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Gallego, Guillermo, Anran Li, Van-Anh Truong, and Xinshang Wang. "Approximation Algorithms for Product Framing and Pricing." Operations Research 68, no. 1 (2020): 134–60. http://dx.doi.org/10.1287/opre.2019.1875.
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Myklebust, T. G. J., M. A. Sharpe, and L. Tunçel. "Efficient heuristic algorithms for maximum utility product pricing problems." Computers & Operations Research 69 (May 2016): 25–39. http://dx.doi.org/10.1016/j.cor.2015.11.013.
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Wan, Fu Cai, and Man Hua Qiu. "Customer Data-Oriented Multi-Product Pricing Model." Applied Mechanics and Materials 602-605 (August 2014): 106–10. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.106.
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Through interaction with online consumers, e-commerce websites can gather data reflecting consumer preferences. The available data on consumer preferences together with sophisticated analytical tools enables companies’ increases in profit through optimization of prices. We consider a class of models of consumer purchasing behavior, each of which affected the data on a consumer’s requirements and budget constraint to subsequent purchasing tendencies. We can study for the multi-product pricing problem based on the max budgets of consumers, and a customer data-oriented multi-product pricing model is presented. Proposed models were solved through genetic algorithms. Optimal solution of the given examples shows that this model is effective for enterprises.
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Elmachtoub, Adam N., and Michael L. Hamilton. "The Power of Opaque Products in Pricing." Management Science 67, no. 8 (2021): 4686–702. http://dx.doi.org/10.1287/mnsc.2020.3750.
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We study the power of selling opaque products, that is, products where a feature (such as color) is hidden from the customer until after purchase. Opaque products, which are sold with a price discount, have emerged as a powerful vehicle to increase revenue for many online retailers and service providers that offer horizontally differentiated items. In the opaque selling models we consider, all of the items are sold at a single common price alongside opaque products that may correspond to various subsets of the items. We consider two types of customers, risk-neutral ones, who assume they will receive a truly random item of the opaque product, and pessimistic ones, who assume they will receive their least favorite item of the opaque product. We benchmark opaque selling against two common selling strategies: discriminatory pricing, where one explicitly charges different prices for each item, and single pricing, where a single price is charged for all the items. We give a sharp characterization of when opaque selling outperforms discriminatory pricing; namely, this result holds for situations where all customers are pessimistic or the item valuations are supported on two points. In the latter case, we also show that opaque selling with just one opaque product guarantees at least 71.9% of the revenue from discriminatory pricing. We then provide upper bounds on the potential revenue increase from opaque selling strategies over single pricing and describe cases where the increase can be significantly more than that of discriminatory pricing. Finally, we provide pricing algorithms and conduct an extensive numerical study to assess the power of opaque selling for a variety valuation distributions and model extensions. This paper was accepted by Gabriel Weintraub, revenue management and market analytics.
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Shioda, R., L. Tunçel, and T. G. J. Myklebust. "Maximum utility product pricing models and algorithms based on reservation price." Computational Optimization and Applications 48, no. 2 (2009): 157–98. http://dx.doi.org/10.1007/s10589-009-9254-5.
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Bhaktha, Ankith S., Akshay P. Raj, Devika N, and Gripsy Paul. "Online Order Management System." International Journal for Research in Applied Science and Engineering Technology 11, no. 8 (2023): 1–10. http://dx.doi.org/10.22214/ijraset.2023.54265.
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Abstract: Our proposed online order management system is a platform that provides sellers with a comprehensive overview of their stock and orders. The platform streamlines the order management process, allowing the seller to view and manage their products and stocks with ease. Unlike traditional online order management systems, our platform goes beyond basic stock management and incorporates two machine learning algorithms to improve the seller's efficiency and profitability. The first machine learning algorithm provides the seller with insights into the popularity of a particular product category during a specific month. By notifying the seller of trends, they can better plan their stock levels to maximize profits. For example, if a seller notices that a particular product category is in high demand during the holiday season, they can increase their stock levels to meet the increased demand and maximize their profits. The second machine learning algorithm dynamically sets prices for the seller's products based on factors such as demand, date, and product category. This algorithm takes into account market trends and consumer behavior to set a price that is both competitive and profitable for the seller. For example, if a particular product is in high demand during a particular day, the algorithm will adjust the price to reflect the increased demand and allow the seller to sell the product at a higher price. In addition to these two machine learning algorithms, our online order management system includes a range of other features to help sellers manage their business effectively and efficiently. This includes order tracking, stock management, and customer management, all of which can be accessed from a single dashboard. The implementation of these machine learning algorithms is a crucial aspect of our online order management system, as they allow sellers to make informed decisions about their business. By using data and insights to optimize stock levels and pricing strategies, sellers can maximize their profits and minimize their losses. It is a comprehensive platform that provides sellers with the tools they need to manage their business effectively and efficiently.
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Bucarey, Víctor, Sourour Elloumi, Martine Labbé, and Fränk Plein. "Models and algorithms for the product pricing with single-minded customers requesting bundles." Computers & Operations Research 127 (March 2021): 105139. http://dx.doi.org/10.1016/j.cor.2020.105139.
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Ke, Chenxu, and Ruxian Wang. "Cross-Category Retailing Management: Substitution and Complementarity." Manufacturing & Service Operations Management 24, no. 2 (2022): 1128–45. http://dx.doi.org/10.1287/msom.2021.0968.
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Problem definition: This paper studies pricing and assortment management for cross-category products, a common practice in brick-and-mortar retailing and e-tailing. Academic/practical relevance: We investigate the complementarity effects between the main products and the secondary products, in addition to the substitution effects for products in the same category. Methodology: In this paper, we develop a multistage sequential choice model, under which a consumer first chooses a main product and then selects a secondary product. The new model can alleviate the restriction of the independence of irrelevant alternatives property and allows more flexible substitution patterns and also takes into account complementarity effects. Results: We characterize the impact of the magnitude of complementarity effects on pricing and assortment management. For the problems that are hard to solve optimally, we propose simple heuristics and establish performance guarantee. In addition, we develop easy-to-implement estimation algorithms to calibrate the proposed sequential choice model by using sales data. Managerial implications: We show that ignoring or mis-specifying complementarity effects may lead to substantial losses. The methodologies on modeling, optimization, and estimation have potential to make an impact on cross-category retailing management.
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Subbarayudu, Yerragudipadu, G. Vijendar Reddy, M. Vamsi Krishna Raj, K. Uday, M. D. Fasiuddin, and P. Vishal. "An efficient novel approach to E-commerce retail price optimization through machine learning." E3S Web of Conferences 391 (2023): 01104. http://dx.doi.org/10.1051/e3sconf/202339101104.
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Businesses can use price optimization to discover the most profitable price point by using customer and market data to drive their decisions. The optimal price points will result in the company making the most money possible, but they may also be created to help the company expand into untapped markets or increase its market share, for example Businesses can use machine learning to price products and services to maximise sales or profitability by using data instead of educated guess-work. When utilised for price optimization, ML-based algorithms can be used to forecast demand for a particular product as well as the ideal price and how buyers will respond to specific pricing. Pricing decisions can be made more accurately using machine learning, which will boost a company's revenue.
