Dynamic Pricing Policy Research Articles

Learning Dynamic Selection and Pricing of Out-of-Home Deliveries

Home delivery failures, traffic congestion, and relatively large handling times have a negative impact on the profitability of last-mile logistics. A potential solution is the delivery to parcel lockers or parcel shops, denoted by out-of-home (OOH) delivery. In the academic literature, models for OOH delivery are so far limited to static settings, contrasting with the sequential nature of the problem. We model the sequential decision-making problem of which OOH location to offer against what incentive for each incoming customer, taking into account future customer arrivals and choices. We propose dynamic selection and pricing of OOH (DSPO), an algorithmic pipeline that uses a novel spatial-temporal state encoding as input to a convolutional neural network. We demonstrate the performance of our method by benchmarking it against two state-of-the-art approaches. Our extensive numerical study, guided by real-world data, reveals that DSPO can save 19.9 percentage points (%pt) in costs compared with a situation without OOH locations, 7%pt compared with a static selection and pricing policy, and 3.8%pt compared with a state-of-the-art demand management benchmark. We provide comprehensive insights into the complex interplay between OOH delivery dynamics and customer behavior influenced by pricing strategies. The implications of our findings suggest that practitioners adopt dynamic selection and pricing policies. History: This paper has been accepted for the Transportation Science special issue on TSL Conference 2023. Funding: This work was supported by TKI DINALOG.

Data-Driven Clustering and Feature-Based Retail Electricity Pricing with Smart Meters

The adoption of smart meters and dynamic pricing programs is rapidly increasing among electric utility companies. In “Data-Driven Clustering and Feature-Based Retail Electricity Pricing with Smart Meters,” Keskin, Li, and Sunar analyze how utility companies should use smart meter data for better pricing decisions. Utility companies typically have access to consumption patterns and high-dimensional features on customer characteristics and exogenous factors. The authors identify that such feature data can exhibit different forms of heterogeneity—over time and over customers. They show that the different forms of feature heterogeneity significantly worsen the best profit performance that can be achieved by a data-driven dynamic pricing policy. The authors also develop a policy based on joint spectral clustering and contextual dynamic pricing and prove that this policy achieves near-optimal profit performance.

Contextual Dynamic Pricing with Strategic Buyers

Personalized pricing, which involves tailoring prices based on individual characteristics, is commonly used by firms to implement a consumer-specific pricing policy. In this process, buyers can also strategically manipulate their feature data to obtain a lower price, incurring certain manipulation costs. Such strategic behavior can hinder firms from maximizing their profits. In this article, we study the contextual dynamic pricing problem with strategic buyers. The seller does not observe the buyer’s true feature, but a manipulated feature according to buyers’ strategic behavior. In addition, the seller does not observe the buyers’ valuation of the product, but only a binary response indicating whether a sale happens or not. Recognizing these challenges, we propose a strategic dynamic pricing policy that incorporates the buyers’ strategic behavior into the online learning to maximize the seller’s cumulative revenue. We first prove that existing nonstrategic pricing policies that neglect the buyers’ strategic behavior result in a linear Ω ( T ) regret with T the total time horizon, indicating that these policies are not better than a random pricing policy. We then establish an O ( T ) regret upper bound of our proposed policy and an Ω ( T ) regret lower bound for any pricing policy within our problem setting. This underscores the rate optimality of our policy. Importantly, our policy is not a mere amalgamation of existing dynamic pricing policies and strategic behavior handling algorithms. Our policy can also accommodate the scenario when the marginal cost of manipulation is unknown in advance. To account for it, we simultaneously estimate the valuation parameter and the cost parameter in the online pricing policy, which is shown to also achieve an O ( T ) regret bound. Extensive experiments support our theoretical developments and demonstrate the superior performance of our policy compared to other pricing policies that are unaware of the strategic behaviors. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Optimizing electric vehicle charging scheduling using enhanced multi-agent neural networks with dynamic pricing

