mahdi ghaemiasl; Sadegh Bafandeh Imandoust; Elham Dashti
Abstract
Underestimation of this high-demand services in today's world has resulted in the non-optimal allocation of resources and incorrect management and planning. In this research, focusing on Chalidareh Tourism Complex in Mashhad, a finite-horizon bayesian dynamic pricing model has been used to determine ...
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Underestimation of this high-demand services in today's world has resulted in the non-optimal allocation of resources and incorrect management and planning. In this research, focusing on Chalidareh Tourism Complex in Mashhad, a finite-horizon bayesian dynamic pricing model has been used to determine the extent of willingness to pay for non-market regional natural resources. In so doing, based on Chen and Wu (2016), Gamma and Two-Point priors with exponential and normal WTP (Willingness to Pay) distribution have been used. The results showed that the average WTP for general exploitation of this complex is within the extent of 12230 IRR (as minimum) in Gamma prior and exponential distribution and 45270 IRR (as maximum) in the Two-Point prior and exponential distribution. Also, the average of WTP is 28750 IRR, while the WTP is 10623 IRR in non-Bayesian approach, which is lower than any of Bayesian estimations. Therefore, the application of Finite-Horizon Bayesian Dynamic Pricing (FHBD) algorithm in dynamic pricing can be an appropriate way to determine the threshold amount of WTP for the exploitation of natural resources.
Introduction
An important insight from the literature on dynamic pricing is that the optimal selling price of such products depends on the remaining inventory and the length of the remaining selling season (see e.g., Gallego & Van Ryzin, 1994). The optimal decision is, thus, not to use a single price but a collection of prices: one for each combination of the remaining inventory and the length of the remaining selling season. To determine these optimal prices it is essential to know the relation between the demand and the selling price. In most literature from the 1990s on dynamic pricing, it is assumed that this relation is known to the seller, but in practice, the exact information on the consumer behavior is generally not available. It is, therefore, not surprising that the review on dynamic pricing by Bitran and Caldentey (2003) mentions dynamic pricing with demand learning as an important future research direction. The presence of digital sales data enables a data-driven approach of dynamic pricing, where the selling firm not only determines optimal prices, but also learns how changing prices affects the demand. Ideally, this learning will eventually lead to optimal pricing decisions.
Theoretical Framework
In this paper, we focus on the dynamic pricing problem of selling a limited amount of inventory over a short selling horizon. In this regard, dedicating a certain number of periods for exploratory experimenting may be costly due to the limited time and inventory. Instead, a simultaneous optimization of pricing and learning is desired, which can be achieved by formulating the problem as a Bayesian dynamic program. However, computing the optimal policy for the dynamic program can be difficult, if not intractable due to the high dimensionality. Moreover, the binary customer choice model described above gives a rise to a two-sided censoring effect, that is, the observation of the customer’s WTP is censored either from the left or from the right side by the posted price. Because no simple conjugate prior distribution exists under the two-sided censoring (Braden & Freimer, 1991), one cannot resort to the conjugate prior technique to reduce the problem dimensionality.
Methodology
Consider a finite-horizon dynamic pricing problem for a single product. Inventory replenishment is not possible during the selling horizon, and the terminal value at the end of the horizon is zero. At the beginning of each period, given the available inventory quantity q, the seller determines the unit price p for the product. The goal is to maximize the expected total revenue over the finite horizon. Specifically, we divide the finite selling horizon into T periods to guarantee that there is one customer arrival in each period (e.g., Broder & Rusmevichientong, 2012; Talluri & Van Ryzin, 2004). Time periods are indexed in reverse order, with the first selling period being period T and the last period being period 1. The customer arriving in period t has WTP Xt, which is a random draw from an i.i.d. distribution with a continuous density f (x|θ), where x ≥ 0 is the actual WTP and θ ∈ Θ is an unknown parameter of the distribution. At the beginning of period t, the seller has a prior belief concerning the value of θ, denoted by πt (θ). For the ease of exposition, we assume that Θ is a continuous set and that πt (θ) is a density over this set. When Θ is a discrete set, all our analysis will carry through by treating πt (θ) as a probability mass function. We shall use πt (θ) and πt (θ) interchangeably and suppress the subscript t whenever appropriate within the context.
Results and Discussion
A Finite-Horizon Bayesian Dynamic Pricing Model base on Chen and Wu (2016), Gamma and Two-Point priors with exponential and normal WTP distribution have been used. Results showed that the average WTP for general exploitation of this complex is within the extent of 12230 IRR (as minimum) in Gamma prior and exponential distribution and 45270 IRR (as maximum) in Two-Point prior and exponential distribution. Also, the average is 28750, while the WTP is 10623 IRR in non-Bayesian approach, which is lower than all the Bayesian estimation results. Therefore, the application of FHBD algorithm in dynamic pricing can be an appropriate way to determine the threshold amount of WTP for the exploitation of natural resources.
Conclusions & Suggestions
In sum, we study the Bayesian dynamic pricing problem under two-sided censoring with a short time horizon and limited inventory. Upon comparing it with the exact-observation system, we found that having better information always improves the revenue performance, while the optimal price under the exact-observation system can be either higher and lower than that under the two-sided censoring system. When comparing the above two systems with the no-learning system, we discover a surprising result that learning can bring a negative value when the inventory is scarce due to the biased learning effect. A derivative approximation heuristic is then devised to numerically solve the two-sided censoring problem. We further develop a performance bound to compare our proposed heuristic with other benchmark heuristics. Numerical experiments demonstrate that our heuristic consistently outperforms others and is robust with respect to WTP distributions. The two-sided censoring effect in our problem is a result of the binary customer choice model. When a customer faces a choice among multiple products, a more general choice model that surveys in the substitution effect is needed.
mahdi ghaemiasl; mostafa salimifar; mostafa rajabi mashhadi; Mohammad Hossien Mahdavi Adeli
Abstract
Renewable energy is derived from the natural processes that are replenished constantly. In its various forms, it derives directly from the sun, or from the deep heat generated from the earth. It isdefined as the electricity and heat being generated from solar, wind, ocean, hydropower, biomass, geothermal ...