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Hutchinson, Christophe Samuel, Gulnara Fliurovna Ruchkina, and Sergei Guerasimovich Pavlikov. "Tacit Collusion on Steroids: The Potential Risks for Competition Resulting from the Use of Algorithm Technology by Companies." Sustainability 13, no. 2 (2021): 951. http://dx.doi.org/10.3390/su13020951.
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Digitalization has a growing impact on everyone’s life. It influences the way consumers purchase products, read online news, access multimedia content, and even meet or interact socially. At the core of digital products lies algorithm technology, decision-making software capable of fulfilling multiple tasks: data mining, result ranking, user matching, dynamic pricing, product recommendations, and ads targeting, among others. Notwithstanding the perceived benefits of algorithms for the economy, the question has been raised of whether the use of algorithms by businesses might have countervailing effects on competition. Although any anti-competitive behavior typically observed in traditional markets can be implemented by this technology, a particular issue highlighted in discussions between researchers and practitioners is the concern that algorithms might foster collusion. Because of their capacity to increase market transparency and the frequency of interactions between competing firms, they can be used to facilitate parallel collusive behavior while dispensing competing firms with the need for explicit communication. Consequently, it is not excluded that algorithms will be used in the years to come to obtain the effects of a cartel without the need to enter into restrictive agreements or to engage in concerted practices. We evaluate the collusion risks associated with the use of algorithms and discuss whether the “agreement for antitrust purposes” concept needs revisiting. The more firms made use of types of algorithms that enable direct and indirect communication between the competitors, the more likely those companies may be considered liable.
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SHINDE, AMIT, MOEED HAGHNEVIS, MARCO A. JANSSEN, GEORGE C. RUNGER, and MANI JANAKIRAM. "SCENARIO ANALYSIS OF TECHNOLOGY PRODUCTS WITH AN AGENT-BASED SIMULATION AND DATA MINING FRAMEWORK." International Journal of Innovation and Technology Management 10, no. 05 (2013): 1340019. http://dx.doi.org/10.1142/s0219877013400191.
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A framework is presented to simulate and analyze the effect of multiple business scenarios on the adoption behavior of a group of technology products. Diffusion is viewed as an emergent phenomenon that results from the interaction of consumers. An agent-based model is used in which potential adopters of technology product are allowed to be influenced by their local interactions within the social network. Along with social influence, the effect of product features is important and we ascribe feature sensing attributes to the consumer agents along with sensitivities to social influence. The model encompasses utility theory and discrete choice models in the decision-making process for the consumers. We use expressive machine learning algorithms that can handle complex, nonlinear, and interactive effects to identify important inputs that contribute to the model and to graphically summarize their effects. We present a realistic case study that demonstrates the ability of this framework to model changes in market shares for a group of products in response to business scenarios such as new product introduction and product discontinuation under different pricing strategies. The models and other tools developed here are envisioned to be a part of a recommender system that provides insights into the effects of various business scenarios on shaping market shares of different product groups.
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Guerreiro, Marcio Trindade, Eliana Maria Andriani Guerreiro, Tathiana Mikamura Barchi, et al. "Anomaly Detection in Automotive Industry Using Clustering Methods—A Case Study." Applied Sciences 11, no. 21 (2021): 9868. http://dx.doi.org/10.3390/app11219868.
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In automotive industries, pricing anomalies may occur for components of different products, despite their similar physical characteristics, which raises the total production cost of the company. However, detecting such discrepancies is often neglected since it is necessary to find the problems considering the observation of thousands of pieces, which often present inconsistencies when specified by the product engineering team. In this investigation, we propose a solution for a real case study. We use as strategy a set of clustering algorithms to group components by similarity: K-Means, K-Medoids, Fuzzy C-Means (FCM), Hierarchical, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Self-Organizing Maps (SOM), Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Differential Evolution (DE). We observed that the methods could automatically perform the grouping of parts considering physical characteristics present in the material master data, allowing anomaly detection and identification, which can consequently lead to cost reduction. The computational results indicate that the Hierarchical approach presented the best performance on 1 of 6 evaluation metrics and was the second place on four others indexes, considering the Borda count method. The K-Medoids win for most metrics, but it was the second best positioned due to its bad performance regarding SI-index. By the end, this proposal allowed identify mistakes in the specification and pricing of some items in the company.
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Rana, Samir. "Time Series Model to Forecast the Pricing of Dairy Products." Mathematical Statistician and Engineering Applications 70, no. 2 (2021): 1670–77. http://dx.doi.org/10.17762/msea.v70i2.2457.
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Seasonal patterns and patterns of food production are linked with each other, which contributes to have a significant impact on the economy of a country. Seasons and food production patterns are linked with each other. Because of this, it is of the utmost importance to establish projections about the production of food patterns that are sensitive to variations in the climate, which will ultimately result in satisfied customers and successful production. Because of this, it is vital to establish trustworthy techniques of dairy production forecasting in order to prevent a shortage of dairy production in an industry. As a result, the purpose of this study is to construct a model that is capable of reliably predicting the price of dairy products in relation to changes in the climate. In the course of the research for the thesis, Ireland will serve as the case study, and a mix of time series and machine learning models will be utilized in order to make price predictions. A time series of data relating to dairy production from the year 1990 to 2019 is extracted and utilized as a data source for dairy products. This data source will be used until the year 2019. The work that was actually done for the proposed paper makes use of the entire dataset for training purposes and makes use of the pricing list for the previous year for the testing phase. These are then utilized as variables in order to assess product yields as well as product losses. The implementation, on the other hand, is based on the principles of time series as well as four machine learning algorithms, specifically ARIMA, ARIMA Garch, SEM, and SARIMA. In compared to the results of other models, it was shown that the SARIMA model produced superior results. In addition, the findings were computed on the basis of an assessment matrix that took into account the root mean square error.
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Rasekh, Mansour, Hamed Karami, Alphus Dan Wilson, and Marek Gancarz. "Classification and Identification of Essential Oils from Herbs and Fruits Based on a MOS Electronic-Nose Technology." Chemosensors 9, no. 6 (2021): 142. http://dx.doi.org/10.3390/chemosensors9060142.
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The frequent occurrence of adulterated or counterfeit plant products sold in worldwide commercial markets has created the necessity to validate the authenticity of natural plant-derived palatable products, based on product-label composition, to certify pricing values and for regulatory quality control (QC). The necessity to confirm product authenticity before marketing has required the need for rapid-sensing, electronic devices capable of quickly evaluating plant product quality by easily measurable volatile (aroma) emissions. An experimental MAU-9 electronic nose (e-nose) system, containing a sensor array with 9 metal oxide semiconductor (MOS) gas sensors, was developed with capabilities to quickly identify and classify volatile essential oils derived from fruit and herbal edible-plant sources. The e-nose instrument was tested for efficacy to discriminate between different volatile essential oils present in gaseous emissions from purified sources of these natural food products. Several chemometric data-analysis methods, including pattern recognition algorithms, principal component analysis (PCA), and support vector machine (SVM) were utilized and compared. The classification accuracy of essential oils using PCA, LDA and QDA, and SVM methods was at or near 100%. The MAU-9 e-nose effectively distinguished between different purified essential oil aromas from herbal and fruit plant sources, based on unique e-nose sensor array responses to distinct, essential-oil specific mixtures of volatile organic compounds (VOCs).