Electric vehicles have gained widespread recognition as an environmentally conscious mode of transportation, primarily due to significant technological advancements and reduced emissions. When seamlessly integrated into the smart grid infrastructure, these vehicles can serve as flexible energy consumers and potential energy storage units. However, the efficient management of charging stations due to dynamic load demand & pricing poses a significant challenge, aiming to reduce wait times for electric vehicle owners and reduce electricity costs. To overcome this challenge, this study proposes a solution based on dynamic pricing policy and the adaptive Markov decision process. The approach utilizes a reinforcement learning-enabled enhanced multi-agent neural network (EMANN) to optimize charging schedules. This adaptive Markov decision process achieved optimal scheduling by minimizing charging costs during off-peak hours while completing charging in the shortest possible time. Furthermore, a constrained policy is employed to train the enhanced neural network using reinforcement learning, allowing the network to learn and solve adaptive Markov decision processes directly. To evaluate the effectiveness of this solution, the study conducted a numerical experiment using real-time data from Tesla electric vehicles. Combined with queuing models, the EMANN yielded impressive results, including a peak savings of 19.05% and an average profit of $376.1 per kilowatt-hour, achieved within a network convergence time of 512 s. The proposed algorithm for scheduling electric vehicles has been proven to be efficient in addressing the complexities associated with managing electric vehicle charging stations.

FairPlay: A Multi-Sided Fair Dynamic Pricing Policy for Hotels

In recent years, popular touristic destinations face overtourism. Local communities suffer from its consequences in several ways. Among others, overpricing and profiteering harms local societies and economies deeply. In this paper we focus on the problem of determining fair hotel room prices. Specifically, we put forward a dynamic pricing policy where the price of a room depends not only on the demand of the hotel it belongs to but also on the demand of: (i) similar rooms in the area and (ii) their hotels. To this purpose, we model our setting as a cooperative game and exploit an appropriate game theoretic solution concept that promotes fairness both on the customers' and the providers' side. Our simulation results involving price adjustments across real-world hotels datasets, confirm that ours is a fair dynamic pricing policy, avoiding both over- and under-pricing hotel rooms.

Dynamic Pricing and Route Guidance: A Multi-Agent System for Effective Parking and Traffic Management

In a city center that is usually flooded with traffic, finding a parking space is a challenge for most commuters. Searching for parking generates additional urban traffic, increases energy consumption and vehicle emissions, and wastes time. This study examines how parking systems can be used as an effective tool for reducing traffic congestion. It proposes a new, smart approach to parking using real-time dynamic pricing and route guidance in a multi-agent system to better align supply and demand, thereby reducing both congestion and the amount of time spend by drivers searching for parking. To examine the effects of dynamic parking pricing on road traffic, we conducted a multi-agent open-source framework to simulate spatiotemporal distributions of daily individual activity. By extending MATSim, we were able to model parking usage more realistically. We developed several micro-simulation scenarios to evaluate strategy performance for both on-street and off-street parking in the city center of Tunis, Tunisia. The results show that dynamic pricing policies can be used to plan and manage parking areas more efficiently, minimizing the dispersion of parking occupancy lots. Variations in parking prices were shown to increase parking lot vacancies and reduce traffic congestion. Moreover, the system can also generate increased revenue for government and private-sector parking authorities while improving driver satisfaction. Finally, the parking dynamic pricing strategy promises a convenient mobility experience, while increasing efficiency and reducing the negative externalities of urban transportation.

Dynamic pricing and replenishment policy under price, time, and service level-dependent demand and preservation investment

Time, price, and servicing are three important factors which affect the demand for any product. In a multi-period inventory model, different pricing in different periods may have a positive impact on demand. Offering discounts to customers during a shortage period is an efficient way to reduce lost sales. To address all these issues, a multi-period inventory model is developed and analyzed in this paper to obtain optimal pricing and replenishment periods where the market demand depends on price, time and service level provided by the retailer. The time horizon is fixed; the replenishment cycles are of equal length, but the shortage period may vary in different cycles. The retailer is allowed to charge different prices in different periods and provide discounts if someone places demand during a shortage period. Learning effects in holding and ordering costs are considered. Under a dynamic pricing framework, this study examines service improvement through learning and price discounts during shortages as an incentive for customers. It demonstrates that discounting indeed helps to produce more revenue when the back-order rate is low, and the service level is critical in generating demand when the stock-out period is lengthy, revealing a critical relationship between shortage periods and service level. A numerical experiment is performed to investigate the optimal policy in different situations and obtain some managerial insights.