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Renewable energy is derived from the natural processes that are replenished constantly. In its various forms, it derives directly from the sun, or from the deep heat generated from the earth. It isdefined as the electricity and heat being generated from solar, wind, ocean, hydropower, biomass, geothermal resources, biofuels, and the obtained hydrogen from the renewable resources. The renewable energy resources exist over the wide geographical areas in contrast to other energy sources, which are concentrated in a limited number of countries. The rapid deployment of the renewable energy and energy efficiency result in the significant energy security, climate change mitigation, and the economic benefits. The results of a recent review of the literature concluded that as the greenhouse gas (GG) emitters begin to be held liable for the damages emerging from GHG emissions in which lead to the climate change, the liability mitigation value would increase to provide powerful the incentives for the deployment of renewable energy technologies. At the national level, at least 30 nations around the world have already renewable energy contributing to more than 20% of energy supply. The national renewable energy markets are projected to continue to grow strongly in the coming decade and beyond while some 120 countries have various policy targets for the longer-term shares of the renewable energy including the 20 percent of the targeted electricity generated from the European Union by 2020. Some countries have much higher long-term policy targets of up to 100 percent renewables. Outside Europe, a diverse group of 20 or more other countries have targeted the renewable energy shares in the 2020-2030 rangimg from 10 to 50 percent.
This study is an investigation of the climatic characteristics of the regions of Khorasan and the neighboring areas within the interior regions (Semnan, Sistan, Yazd, and Mazandaran) as well as the foreign regions (Turkmenistan and Afghanistan). Besides, it is probingthe technical-economic conditions of the renewable-fossil hybrid power generation along with the impact of the implementation of 2030 renewable energy outlook policies of Khorasan regional electricity hybrid production system as well.
Methodology
Analytic programming was inspired by the numerical methods in Hilbert functional spaces and by GP. The principles of AP are somewhere between these two philosophies: The idea of the evolutionary creation of symbolic solutions arise from GP, whereas the general ideas of the functional spaces and the construction of the resulting function by means of a search process (usually done by the numerical methods such as Ritz or Galerkin method) are adopted from Hilbert spaces. Like GP or GE, AP is based on a set of functions, operators and so-called terminals, which are usually constants or independent variables. All these ‘mathematical’ objects create a set from which AP tries to synthesize an appropriate solution. The main principle of AP is based on Discrete Set Handling (DSH), proposed by Zelinka (2001). DSH can be seen as a universal interface between EA and the problem to be solved symbolically. That is, why AP can be carried out using almost any evolutionary algorithm. The set of the mathematical objects are functions, operators and so-called terminals (usually constants or independent variables). All these objects are mixed together consisting of functions with different number of arguments. Because of the variability of the content of this set, the article purposes of General Functional Set (GFS) is required. The structure of GFS is nested, for instance, it is created by the subsets of functions according to the number of their arguments. The content of GFS is dependent only on a user. Various functions and terminals can be mixed together. The subset structure presence in GFS is vitally important in AP. It is used to avoid the synthesis of the pathological programs, for instance, programs containing functions without arguments, etc. Performance of AP is, of course, improved if functions of GFS are expertly chosen based on the experiencies with the solved problem. The important part of the AP is the sequence of mathematical operations which are used for the program synthesis. These operations are used to categorize the individuals of the society into a suitable program. Mathematically saying, it is mapping the individual domain onto the program domain. This mapping consists of two main parts. The first part is called Discrete Set Handling (DSH) and the second one is the security procedures which do not allow for the pathological programs synthesis.
Results and discussion
Simulation results show that the implementation of 2030 renewable energyies outlook policies will lead to 18.62 TWh optimal inter-regional and trans-regional exports which 2.32 TWh of this optimal export will be generated because of implementation of 2030 renewable energy outlook policies. This 14 percent increase in the inter-regional and trans-regional exports creates 5,000 jobs in Khorasan and increases the associated cost by 32 percent, but there would be little impact on the environmental emissions’ reduction. The related reason for this insignificant reduction in the environmental emissions is the low limited renewable power generation in the production system. Besides, thereason for the significant increase in the price is the high capital cost of the solar and wind production which needs strong financial support from the technical-engineering wind and the solar projects as they share the cost of production with the customers. The maximum potential production capacity in order to cope with the fluctuating nature of the renewable generation, is thebasic attempt for the development of the renewable electricity generation.
Conclusion
Most of the world's leading countries in the field of renewable energy have used Feed-in Tariff to create an affordable price for the renewable power generation systems. It achieves this by offering long-term contracts to renewable energy producers, typically, based on the cost of generation of each technology. In addition, feed-in tariffs often include tariff degression which is amechanism according to which the price (or tariff) ratchets down over time. This is done in order to track and encourage the technological cost reductions. Also, developing the required financial incentives and promoting the standards for connecting the renewable sources to the grid is called for. In addition, the rules regarding the sharing of costs with the common network can also provide the necessary legal and technical infrastructure to make the hybrid production system.