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Kim, Chin-Woo. "Price Personalization." LAW RESEARCH INSTITUTE CHUNGBUK NATIONAL UNIVERSITY 13, no. 2 (2022): 43–81. http://dx.doi.org/10.34267/cbstl.2022.13.2.43.
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Product prices are now one of the many areas of our daily life in which artificial intelligence is becoming increasingly important. Digitization, especially online trading, opens up completely new possibilities and gives price adjustments a big boost in various ways. Thanks to their computing speed, algorithms can not only change and vary prices very quickly. Their enormous capacities for collecting and processing information even make it possible to include consumer data in the price calculation and thus adjust prices individually. Although we have become accustomed to uniform prices, the price has never been a purely static construct. Fluctuating prices are not a new phenomenon. Pricing freedom prevails within the framework of private autonomy. The aim of this paper is to examine how Korean law can respond to the new challenges posed by algorithmic price diversity. The law currently seems to offer little help against the currently existing lack of transparency in relation to the pricing process, which can go hand in hand with automated price individualization. So far, there are no legal regulations for disclosing how prices are determined. A central concern when dealing with individualized prices concerns the existence of information asymmetries and the (in)transparency of pricing strategies.
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Zelenko, Eduard. "DETERMINING THE CORRELATION BETWEEN DATASETS FOR CALCULATION OF THE RETAIL PRICE WHEN USING SOFTWARE AGENTS." Management of Development of Complex Systems, no. 50 (June 27, 2022): 102–5. http://dx.doi.org/10.32347/2412-9933.2022.50.102-105.
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The development and support of algorithms for calculating prices in the field of trade is one of the current trends in the development of information systems in recent years, which attracts the attention of specialists of various profiles. Experts in the field of economics see in such algorithms new opportunities for improving automated systems, transforming them into public automated systems of a new generation with advanced means of presenting various digital information resources and accessing them, created taking into account the need for integration and the use of APIs. Within the framework of the outlined problem, the scientific tasks of developing models, methods, algorithms and programs that simulate processes for data processing and price calculation in order to determine their main characteristics for the construction of mathematical software for automated retail price calculation systems are important [12]. In general, effective pricing contributes to the subordination of production to social needs, and an adequate level of prices contributes to economic growth, provides an effective competitive environment and orients production to innovative content [1]. The above determines the relevance of the development and improvement of algorithms for calculating the retail price of a product in the event, that already known algorithms determine the price inaccurately (for example, when there is insufficient amount of necessary data due to certain factors). The use of the latest ones in such situations quite often increases the risk of selling the product at an irrelevant price, which in turn can lead to a decrease in turnover or loss of funds.
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Bavar, Farhad, Majid Sabzehparvar, and Mona Ahmadi Rad. "Routing cross-docking depots, considering the time windows and pricing routes (case study: container transportation of Chabahar port)." Nexo Revista Científica 33, no. 02 (2020): 409–22. http://dx.doi.org/10.5377/nexo.v33i02.10780.
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In this study, we develop a model for routing cross-docking centers considering time windows and pricing routs. In this model picking and delivery in several times is permitted and each knot can be serviced by more than one vehicle. Every truck can transport one or more product, in other words, we consider compatibility between product and vehicle. This model includes two goals: reducing the total cost and reducing the cost of carrying goods (freight fare). The total cost includes the cost required to traverse between the points, the cost of traversing the routes between the central cross-docking center and the first points after moving, and the cost to traverse the routes between the last points in each route and the depots that must be minimized. In general, the purpose of the model is to obtain the number of cross-docking center, the number of vehicles and the best route in the distribution network. We present a nonlinear programming model for this problem. We have solved the proposed model by GAMS. As the dimensions of the problem increase, the implementation time of the program increases progressively. So, in order to solve the model in medium and large scales, we proposed a genetic meta-heuristic algorithm. The results of examining different issues by the meta-heuristic approach show the very high efficiency of the developed algorithms in terms of the solution time and the answer of the problem.
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M. Usman, Uju Suji'ah, and Muh. Nashirudin. "CRYPTOCURRENCY IN ISLAMIC LAW." Jurnal Multidisipliner Bharasa 1, no. 1 (2022): 45–56. http://dx.doi.org/10.56691/jurnalmultidisiplinerbharasa.v1i1.6.
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Virtual money is a product of specific cryptocurrency algorithms, where no particular institution or authority controls the circulation of this digital money or underlying assets for which there is no basis for pricing and consumer protection. By ceding the money system into the market, Cryptocurrencies require legality considerations. This research aims to explain cryptocurrency as a means of payment from the perspective of Islamic Law. This research uses a literature review or normative approach. The results showed that a Legal Vacuum or the empty laws governing Cryptocurrencies could potentially negatively impact. This is because there is the principle of haram li ghairihi, where something contains an element of uncertainty. In the Indonesian Ulema Council (MUI) perspective through Fatwa No. 116/DSN/-MUI/IX/2017, cryptocurrency is included in the concept of sharia maqashid due to uncertainty of containing element maysir (gambling).
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GASANOVA, Marina R. "An approach to building a financial model for the purposes of planning resource products of the corporate segment in commercial banks." Finance and Credit 28, no. 5 (2022): 1078–106. http://dx.doi.org/10.24891/fc.28.5.1078.
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Subject. The paper considers planning of resource products in the corporate business segment of a commercial bank. Objectives. The aim is to develop a model for planning financial results generated by attracted time deposit accounts and demand accounts of the corporate business segment in a commercial bank. Methods. I employ mathematical, statistical, and econometric methods and applied programming methods (SARIMA model for planning the profile of resource products of corporate clients, taking into account seasonality; the Ward's method for clustering corporate clients to create a pattern of financial behavior; SQL language for building uniquely designed models of resource product planning). Results. I identified the main drivers, algorithms and pricing principles of resource products. On their basis, I developed my own models for planning financial results from attracting time deposit accounts and demand accounts of the corporate business segment in a commercial bank. The uniquely designed planning models were tested on the basis of the medium-sized corporate business segment of one of the largest Russian commercial banks. Conclusions. The use of proprietary models enables to increase the accuracy of financial planning. The definition of a corporate client as the main driver contributes to the construction of a customer-oriented corporate customer service system, develops an incentive system, and allows to create additional incentives to increase cross-sales of banking products.