Simple Monotonic Readjustment Policies with Applications to Markdown Pricing and Pricing in the Presence of Strategic Customers

A Simple Monotonic Dynamic Pricing Policy We consider a canonical revenue management setting in which a monopolist seeks to maximize expected total revenues by selling a fixed inventory of product to customers who arrive sequentially over time. It is known in the literature that a simple dynamic pricing policy can perform reasonably well in such setting. However, the literature typically assumes that price can move freely over time (i.e., it can either go up or down). In practice, firms often impose certain restrictions on price movement. In this paper, we consider the setting in which price can only move monotonically over time (either only decreasing or only increasing). We devise a simple provably near-optimal dynamic pricing policy. Aside from markdown applications, our policy can also be applied to the setting with strategic customers.

Asymptotic Optimality of Constant-Order Policies in Joint Pricing and Inventory Models

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].

Real-Time Spatial–Intertemporal Pricing and Relocation in a Ride-Hailing Network: Near-Optimal Policies and the Value of Dynamic Pricing

In “Real-Time Spatial–Intertemporal Pricing and Relocation in a Ride-Hailing Network: Near-Optimal Policies and The Value of Dynamic Pricing,” Chen, Lei, and Jasin consider a dynamic pricing problem faced by a ride-hailing service provider who manages a fixed number of servers and serves price-sensitive customers within a network. Servers serve arriving customers by relocating from the requested origins to destinations within a certain travel time. The authors first propose a static pricing policy based on the optimal solution to a deterministic relaxation of the original stochastic problem. They show that the proposed static policy matches the best possible asymptotic performance of any static policy. The authors further propose a dynamic pricing policy that adaptively changes the prices in a way that reduces the impact of past demand randomness on the balance of future distributions of servers and customers across the network. They show that the dynamic pricing policy achieves significantly better asymptotical performance. The proposed policies and their performance guarantees are further extended to a case where the firm jointly decides the relocation of vacant servers

Intelligent decision support tool for optimizing stochastic inventory systems under uncertainty

Pricing and production policies play a key role in ensuring the added value of supply chain systems. For perishable inventory management, the pricing and production lines must be manipulated dynamically since several uncertainties are involved in the system’s behavior. This study discusses the impact of dynamic pricing and production policies on an uncertain stochastic inventory system with perishable products. The mathematical model of the inventory management system under external disturbance is formulated using a continuous differential equation in which the price and production rates are considered as control factors to optimize total profits, which is described as an objective function. An analytical solution for the optimal pricing and production rate was obtained using the Hamilton-Jacobi-Bellman equation. The unknown disturbance was approximated using an intelligent approach called radial basis function neural network. Finally, extensive numerical simulations were presented to validate the theoretical results and optimization solutions (including the efficiency of the approximation of the unknown disturbance) for the dynamic pricing and production management strategy of an uncertain stochastic inventory system against volatile markets. The performance of the proposed method was analyzed under different stock level conditions, which highlighted the importance of keeping the inventory levels at an optimal range to ensure the profitability of business operations. This management strategy can assist a business with solutions for inventory policies while supporting decision-making processes to facilitate coping with production management disruptions.

Dynamic pricing and inventory control for multiple products in a retail chain

The biggest problems in the retail industry are related to price allocation to keep the product attractive to the market, without allowing arbitrage, together with adequate inventory control involving substitution and complementarity. The above objectives gains relevance if the uncertain behavior of the market is taken into account. Most of the existing literature addresses each of the above problems independently or as a combination of some of them and not as a whole that is related to each other. In this research, we present a model that allows setting optimal dynamic pricing and inventory policies in each period in which a planning horizon is divided. When assigning the price, the model avoids the presence of arbitrage, by allowing the use of fixed price policies (same price for the product in the stores) and/or variable price policies (different prices for the product in the stores). When making replenishments, it allows having an adequate quantity of each product, since it incorporates substitution and complementarity between them. The market demand in each store is represented by a probability distribution that is a function of the market by means of a seasonality parameter, price sensitivity, price and inventory, subject to a set of restrictions. We find that demand has uncertainty in the seasonality parameter and in the price sensitivity, due to variations outside the historical data with which it is calculated. To handle this uncertainty, we use a Robust Stochastic Optimization formulation, which we solve in an approximate way, using a scenario generation technique. We show computational experiments applied to a case with industrial data. The results show the advantages of using this methodology, allowing managers to make the right decisions and thus improve operating results.

