Dynamic pricing is a field of research that has gained acceptance in the scientific community and management literature. This paper aims to review the citations made in the literature on Dynamic pricing and investigate the development of knowledge of this field of research. Bibliometric methods were used to conduct this study, including scientific mapping of Dynamic pricing. VOSviewer software was used for scientific mapping. Five clusters in the co-citation were introduced by giving statistical and graphical information. A conceptual framework of perceived price fairness was presented. The results show a growing trend in Dynamic pricing. It has been shown that adequate studies have not been there to identify the variables affecting Dynamic pricing and to consider all the dimensions affecting the perceived fairness of price, and fewer studies have been conducted in the field of B2B research. The results of the study show that in all bibliographic fields USA is dominant to other countries. This article is the first bibliographic study in the field of Dynamic pricing, and it presents the research gap in this area and directs the perspective of future research. This article is useful for researchers and enthusiasts in the field of Dynamic pricing.

We extend the concept of Dynamic pricing by integrating it with “overselling with opportunistic cancellation” option, within the framework of Dynamic policy. Under this strategy, to sell a stock of perishable product (or capacity) two prices are offered to customers at any given time period. Customers are categorized as high-paying and low-paying ones. The seller deliberately oversells its capacity if high paying customers show up, even when the capacity is already fully booked by low-paying customers. In that case, the sale to some low-paying customers is canceled, although an appropriate compensation must be provided. A Dynamic programming approach is applied to formulate and solve this problem. We develop two models for continuous and periodic pricing, depending on the frequency of price changing. The advantage of this system over Dynamic pricing model is investigated through some numerical examples. We also study some structural properties of the optimal policies.

The complexity of the production systems and increasing competition in the business environment has made uncertainty a crucial problem in supply chain design. In this regard, this study aims to develop an optimization model to configure multi-period, multi-product and multi-echelon supply chains, managing the demand and supply uncertain parameters simultaneously to maximize the profit of the entire chain. In this model, the uncertainty of the parameters was regulated by robust optimization and Dynamic pricing approaches. First, the developed model was solved using the GAMS software, then the appropriate performance of the proposed model was compared with a certain base model, the validation of results were performed using sensitivity analysis. In the next step, through simulating supply and demand fluctuations, the model's performance under different conditions of uncertainty was evaluated. The results indicate that the model's optimal solution could resist this random uncertainty, even exposing a high level of supply and demand fluctuations.

Revenue management is based on offering the right product to the right customer at the right time. Revenue management scientists have concluded that traditional tools must be restructured based on the economic crisis, which has changed customers' behavior, and also the advent of low cost carriers, which attract a significant share of the customers. These new carriers offer a simplified fare structure and lower fares, and they apply price-based revenue management techniques. Dynamic pricing has several advantages, such as increase in revenue, goal oriented pricing and leveling capacity usage among resources. One of the challenges of the parallel flight sets is aggregating demand on specific flights, which can be overcome by applying Dynamic pricing techniques. In this article a Dynamic pricing technique based on customer choice behavior is presented for low cost carriers. Choice-based deterministic linear programming is used for approximating marginal values of resources by dual prices. These values are applied for approximating the Dynamic programming problem for pricing of substitutable products. A multinomial logit model is used for modeling customer choice behavior. The proposed system is goal oriented and charges lower prices for leisure customers and higher prices for business. Monte Carlo simulation is applied and two pricing strategies, Dynamic and fixed pricing, are analyzed and their results compared. The case study results show that for ample capacity compared to demand, application of a fixed pricing strategy with a price point that has low utility leads to a noticeable reduction in the company's revenue. In the case of force for using a fixed pricing policy, different factors such as initial capacity, demand and customer utility should be considered to choose suitable prices. Simultaneously with limiting initial capacity, Dynamic pricing will move towards fixed pricing with higher price points.