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Wu-Smith, Peiling, Philip T. Keenan, Jonathan H. Owen, et al. "General Motors Optimizes Vehicle Content for Customer Value and Profitability." INFORMS Journal on Applied Analytics 53, no. 1 (2023): 59–69. http://dx.doi.org/10.1287/inte.2022.1144.
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General Motors (GM) vehicles have more than 100 customer-facing features, known as vehicle content. Decisions about how to package and price these features have a significant impact on our customers’ experiences and on GM’s business results. Vehicle features are assigned as standard, optional, or unavailable on different trim levels, resulting in an enormous combinatorial solution space. Vehicle content optimization (VCO) combines customer market research, discrete choice models, and custom multiobjective nonlinear optimization algorithms to optimize vehicle contenting and pricing decisions. VCO comprehends complex dynamics and tradeoffs and allows GM to optimally balance customer preferences and profitability. After six years of development and multiple proof-of-concept and pilot studies, VCO was officially integrated into GM’s Global Vehicle Development Process in 2014. As of 2021, VCO has been used on more than 85 vehicle programs globally. It has enabled customer-centric product development and more efficient engineering, sourcing, and manufacturing. GM Finance verified that VCO enabled $4.4 billion of incremental profit over the average product life cycle (i.e., six years on average) since 2018, making it a vastly impactful example of operations research and applied analytics.
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Li, Jiliu, Zhixing Luo, Roberto Baldacci, Hu Qin, and Zhou Xu. "A New Exact Algorithm for Single-Commodity Vehicle Routing with Split Pickups and Deliveries." INFORMS Journal on Computing 35, no. 1 (2023): 31–49. http://dx.doi.org/10.1287/ijoc.2022.1249.
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We present a new exact algorithm to solve a challenging vehicle routing problem with split pickups and deliveries, named as the single-commodity split-pickup and split-delivery vehicle routing problem (SPDVRP). In the SPDVRP, any amount of a product collected from a pickup customer can be supplied to any delivery customer, and the demand of each customer can be collected or delivered multiple times by the same or different vehicles. The vehicle fleet is homogeneous with limited capacity and maximum route duration. This problem arises regularly in inventory and routing rebalancing applications, such as in bike-sharing systems, where bikes must be rebalanced over time such that the appropriate number of bikes and open docks are available to users. The solution of the SPDVRP requires determining the number of visits to each customer, the relevant portions of the demands to be collected from or delivered to the customers, and the routing of the vehicles. These three decisions are intertwined, contributing to the hardness of the problem. Our new exact algorithm for the SPDVRP is a branch-price-and-cut algorithm based on a pattern-based mathematical formulation. The SPDVRP relies on a novel label-setting algorithm used to solve the pricing problem associated with the pattern-based formulation, where the label components embed reduced cost functions, unlike those classical components that embed delivered or collected quantities, thus significantly reducing the dimension of the corresponding state space. Extensive computational results on different classes of benchmark instances illustrate that the newly proposed exact algorithm solves several open SPDVRP instances and significantly improves the running times of state-of-the-art algorithms. History: Accepted by Andrea Lodi, Area Editor for Design and Analysis of Algorithms–Discrete. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72222011, 71971090, 71821001, 72171112], by the Young Elite Scientists Sponsorship Program by CAST [Grant 2019QNRC001], and by the Research Grants Council of Hong Kong SAR, China [Grant 15221619]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/ijoc.2022.1249 .
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Cohen, Maxime C., Ilan Lobel, and Renato Paes Leme. "Feature-Based Dynamic Pricing." Management Science 66, no. 11 (2020): 4921–43. http://dx.doi.org/10.1287/mnsc.2019.3485.
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We consider the problem faced by a firm that receives highly differentiated products in an online fashion. The firm needs to price these products to sell them to its customer base. Products are described by vectors of features and the market value of each product is linear in the values of the features. The firm does not initially know the values of the different features, but can learn the values of the features based on whether products were sold at the posted prices in the past. This model is motivated by applications such as online marketplaces, online flash sales, and loan pricing. We first consider a multidimensional version of binary search over polyhedral sets and show that it has a worst-case regret which is exponential in the dimension of the feature space. We then propose a modification of the prior algorithm where uncertainty sets are replaced by their Löwner-John ellipsoids. We show that this algorithm has a worst-case regret which is quadratic in the dimension of the feature space and logarithmic in the time horizon. We also show how to adapt our algorithm to the case where valuations are noisy. Finally, we present computational experiments to illustrate the performance of our algorithm. This paper was accepted by Yinyu Ye, optimization.
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Malamud, Semyon, Eugene Trubowitz, and Mario V. Wüthrich. "Market Consistent Pricing of Insurance Products." ASTIN Bulletin 38, no. 02 (2008): 483–526. http://dx.doi.org/10.2143/ast.38.2.2033351.
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We present the first step in a program to develop a comprehensive, unified equilibrium theory of asset and liability pricing. We give a mathematical framework for pricing insurance products in a multiperiod financial market. This framework reflects classical economic principles (like utility maximization) and generates pricing algorithms for non-hedgeable insurance risks.
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Malamud, Semyon, Eugene Trubowitz, and Mario V. Wüthrich. "Market Consistent Pricing of Insurance Products." ASTIN Bulletin 38, no. 2 (2008): 483–526. http://dx.doi.org/10.1017/s0515036100015269.
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We present the first step in a program to develop a comprehensive, unified equilibrium theory of asset and liability pricing. We give a mathematical framework for pricing insurance products in a multiperiod financial market. This framework reflects classical economic principles (like utility maximization) and generates pricing algorithms for non-hedgeable insurance risks.
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Khairkar, Divya, Shreya Dambhare, Om Gupta, Vedant Thakre, Nilay Raut, and Prof S. N. Dagadkar. "E-Commerce Product Price Tracker using Dynamic Pricing Algorithm." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 216–22. http://dx.doi.org/10.22214/ijraset.2023.51478.
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Abstract: The increasing popularity of e-commerce has led to a growing demand for tools that help consumers track prices and make informed purchasing decisions. This paper presents an online price tracker that enables users to monitor the prices of products across multiple e-commerce websites in real-time. The tracker utilizes web scraping techniques to extract pricing data from various e-commerce platforms and presents it in a user-friendly interface. Users can set up custom alerts to receive notifications when the price of a product drops below a certain threshold, allowing them to take advantage of discounts and save money. The price tracker also provides historical price data for each product, enabling users to analyze trends and make more informed purchasing decisions. Overall, the online price tracker provides a valuable tool for consumers to make informed decisions when shopping online.
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Gao, Yun Jing, Lin Lin Zhou, and Hui Huang Pi. "Research on Durable Consumer Goods Manufacturer's Pricing Decision." Applied Mechanics and Materials 687-691 (November 2014): 4823–27. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.4823.
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The pricing decision of consumer goods manufacturing enterprise is a very important part in its business strategies. Price of the product is directly related to the ability to obtain the expected earnings. In this paper, based on the analysis of the characteristics of durable consumer goods and the factors that affect the price, considering the continuous product innovation and diversification of consumer demand, psychological factors of consumers, as well as the price timeliness, starting from the quantitative point of view, a dynamic pricing model of durable consumer goods aimed at the maximum value of products was set up. Finally, a numerical example solving by the genetic algorithm was given to verify the feasibility of the model.