Matching of everyday power supply and demand with dynamic pricing: Problem formalisation and conceptual analysis

The energy transition is expected to significantly increase the share of renewable energy sources whose production is intermittent in the electricity mix. Apart from key benefits, this development has the major drawback of generating a mismatch between power supply and demand. The innovative dynamic pricing approach may significantly contribute to mitigating that critical problem by taking advantage of the flexibility offered by the demand side. At its core, this idea consists in providing the consumer with a price signal which is evolving over time, in order to influence its consumption. This novel approach involves a challenging decision-making problem that can be summarised as follows: how to determine a price signal maximising the synchronisation between power supply and demand under the constraints of maintaining the producer/retailer’s profitability and benefiting the final consumer at the same time? As a contribution, this research work presents a detailed formalisation of this particular decision-making problem. Moreover, the paper discusses the diverse algorithmic components necessary to efficiently design a dynamic pricing policy: different forecasting models together with an accurate statistical modelling of the demand response to dynamic prices.

Demand management in time-slotted last-mile delivery via dynamic routing with forecast orders

In this paper, we propose a partially time-windowed dynamic routing approach with forecast orders to tackle the dynamic pricing problem of attended home delivery, one of the challenging problems in last-mile logistics. The purpose of forecast orders is to find a cost-effective route map for delivery with forecast orders to guide the delivery system to accept real orders in potentially better time slots for servicing. Initially, we build and optimise virtual routes using forecast orders without time windows. Upon the arrival of real orders, we replace the forecast orders with incoming real orders to help the delivery system estimate the opportunity cost of making the delivery in each time slot. Dynamic pricing is then used to influence the customer’s choices of servicing slots, and based on what the customer selects, the real order replaces the forecast order, and the route map is re-optimised. This strategy leads to better scheduling for the overall route map and more slot availability for incoming customers. Through computational study, we demonstrate the benefit of our approach compared to static pricing and previous dynamic pricing policies, with or without forecast orders. In particular, by employing forecast orders without time windows, we witness a decrease in delivery costs and an increase in the number of accepted orders, leading to higher profits.

Optimal dynamic pricing and production policy for a stochastic inventory system with perishable products and inventory-level-dependent demand

In this paper, we consider a joint dynamic pricing and production policy for a stochastic inventory system with perishable products. The demand is stochastic and dependent on the price and the level of the on-hand inventory. Combined dynamic pricing and production control, a stochastic dynamic optimization model that maximizes the total discounted profit is built. Applying the stochastic optimal control theory, we formulate the problem of finding the optimal joint dynamic pricing and production schedule as the problem of solving a Hamilton-Jacobi-Bellman (HJB) equation. By solving the HJB equation, analytical solutions for optimal pricing and production rate are obtained. In addition, a numerical example is given to illustrate the validness of the theoretical results and sensitivity analysis on major system parameters is carried out.

Minimizing food waste in grocery store operations: Literature review and research agenda

Research on grocery waste in food retailing has recently attracted particular interest. Investigations in this area are relevant to address the problems of wasted resources and ethical concerns, as well as economic aspects from the retailer’s perspective. Reasons for food waste in retail are already well-studied empirically, and based on this, proposals for reduction are discussed. However, comprehensive approaches for preventing food waste in store operations using analytics and modeling methods are scarce. No work has yet systematized related research in this domain. As a result, there is neither any up-to-date literature review nor any agenda for future research. We contribute with the first structured literature review of analytics and modeling methods dealing with food waste prevention in retail store operations. This work identifies cross-cutting store-related planning areas to mitigate food waste, namely (1) assortment and shelf space planning, (2) replenishment policies, and (3) dynamic pricing policies. We introduce a common classification scheme of literature with regard to the depth of food waste integration and the characteristics of these planning problems. This builds our foundation to review analytics and modeling approaches. Current literature considers food waste mainly as a side effect in costing and often ignores product age dependent demand by customers. Furthermore, approaches are not integrated across planning areas. Future lines of research point to the most promising open questions in this field.