ObjectiveThe lack of perceived fairness in pricing can lead to various negative consequences, including diminished trust in the seller and the prices, reduced demand, lower customer satisfaction, negative word-of-mouth advertising, loss of customer loyalty to the company or brand, increased complaints, and a reluctance to make future purchases. Considering all the mentioned destructive consequences, it is necessary to design a perceived fairness model for Dynamic pricing. Accordingly, this study to present the perceived fairness model of Dynamic pricing using a meta-synthesis approach. MethodologyThis research is descriptive in terms of its practical purpose, data collection, and qualitative approach. The method used in this study is qualitative. Meta-synthesis involves several steps: searching, evaluating, synthesizing, expressing, and partially interpreting both quantitative and qualitative research. Transcombination can be performed using various methods; in this study, the 7-step model of Sandelowski and Barso was employed. The data collection tool consisted of past documents, including 32 articles. Content analysis was used as the method for data analysis. FindingsThe findings indicated the drivers affecting the perceived fairness of Dynamic pricing in three categories: customer-related factors (demographic characteristics, price knowledge, price expectation, consumption and behavioral experience, familiarity with Dynamic pricing), company-related factors (pricing transparency, communication with customers, trust), and market-related factors (price position, price dispersion). These drivers are the set of factors that affect the perceived fairness of Dynamic pricing. The drivers include various factors. Perceived fairness of Dynamic pricing includes two dimensions: emotional fairness and cognitive fairness. Customers who expect low prices will be suspicious and unfair about price increases. The actions taken by companies to raise customer awareness about the reasons for price increases, the timing of price changes, and similar actions in past periods significantly influence customers' accurate understanding of the price changes, preventing them from perceiving them as unfair. Perceived fairness consequences of Dynamic pricing are categorized into two categories: positive consequences (customer satisfaction, customer loyalty, repurchase intention) and negative consequences (negative feelings, negative behaviors, and negative advertising by customers). ConclusionAny price difference should be based on a logical reason and serve as a tool for segmentation. While pricing policies inevitably influence consumers' purchase intentions, they should not undermine the perceived fairness from the customer's perspective. The identified drivers of perceived fairness in Dynamic pricing, when applied to pricing the same goods under different conditions, provide a foundation for pricing decision-makers within companies.

Network pricing is an effective transportation demand management scheme to reduce traffic and control air pollution, particularly in CBD’s. In this study, congested area cordon entry and Park-and-Ride (P/R) facility use are priced for automobiles. Increase in these prices may reduce private auto demand by impelling some of them into using public transport from the origin of their trips, or encouraging them to park their cars at the P/R facilities and use buses, or pay the entrance fee and drive inside the cordon. A Markov decision process has been devised to findan optimal policy for pricing the P/R facilities and cordon entry in a city within a variable demand context. This process is sensitive to weather condition (more favorable atmospheric condition allows lower prices). It leads to a balance between the costs of pollution hazards to the public at large on one side, and the cost imposed upon auto drivers to take the next best decisions and their travel times and costs on the other side: thus giving a resilient character to the city. The model has been applied to the network of a large urban area, and the results are analyzed. Suggestions for further research end the discussion.

In 21st century, achieving business success and survival become harder for enterprises. This fact comes from the new trade environment that constant change is its basic element. New situation necessitates reviewing the trading priorities, quality structure, strategic views and new pricing models that has been ever used. In this new environment which most of the trades are being done electronically and automatically, new ways is devised to understand the needs of customers and Dynamic pricing is one of which. But in these Dynamic pricing methods, human factor is ignored and these pricing agents usually act ignoring this fact that one party is human. In this paper, after reviewing pricing models and techniques, we try to analyze the human factor using field study and then a new model is developed using the findings based on the effect of relevant factors. Finally, different pricing methods are compared using a new model. Considering the effect of the human factors, an imaginary environment is developed and different pricing methods are used to maximize the profit in the imaginary environment. Simulation results can be used in different situations to determine the best pricing method.