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Ma, Yuhang, Paat Rusmevichientong, Mika Sumida, and Huseyin Topaloglu. "An Approximation Algorithm for Network Revenue Management Under Nonstationary Arrivals." Operations Research 68, no. 3 (2020): 834–55. http://dx.doi.org/10.1287/opre.2019.1931.
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Many revenue management problems require making capacity control and pricing decisions for multiple products. The decisions for the different products interact because either the products use a common pool of resources or the customers choose and substitute among the products. When pricing airline tickets, for example, different itinerary products use the capacities on common flight legs and the customers choose and substitute among different itinerary products that serve the same origin-destination pair. Finding the optimal capacity control and pricing decisions in such problems can be challenging because one needs to simultaneously consider the capacities available to serve a large pool of products. In “An Approximation Algorithm for Network Revenue Management under Nonstationary Arrivals,” Ma, Rusmevichientong, Sumida, and Topaloglu develop efficient methods to make decisions with performance guarantees in high-dimensional capacity control and pricing problems.
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Melnikov, Oleg. "Heuristic Rules for the Dynamic Pricing Problem." Organizations and Markets in Emerging Economies 14, no. 2(28) (2023): 436–57. http://dx.doi.org/10.15388/omee.2023.14.99.
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This paper is devoted to the development of heuristics for the dynamic pricing problem. A discrete time model of dynamic pricing on the fixed time horizon is proposed. It is applicable to products that satisfy two properties: 1) product value expires at a certain predetermined date, and 2) consumers demand at most a single unit of the product. This type of demand structure allows deriving a simple system of recursive equations for optimal prices using dynamic programming techniques. Optimal pricing policy is expressed as a function of time to expiration and inventory levels of unsold products. An analytical solution to this problem was obtained for special cases, while for the general case, a numerical algorithm has been developed. Qualitative characteristics of the optimal pricing policy are established, and their implications for dynamics of inventories and prices are discussed. Based on these observations, a simple heuristic rule for dynamic price adjustments is proposed. Performance of this heuristic is evaluated against the optimal dynamic and fixed-price policies using Monte-Carlo experiments. Results demonstrate high efficiency of the proposed heuristic strategy and its even simpler derivatives. Heuristics’ adaptability and ease of implementation should make it suitable and attractive for small and medium businesses.
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Guo, Pengfei, Zhaotong Lian, and Yulan Wang. "PRICING PERISHABLE PRODUCTS WITH COMPOUND POISSON DEMANDS." Probability in the Engineering and Informational Sciences 25, no. 3 (2011): 289–306. http://dx.doi.org/10.1017/s0269964811000027.
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We consider the dynamic pricing problem of perishable products in a system with a constant production rate. Potential demands arrive according to a compound Poisson process, and are price-sensitive. We carry out the sample path analysis of the inventory process and by using level-crossing method, we derive its stationary distribution given a pricing function. Based on the distribution, we express the average profit function. By a stochastic comparison approach, we characterize the pricing strategy given different customers willingness-to-pay functions. Finally, we provide an approximation algorithm to calculate the optimal pricing function.
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Zou, Jianfeng, and Hui Li. "Precise Marketing of E-Commerce Products Based on KNN Algorithm." Computational Intelligence and Neuroscience 2022 (August 11, 2022): 1–12. http://dx.doi.org/10.1155/2022/4966439.
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In order to better understand the purchase decision-making process of consumers, this paper makes an in-depth study on the precision marketing of e-commerce products on the basis of KNN algorithm. Through data mining, classic KNN algorithm, BPNN algorithm, and other methods, this paper takes the price and purchase intention of e-commerce agricultural products as an example. Based on the classic nearest neighbor algorithm, binomial function is combined with Euclidean distance formula when calculating the nearest neighbor through similarity. The particle swarm optimization algorithm is used to optimize the binomial function coefficient and the K value of the nearest neighbor algorithm, and the results of the best prediction model for the prediction application of e-commerce agricultural product price and purchase intention are established. Both pricing strategies and promotion strategies will weaken the compromise effect of consumers when they choose e-commerce agricultural products. After studying the calculation method of the KNN algorithm, it not only correctly predicts the price of e-commerce agricultural products but also makes a corresponding prediction and analysis of consumers’ purchase intention of e-commerce agricultural products, with the highest accuracy of 94.2%. At the same time, in the future precision marketing process, e-commerce agricultural products enterprises use data technology to achieve precision marketing, which effectively changes the shortcomings of traditional marketing and improves the product marketing effect and economic benefits.
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TADJOUDDINE, EMMANUEL M. "MODELING AND SIMULATION OF SEQUENTIAL AUCTIONS: PRICING AND CALIBRATION ALGORITHMS." International Journal of Modeling, Simulation, and Scientific Computing 03, no. 03 (2012): 1250009. http://dx.doi.org/10.1142/s1793962312500092.
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We consider sequential auctions wherein seller and bidder agents need to price goods on sale at the 'right' market price. We propose algorithms based on a binomial model for both the seller and buyer. Then, we consider the problem of calibrating pricing models to market data. To this end, we studied a stochastic volatility model used for option pricing, derived, and analyzed Monte Carlo estimators for computing the gradient of a certain payoff function using Finite Differencing and Algorithmic Differentiation. We then assessed the accuracy and efficiency of both methods as well as their impacts into the optimization algorithm. Numerical results are presented and discussed. This work can benefit those engaged in electronic trading or investors in financial products with the need for fast and more precise predictions of future market data.
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Raza, Syed Asif. "Optimal decisions on fencing, pricing, and selection of process mean in imperfectly segmented markets." RAIRO - Operations Research 54, no. 6 (2020): 1573–92. http://dx.doi.org/10.1051/ro/2019115.
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This paper integrates the selection of a process mean, production and marketing decisions at a firm’s level. We discussed a manufacturing firm’s problem that integrates its manufacturing decisions on production quantities and selection of a process mean with marketing decisions. The marketing decisions include setting prices, and the fencing investment to mitigate the effect of demand leakages between market segments. The manufacturing firm yields products of varied quality based on a single quality characteristic (e.g., amount of fill). The firm operates in a monopoly, and manufacturing process is assumed to follow a normal distribution, and therefore, it produces multi-grade (class) products distinct in their single quality characteristic. Depending upon the quality characteristic, a product with quality characteristic equal to greater than the upper specification limit is classified as grade 1 product, and sold in primary market at a full price. When the quality characteristic falls between the lower and the upper specification limits, it is referred to as a grade 2 product, and sold in a secondary market at a discounted price. Any product with a quality characteristic lower than the lower specification limit is reworked at an additional cost. A 100% error-free inspection is conducted to segregate the products at a negligible cost. Unlike many related studies in literature, this research proposes a novel integration of the pricing and production quantity decisions along with the process targeting in the two markets with pricing decision in the presence of demand leakages due to cross-elasticity. Furthermore, it is assumed that the firm can mitigate the demand leakage at an additional investment on improving fencing. Thus, the firm’s optimal decision would also include the pricing in each market segment, and fencing investment along with its decision on the production quantity for each product class. Mathematical models are developed to address this problem assuming the price-dependent stochastic demand. Structural properties of these models are explored and efficient heuristic solution methodologies are developed. Later, we also developed models when the stochastic demand information is only partially known, and proposed Harmony Search algorithm on the problem. Numerical experimentation is reported to highlight the importance of the proposed integrated framework and the impact of the problem related parameters on a firm’s profitability and its integrated optimal control decisions on selection of a process mean, pricing, production quantity, and fencing investment.