Optimal joint dynamic pricing and inventory replenishment policy for perishable products with freshness-sensitive demand

Optimal joint dynamic pricing and inventory replenishment policy for perishable products with freshness-sensitive demand

Smes managers’ ratings of pricing policies for marketing of consumers’ goods in abia state, nigeria

The survival of SMEs in Abia State depends on their ability to dispose of goods produced. It is through marketing the goods that SMEs reach the customers, recover money used for production, and make profit in return. This study was carried out to determine the pricing policies adopted by SMEs managers in marketing consumers’ goods in Abia State, Nigeria. Two research questions and two null hypotheses guided the study. Survey research design was adopted, and the population of the study consisted of 1,809 SMEs managers in Abia State. A sample size of 328 SMEs managers was selected using Taro Yamane formula. The sampling technique was a proportionate random sampling. A structured questionnaire which was validated by three experts in the field of educationand marketing was used for data collection. Structured questionnaire titled “Utilization of Pricing Policies in Marketing Consumer Goods Questionnaire (UPP-MCGQ)”, containing 15 items was used for data collection. The reliability of the instrument was established using pilot-testing and data collected were analyzed using Cronbach Alpha formula which yielded correlation coefficients of 0.86 and 0.91 for clusters B1 and B2 respectively with an overall value of 0.89. Data collected were analyzed using mean, standard deviation and ANOVA to answer the research questions and test the null hypotheses at 0.05 level of significance. Findings revealed that SMEs managers utilize value based pricing policies in marketing their goods to a small extent while they utilize dynamic pricing policy to a high extent. Years of experience were not significant factors on the extent SMEs managers utilize value based and dynamic pricing policies in marketing consumer goods. It was concluded that SMEs managers must ensure that their prices elicit the required demand of their products to cover business costs and make profit in the marketplace. The researcher recommended among others that, managers of SMEs should focus more on understanding customers’ value of their goods to enable them effectively set prices that will stimulate sales and generate profit.

Multi-period dynamic pricing model for deteriorating products in a supply chain with preservation technology investment and carbon emission

In this study, a multi-period dynamic pricing model in a two-echelon supply chain consisting of a retailer and a manufacturer is developed for a deteriorating product with a fixed shelf life. The demand at the retailer in each period is considered to be a function of the retail price, inventory on stock, reference price, and product freshness. Preservation technology investment (PTI) is considered to retard the rate of deterioration with carbon emission to keep the process green. Centralised and decentralised models are developed considering dynamic pricing for the retailer and static pricing for the manufacturer with a variable replenishment cycle length. Algorithms have been developed to obtain the optimal replenishment cycle length, retailer’s end of inventory level, retail price, PTI and discounted total profit for the retailer/manufacturer/supply chain. We study the generated trade-off between PTI, benefits of reduced deterioration rate and cost of carbon emission. The numerical studies suggest that supply chain profit can be greatly improved with efficient PTI and dynamic pricing policy. We also find that the initial reference price plays a key role in improving the profit.

Differential Privacy in Personalized Pricing with Nonparametric Demand Models

With the rapid development of artificial intelligence and big data, the application of data-driven personalized pricing has been increasingly prevalent in real practices such as finance, insurance, and retailing. However, with the public’s growing concern of the abuse of their personal data, legislation efforts are being taken to guarantee data privacy. In this work, we guarantee customers’ data privacy from the algorithm design of our dynamic personalized pricing policies. Two algorithms are developed with different levels of privacy guarantee. The first algorithm protects customers’ data in a centralized manner, meaning that the data aggregator (the pricing platform) is trusted, and the attacker is unlikely to know customers’ personal information. The second algorithm has a stronger privacy guarantee, which is mathematically proved to be able to protect customers’ data even when the data set is hacked. Besides privacy protection, both of our algorithms are effective in achieving near-optimal revenue maximization.