertainty and short life-cycles complicate ordering and pricing decision of retailers that leads to a decrease in the profit. This study deals with the joint inventory and dynamic pricing policy for such products considering stochastic price-dependent demand. The aim is to develop a discount policy that enables the retailer to order more at the start of the selling season thus increase the profit and market share of the retailer. A multi-period newsvendor model is developed under the distribution-free approach and the optimal stocking quantities, unit selling price, and the discount percentage are obtained. The results show that the proposed discount policy increases the expected profit of the system. Additionally, the stocking quantity and the unit selling price also increases in the proposed discount policy. The robustness of the proposed model is illustrated with numerical examples and sensitivity analysis. Managerial insights are given to extract significant insights for the newsvendor model with discount policy.

scholarly journals EFFECT OF SALVAGE MARKET ON STRATEGIC TECHNOLOGY CHOICE AND CAPACITY INVESTMENT DECISION OF FIRM UNDER DEMAND UNCERTAINTY

2016 ◽  
Vol 17 (1) ◽  
pp. 140-155 ◽  
Author(s):  
Mohammad Ali KASHEFI
Keyword(s):  
Demand Uncertainty ◽  
Numerical Study ◽  
Investment Decision ◽  
Secondary Market ◽  
Theoretic Model ◽  
Technology Choice ◽  
Capacity Investment ◽  
Flexible Technology ◽  
Game Theoretic ◽  
Unique Nash Equilibrium

This paper examines the effect of salvage market on technology choice and capacity investment decision of two firms that compete on quantity under demand uncertainty. A game theoretic model applies such that firms choose their production technology between two alternatives: flexible versus inflexible production process. Then they decide on the amount of capacity investment: flexible firm makes decision about general and specific components and inflexible firm just about unified component. One stage forward both enter the primary market in which demand is uncertain and play a la Cournot and finally, flexible firm will be able to sell its unsold general components in the secondary market with a deterministic price. Numerical study was employed to observe equilibrium behavior of firms. Findings demonstrate that with symmetric parameterization there is a unique Nash equilibrium in which both firms choose inflexible technology while applying asymmetric parameters has the potential to form two types of equilibrium when both firms choose inflexible technology or only one firm chooses flexible technology. Moreover, it is shown that there is a cost threshold that could shift the equilibria.

scholarly journals On Bayesian estimation of step stress accelerated life testing for exponentiated Lomax distribution based on censored samples

2020 ◽  
pp. 239-248
Author(s):  
Refah Mohamed Alotaibi ◽  
Hoda Ragab Rezk
Keyword(s):  
Numerical Study ◽  
Real Data ◽  
Operating Conditions ◽  
Stress Factors ◽  
Cumulative Exposure ◽  
Exposure Model ◽  
Life Testing ◽  
Unknown Parameters ◽  
Accelerated Life ◽  
Credible Intervals

In reliability analysis and life-testing experiments, the researcher is often interested in the effects of changing stress factors such as “temperature”, “voltage” and “load” on the lifetimes of the units. Step-stress (SS) test, which is a special class from the well-known accelerated life-tests, allows the experimenter to increase the stress levels at some constant times to obtain information on the unknown parameters of the life models more speedily than under usual operating conditions. In this paper, a simple SS model from the exponentiated Lomax (ExpLx) distribution when there is time limitation on the duration of the experiment is considered. Bayesian estimates of the parameters assuming a cumulative exposure model with lifetimes being ExpLx distribution are resultant using Markov chain Monte Carlo (M.C.M.C) procedures. Also, the credible intervals and predicted values of the scale parameter, reliability and hazard are derived. Finally, the numerical study and real data are presented to illustrate the proposed study

Effect of Aspect Ratio on High Rise Structures with Diagrid

2021 ◽  
Vol 6 (3) ◽  
Author(s):  
A. Vimala ◽  
A. Vimala
Keyword(s):  
Aspect Ratio ◽  
Numerical Study ◽  
Percentage Increase ◽  
Base Shear ◽  
High Rise ◽  
Aspect Ratios ◽  
Drift Ratio ◽  
Time Period ◽  
Diagrid Structure ◽  
Building Behaviour

Urbanization and population explosion in the present times has led to increase in demand for land and residencies but the availability of land is scare i.e reason a trend has evolved for construction of high rise structures in high rise structures major emphasis given to lateral load resisting systems. As diagrid structural system is lighter, stiffer and is effective in resisting the lateral loads, the present investigation carried out to study the performance diagrid on high rise structures varying aspect ratio. The study is carried out to observe the performance of diagrid structures ranging from 30 to 90 storeys. Diagrid structures are modelled with 3 storey module and performance of 7 models with different storeys i.e 30, 40, 50, 60, 70, 80, 90 (aspect ratio 3.67-10.86) and with fixed plan area. As a part 1 investigation to optimise the diagrid angle a 30 storey Diagrid structure performance is studied with 4 different diagrid angles one storey module angle 35°45’, Two storey module angle 55°13’, Three storey module 65°9’, Four storey module 70°51’. The optimized diagrid angle is used for different aspect ratio high rise structures to investigate the performance in terms of Storey displacement, Storey drift ratio, base shear and time period. For all the models plan area is fixed. Second part of investigation was a numerical study carried out by utilizing identified optimum angle of diagrid is applied on high rise buildings with aspect ratios 3.67, 4.86, 6.06, 7.26, 8.46, 9.67, 10.86 (Aspect ratio is the total height of the building to the width of the building). Behaviour of the Diagrid buildings due to change in aspect ratio is analysed based on parameters such as Storey displacements, Storey drift ratio, Base shear, Time period. As a part of investigation parameters such as Storey displacements and storey drift ratio were evaluated if they were within the limits as per IS code provisions. Percentage increase in storey displacements, maximum storey drift ratio,

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