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Weber, Bradley C. "Hub-and-Spoke Conspiracies: Can Big Data and Pricing Algorithms Form the Rim?" SMU Science and Technology Law Review 26, no. 1 (2023): 25. http://dx.doi.org/10.25172/smustlr.26.1.4.
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A hub-and-spoke conspiracy is a metaphor used to describe an antitrust cartel that includes a firm at one level of a supply chain—such as a buyer or supplier—who acts like the “hub” of a wheel. Vertical agreements up or down the supply chain act as the “spokes,” and a horizontal agreement among the spokes acts as the “rim” of the wheel. Courts have considered hub-and-spoke conspiracies for more than eighty years, and there is large body of case law that pertains to the evidence that is necessary for proving this type of antitrust conspiracy. With the rise of modern digital technologies, firms have increased their reliance on “big data” and data-driven pricing algorithms to determine the prices for their products and services. Using big data, current pricing algorithms can quickly monitor market conditions, including the behavior of rival competitors, and adjust prices in near real-time. Analytical pricing tools that adjust prices based on supply-and-demand conditions and/or costs can create procompetitive benefits because they have the potential to increase efficiency. Anticompetitive effects can occur, however, when multiple competitors use the same pricing algorithm and data set supplied by a common service provider who acts as a hub. This article will explain the history, structure, and characteristics of hub-and-spoke conspiracies. It also will discuss agreements among competitors to exchange confidential price information, which itself can result in antitrust violations under certain circumstances. Finally, the article will summarize and comment on a new wave of antitrust lawsuits that allege hub-and-spoke conspiracies based on competitors’ mutual use of big data and algorithms to set prices.
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Taleizadeh, Ata Allah, Shima Rezvan Beydokhti, Leopoldo Eduardo Cárdenas-Barrón, and Somayeh Najafi-Ghobadi. "Pricing of Complementary Products in Online Purchasing under Return Policy." Journal of Theoretical and Applied Electronic Commerce Research 16, no. 5 (2021): 1718–39. http://dx.doi.org/10.3390/jtaer16050097.
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In online purchasing, customers may return products due to dissatisfaction with the quality of the product, and receive a refund based on the return policy, which is determined by online distributors. Online distributors can offer generous policies to attract more customers, but at the cost of reducing total profits. In this paper, the effect of the pricing and quality of complementary products (products sold together with other items) in online selling under the return policy is investigated. For this purpose, a mathematical model is developed to obtain optimal values for selling price, refund amount, and quality of products. Based on analytical results, a solution algorithm is proposed to solve the numerical examples and perform sensitivity analysis. Findings reveal that, while increasing the sensitivity of demand with respect to the refund amount, the price, quality, and refund on returned products should be increased. In addition, the online distributor should increase the quality of products when customers are more sensitive to the quality of products. Among other results, the selling price is shown to be negatively affected by demand elasticity with respect to price. In this situation, the online distributor should reduce the quality level and the refund amount for returned products to avoid a sharp decline in profit. In addition, when the quality cost is high, the price and quality should be decreased and the refund amount unchanged.
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Schiek, William A., and Emerson M. Babb. "Impact of Reverse Osmosis on Southeast Milk Markets." Journal of Agricultural and Applied Economics 21, no. 2 (1989): 63–75. http://dx.doi.org/10.1017/s0081305200001187.
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AbstractThe Southeast is a net importer of milk and milk products. Milk must be imported from other regions at certain times of the year. Reverse osmosis (RO) is a new processing technology which could significantly reduce milk transportation costs between regions by removing half the water from raw milk prior to shipment. A network flow algorithm, which incorporates federal milk orders and solves for the least cost procurement pattern, was used to assess the impact of RO on southeast milk marketing orders under alternative raw product pricing scenarios.
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Kang, Zheng, and Hui Liu. "Social Welfare-Based Task Assignment in Mobile Crowdsensing." International Journal of Information Technologies and Systems Approach 16, no. 3 (2023): 1–28. http://dx.doi.org/10.4018/ijitsa.326134.
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Mobile crowdsensing (MCS) is a novel data-collection paradigm in the internet of things. Social welfare is an important factor in the task allocation because it integrates the interests of all parties involved in MCS and represents societal satisfaction. The ultimate goal of task allocation is to maximize social welfare as much as possible. Existing social welfare optimization research does not consider the moral and psychological characteristics of people in the real world. In this study, the real-world situation is considered. A task allocation strategy, which includes two stages, is formulated for task allocation. A generalized shortest path algorithm and an optimal pricing algorithm are proposed for each stage. To evaluate the proposed algorithms, extensive simulation experiments are conducted on two real-world datasets. The experimental results demonstrate that the proposed algorithms produce the desired effects, and the proposed strategy significantly increases social welfare by 19% compared to another method.
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Hu, Kefan. "Pricing and inventory Decision Optimization under subscription mode in Supply chain management." E3S Web of Conferences 372 (2023): 02004. http://dx.doi.org/10.1051/e3sconf/202337202004.
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This paper investigates the current situation of subscription mode, applies it to the joint management of pricing and inventory in supply chain management, builds the product pricing and inventory model under subscription mode, and verifies the model derivation and results based on the joint decision model of pricing and inventory, and simulates the decision-making process of the model by computer programming. By comparing the difference between the theoretical model of joint decision of pricing and inventory under uncertain supply and demand conditions and the model of joint decision of pricing and inventory under subscription mode, analyzing the influencing factors and exploring the optimization method of product pricing and inventory model algorithm under subscription mode. The results show that: (1) in supply chain management, it is necessary to solve the problem of supply and demand balance through effective joint decision of pricing and inventory, and effectively improve the competitiveness of logistics enterprises.(2) The introduction of subscription mode in the joint management of pricing and inventory can reduce the uncertainty of both sides, so as to better solve the problem of inventory replenishment strategy and pricing decision under the condition of supply and demand uncertainty than the traditional decision-making model, so as to achieve the coordination of supply chain under the uncertain environment.
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Wu, Xiang, and Jinlong Zhang. "Joint Ordering and Pricing Decisions for New Repeat-Purchase Products." Discrete Dynamics in Nature and Society 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/461959.
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This paper studies ordering and pricing problems for new repeat-purchase products. We incorporate the repeat-purchase rate and price effects into the Bass model to characterize the demand pattern. We consider two decision models: (1) two-stage decision model, in which the sales division chooses a price to maximize the gross profit and the purchasing division determines an optimal ordering decision to minimize the total cost under a given demand subsequently, and (2) joint decision model, in which the firm makes ordering and pricing decisions simultaneously to maximize the profit. We combine the generalized Bass model with dynamic lot sizing model to formulate the joint decision model. We apply both models to a specific imported food provided by an online fresh produce retailer in Central China, solve them by Gaussian Random-Walk and Wagner-Whitin based algorithms, and observe three results. First, joint pricing and ordering decisions bring more significant profits than making pricing and ordering decisions sequentially. Second, a great initiative in adoption significantly increases price premium and profit. Finally, the optimal price shows a U-shape (i.e., decreases first and increases later) relationship and the profit increases gradually with the repeat-purchase rate when it is still not very high.
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Yao, Liangyong, Yan Lin, Yalun Mo, and Feng Wang. "Performance Evaluation of Financial Industry Related Expense Forecasting Using Various Regression Algorithms for Machine Learning." Highlights in Science, Engineering and Technology 57 (July 11, 2023): 235–41. http://dx.doi.org/10.54097/hset.v57i.10007.
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Insurance costs refer to the fees charged by insurance companies to customers to pay for possible risks and losses. Insurance costs are usually based on the personal information of the insured, such as age, gender, occupation, health status and so on. For insurance companies, it is very important to accurately predict insurance costs, because it is directly related to the company's profits and risk control capabilities. The purpose of using regression algorithm to predict insurance expenses is to make insurance companies evaluate customers' risks more accurately and make more reasonable insurance expenses, so as to better manage risks and improve the company's profitability. In addition, for individuals, knowing their own insurance cost forecast results will also help them make better decisions and choose the most suitable insurance products to protect themselves and their families.In order to improve the pricing accuracy and profit rate of insurance companies, this study uses regression algorithm to predict insurance costs. It uses real anonymous data sets, which contain information of the insured from different regions, different ages, different sexes and different smoking status. It uses the comparison algorithm function of regression algorithm, which contains dozens of algorithms and covers all regression algorithms and compare their prediction performance. Our data set takes into account various factors that affect the insurance cost, such as age, gender, body mass index, smoking status and so on. And add them to the model as independent variables. It uses cross-validation to evaluate the generalization ability of the model and R2 index to evaluate the prediction performance. The results show that GBR has the best prediction performance, with R2 of 87%. Our research provides an accurate method for insurance companies to predict insurance costs, which is helpful for insurance companies to formulate more reasonable pricing strategies and improve market competitiveness.
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Hu, Zhengbiao, Dongfeng He, Wei Song, and Kai Feng. "Model and Algorithm for Planning Hot-Rolled Batch Processing under Time-of-Use Electricity Pricing." Processes 8, no. 1 (2020): 42. http://dx.doi.org/10.3390/pr8010042.
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Batch-type hot rolling planning highly affects electricity costs in a steel plant, but previous research models seldom considered time-of-use (TOU) electricity pricing. Based on an analysis of the hot-rolling process and TOU electricity pricing, a batch-processing plan optimization model for hot rolling was established, using an objective function with the goal of minimizing the total penalty incurred by the differences in width, thickness, and hardness among adjacent slabs, as well as the electricity cost of the rolling process. A method was provided to solve the model through improved genetic algorithm. An analysis of the batch processing of the hot rolling of 240 slabs of different sizes at a steel plant proved the effectiveness of the proposed model. Compared to the man–machine interaction model and the model in which TOU electricity pricing was not considered, the batch-processing model that included TOU electricity pricing produced significantly better results with respect to both product quality and power consumption.
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Lee, Chaeyoung, Jisang Lyu, Eunchae Park, et al. "Super-Fast Computation for the Three-Asset Equity-Linked Securities Using the Finite Difference Method." Mathematics 8, no. 3 (2020): 307. http://dx.doi.org/10.3390/math8030307.
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In this article, we propose a super-fast computational algorithm for three-asset equity-linked securities (ELS) using the finite difference method (FDM). ELS is a very popular investment product in South Korea. There are one-, two-, and three-asset ELS. The three-asset ELS is the most popular financial product among them. FDM has been used for pricing the one- and two-asset ELS because it is accurate. However, the three-asset ELS is still priced using the Monte Carlo simulation (MCS) due to the curse of dimensionality for FDM. To overcome the limitation of dimension for FDM, we propose a systematic non-uniform grid with an explicit Euler scheme and an optimal implementation of the algorithm. The computational time is less than 6 s. We perform standard ELS option pricing and compare the results from the fast FDM with the ones from MCS. The computational results confirm the superiority and practicality of the proposed algorithm.
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Chen, Yong-Tong, and Zhong-Chen Cao. "An Investigation on a Closed-Loop Supply Chain of Product Recycling Using a Multi-Agent and Priority Based Genetic Algorithm Approach." Mathematics 8, no. 6 (2020): 888. http://dx.doi.org/10.3390/math8060888.
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Product recycling issues have gained increasing attention in many industries in the last decade due to a variety of reasons driven by environmental, governmental and economic factors. Closed-loop supply chain (CLSC) models integrate the forward and reverse flow of products. Since the optimization of these CLSC models is known to be NP-Hard, competition on optimization quality in terms of solution quality and computational time becomes one of the main focuses in the literature in this area. A typical six-level closed-loop supply chain network is examined in this paper, which has great complexity due to the high level of echelons. The proposed solution uses a multi-agent and priority based approach which is embedded within a two-stage Genetic Algorithm (GA), decomposing the problem into (i) product flow, (ii) demand allocation and (iii) pricing bidding process. To test and demonstrate the optimization quality of the proposed algorithm, numerical experiments have been carried out based on the well-known benchmarking network. The results prove the reliability and efficiency of the proposed approach compared to LINGO and the benchmarking algorithm discussed in the literature.
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Du, Yu-Ang. "Research on the Route Pricing Optimization Model of the Car-Free Carrier Platform Based on the BP Neural Network Algorithm." Complexity 2021 (June 23, 2021): 1–10. http://dx.doi.org/10.1155/2021/8204214.
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The car-free carrier platform is a product of the rapid development of the modern logistics industry and has a vital strategic value for promoting the construction of a country’s comprehensive transportation. However, due to the unreasonable platform pricing model, the industry is currently in a bottleneck period. In order to solve this problem, we established a gray correlation model to calculate the degree of correlation between each characteristic index and platform pricing based on the massive historical transaction data of a certain platform and performed K-means clustering on the results to discover the main factors affecting platform pricing. Based on the abovementioned results, we created a pricing optimization model based on the BP neural network, with the structure of 8-13-1 to predict the freight pricing of the order and test the prediction results. The test shows that the goodness of fit (R2) of the predicted value is close to 1, and the prediction error range is less than 3.7%, which proves the accuracy and effectiveness of the BP neural network model and provides an effective reference for the optimization of the pricing model of the car-free carrier platform.
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Xiong, Yu, Gendao Li, and Kiran Jude Fernandes. "Dynamic pricing model and algorithm for perishable products with fuzzy demand." Applied Stochastic Models in Business and Industry 26, no. 6 (2010): 758–74. http://dx.doi.org/10.1002/asmb.816.
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Berry, Michael David, and John Sessions. "A Forest-to-Product Biomass Supply Chain in the Pacific Northwest, USA: A Multi-Product Approach." Applied Engineering in Agriculture 34, no. 1 (2018): 109–24. http://dx.doi.org/10.13031/aea.12384.
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Abstract. A comprehensive biomass supply chain landscape model is presented to provide an analysis of transportable biomass conversion facility design and evaluate its potential economic viability. This study focuses on the generation of a tactical-based landscape model to optimize biomass extraction, transportation, conversion and product production within a market system. The model considers various pathways including supply options at landings (burn, grind, chip, bale), centralized landings (grind/chip), biomass conversion facilities (biochar, briquettes, torrefied wood) and delivery to final market. The model solves a multi-period, multi commodity, multi-echelon combinatorial problem to maximize net present value using a genetic algorithm. The landscape is evaluated over a one year planning horizon with monthly time steps simulating a transportable conversion facility mobilization cycle. A hypothetical biochar facility located in Lakeview, Oregon was used as a case study. A sequence of scenarios are used to vary system inputs (logistics, product pricing and moisture management strategies) to put bounds around system viability. The results provide an economic framework to view the Pacific Northwest forest harvest residues processing, conversion and transportation supply chain options. System viability is largely dependent on market pricing, plant assumptions and conversion estimates while processing and transportation logistics are smaller, but important contributors for small scale biomass conversion faculty design configurations. Keywords: Biomass supply, Biomass products, Facility location, Tactical planning, Transportable plants.
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A.M, Korneev, and Abdullakh L.S. "Analysis of Economic Indicators of Complex Production Processes." International Journal of Engineering & Technology 7, no. 3.5 (2018): 7. http://dx.doi.org/10.14419/ijet.v7i3.5.15189.
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The article describes the methodology for describing the economic indicators of management effectiveness and decision-making under conditions of complex multi-stage productions. The algorithm and the forecast model of the need for production resources are presented, that allow providing more complete information on costs and help in pricing for various products, significantly reducing the response time to economic and technological situation changes. Characteristics of technology parameters are linked to a multi-stage production process. As the semi-finished product passes through the processing stages, the values of the technological factors are fixed. Methods for estimating the influence of parameters of complex spatially-distributed systems on costs are presented. Important elements of costs that affect the product value are determined. Detailing the cost elements for the technological operations under study is carried out, the boundaries, where the largest amount of resources is spent, are determined.
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A.M, Korneev, and Abdullakh L.S. "Analysis of Economic Indicators of Complex Production Processes." International Journal of Engineering & Technology 7, no. 3.5 (2018): 51. http://dx.doi.org/10.14419/ijet.v7i3.5.15201.
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The article describes the methodology for describing the economic indicators of management effectiveness and decision-making under conditions of complex multi-stage productions. The algorithm and the forecast model of the need for production resources are presented, that allow providing more complete information on costs and help in pricing for various products, significantly reducing the response time to economic and technological situation changes. Characteristics of technology parameters are linked to a multi-stage production process. As the semi-finished product passes through the processing stages, the values of the technological factors are fixed. Methods for estimating the influence of parameters of complex spatially-distributed systems on costs are presented. Important elements of costs that affect the product value are determined. Detailing the cost elements for the technological operations under study is carried out, the boundaries, where the largest amount of resources is spent, are determined.
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Zheng, Jiangbo, Yanhong Gan, Ying Liang, Qingqing Jiang, and Jiatai Chang. "Joint Strategy of Dynamic Ordering and Pricing for Competing Perishables with Q-Learning Algorithm." Wireless Communications and Mobile Computing 2021 (March 13, 2021): 1–19. http://dx.doi.org/10.1155/2021/6643195.
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We use Machine Learning (ML) to study firms’ joint pricing and ordering decisions for perishables in a dynamic loop. The research assumption is as follows: at the beginning of each period, the retailer prices both the new and old products and determines how many new products to order, while at the end of each period, the retailer decides how much remaining inventory should be carried over to the next period. The objective is to determine a joint pricing, ordering, and disposal strategy to maximize the total expected discounted profit. We establish a decision model based on Markov processes and use the Q-learning algorithm to obtain a near-optimal policy. From numerical analysis, we find that (i) the optimal number of old products carried over to the next period depends on the upper quantitative bound for old inventory; (ii) the optimal prices for new products are positively related to potential demand but negatively related to the decay rate, while the optimal prices for old products have a positive relationship with both; and (iii) ordering decisions are unrelated to the quantity of old products. When the decay rate is low or the variable ordering cost is high, the optimal orders exhibit a trapezoidal decline as the quantity of new products increases.
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Sun, Yiyao, and Shiqin Liu. "Interest-Rate Products Pricing Problems with Uncertain Jump Processes." Discrete Dynamics in Nature and Society 2021 (June 19, 2021): 1–8. http://dx.doi.org/10.1155/2021/7398770.
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Uncertain differential equations (UDEs) with jumps are an essential tool to model the dynamic uncertain systems with dramatic changes. The interest rates, impacted heavily by human uncertainty, are assumed to follow UDEs with jumps in ideal markets. Based on this assumption, two derivatives, namely, interest-rate caps (IRCs) and interest-rate floors (IRFs), are investigated. Some formulas are presented to calculate their prices, which are of too complex forms for calculation in practice. For this reason, numerical algorithms are designed by using the formulas in order to compute the prices of these structured products. Numerical experiments are performed to illustrate the effectiveness and efficiency, which also show the prices of IRCs are strictly increasing with respect to the diffusion parameter while the prices of IRFs are strictly decreasing with respect to the diffusion parameter.
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Liu, Zhengyang, Liang Shan, and Zihe Wang. "Optimal Pricing Schemes for Identical Items with Time-Sensitive Buyers." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 5 (2023): 5773–80. http://dx.doi.org/10.1609/aaai.v37i5.25716.
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Time or money? That is a question! In this paper, we consider this dilemma in the pricing regime, in which we try to find the optimal pricing scheme for identical items with heterogenous time-sensitive buyers. We characterize the revenue-optimal solution and propose an efficient algorithm to find it in a Bayesian setting. Our results also demonstrate the tight ratio between the value of wasted time and the seller's revenue, as well as that of two common-used pricing schemes, the k-step function and the fixed pricing. To explore the nature of the optimal scheme in the general setting, we present the closed forms over the product distribution and show by examples that positive correlation between the valuation of the item and the cost per unit time could help increase revenue. To the best of our knowledge, it is the first step towards understanding the impact of the time factor as a part of the buyer cost in pricing problems, in the computational view.