SIMULATION OF INVENTORY POLICY FOR PRODUCT WITH PRICE AND TIME-DEPENDENT DEMAND FOR DETERIORATING ITEM

2012 ◽  
Vol 03 (01) ◽  
pp. 1150001 ◽  
Author(s):  
D. SHUKLA ◽  
U. K. KHEDLEKAR ◽  
R. P. S. CHANDEL ◽  
S. BHAGWAT
Keyword(s):  
Inventory Management ◽  
Dynamic Pricing ◽  
Optimal Number ◽  
Pricing Policy ◽  
Data Set ◽  
Maximum Profit ◽  
Net Profit ◽  
Recurrence Method ◽  
Theoretical Results ◽  
Dependent Demand

In a declining market for goods, we optimize the net profit in business when inventory management allows change in the selling prices n times over time horizon. We are computing optimal number of changes in prices, respective optimal prices, and optimal profit in each of the cycle for a deteriorating product. This paper theoretically proves that for any business setup there exists an optimal number of price settings for obtaining maximum profit. Theoretical results are supported by numerical examples for different setups (data set) and it is found that for every setup the dynamic pricing policy outperforms the static pricing policy. In our model, the deterioration factor has been taken into consideration. The deteriorated units are determined by the recurrence method. Also we studied the effect of different parameters on optimal policy with simulation. For managerial purposes, we have provided some "suggested intervals" for choosing parameters depending upon initial demand, which help to predict the best prices and arrival of customers (demand).

scholarly journals Dynamic Pricing Policy Using Preservation Technology with Stock and Price Sensitive Demand

2020 ◽  
Vol 26 (3) ◽  
pp. 266-274
Author(s):  
Uttam Kumar Khedlekar ◽  
Priyanka Singh ◽  
Neelesh Gupta
Keyword(s):  
Dynamic Pricing ◽  
Policy Decision ◽  
Pricing Strategy ◽  
Selling Price ◽  
Market Demand ◽  
Pricing Policy ◽  
Preservation Technology ◽  
Proposed Model ◽  
Stock Policy ◽  
Dependent Demand

This paper aims to develop a dynamic pricing policy for deteriorating items with price and stock dependent demand. In declining market demand of items decreases with respect to time and also after a duration items get outdated. In this situation it needs a pricing policy to sale the items before end season. The proposed dynamic pricing policy is applicable for a limited period to clease the stock. Policy decision regarding the selling price could aggressively attracts the costumers. Objectives are to maximize the prot/revenue, pricing strategy and economic order level for such a stock dependent and price sensitive items. We are giving numerical example and simulation to illustrate the proposed model.

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

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

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

scholarly journals Functions of several variables analysis applied in inventory management

2013 ◽  
Vol 61 (7) ◽  
pp. 2221-2227 ◽  
Author(s):  
Martina Janková ◽  
Veronika Novotná ◽  
Tereza Varyšová
Keyword(s):  
Inventory Management ◽  
Differential Calculus ◽  
Maximum Term ◽  
Optimal Price ◽  
Several Variables ◽  
Analysis And Synthesis ◽  
Delay In Payments ◽  
Definite Period ◽  
Theoretical Results ◽  
Dependent Demand

In many cases a retailer is not capable of settling an invoice immediately upon receiving it and is given an option by the supplier to settle the invoice within a definite period. The retailer can sell the goods before the deadline, accumulate revenue and earn interest. If the retailer is not able to meet his obligations within the deadline, he is charged an interest. This paper introduces a newly constructed model which enables a retailer to set an optimal price of goods under permissible delay in payments, and to determine the maximum term of payment. The model is based on the assumption of time-dependent demand and has been developed for non-deteriorating goods. The paper further analyzes a situation in which the retailer sell all the goods in time, and a situation in which the deadline was not met. Theoretical results are demonstrated by an illustrative example. The authors of the paper used methods of analysis and synthesis, and the method of mathematical analysis (differential calculus of multivariable functions, solution of ordinary differential equations).The model suggested in the paper can be expanded in the future. One option is generalization of the model, allowing for the lack of goods, bulk discounts, etc.

Personalized Dynamic Pricing with Machine Learning: High-Dimensional Features and Heterogeneous Elasticity

2021 ◽  
Author(s):  
Gah-Yi Ban ◽  
N. Bora Keskin
Keyword(s):  
Dynamic Pricing ◽  
Simulated Data ◽  
The United States ◽  
Model Parameters ◽  
Data Sets ◽  
Demand Model ◽  
Pricing Policy ◽  
Data Set ◽  
Customized Pricing ◽  
The Individual

We consider a seller who can dynamically adjust the price of a product at the individual customer level, by utilizing information about customers’ characteristics encoded as a d-dimensional feature vector. We assume a personalized demand model, parameters of which depend on s out of the d features. The seller initially does not know the relationship between the customer features and the product demand but learns this through sales observations over a selling horizon of T periods. We prove that the seller’s expected regret, that is, the revenue loss against a clairvoyant who knows the underlying demand relationship, is at least of order [Formula: see text] under any admissible policy. We then design a near-optimal pricing policy for a semiclairvoyant seller (who knows which s of the d features are in the demand model) who achieves an expected regret of order [Formula: see text]. We extend this policy to a more realistic setting, where the seller does not know the true demand predictors, and show that this policy has an expected regret of order [Formula: see text], which is also near-optimal. Finally, we test our theory on simulated data and on a data set from an online auto loan company in the United States. On both data sets, our experimentation-based pricing policy is superior to intuitive and/or widely-practiced customized pricing methods, such as myopic pricing and segment-then-optimize policies. Furthermore, our policy improves upon the loan company’s historical pricing decisions by 47% in expected revenue over a six-month period. This paper was accepted by Noah Gans, stochastic models and simulation.

scholarly journals KOMBINASI PRODUKSI OPTIMAL PADA PRODUK UD. SERAYU PEJATEN TABANAN

2021 ◽  
Vol 10 (8) ◽  
pp. 759
Author(s):  
I Putu Pujanam Surya Buana ◽  
Ni Ketut Purnawati
Keyword(s):  
Linear Programming ◽  
Optimal Number ◽  
Production Optimization ◽  
Net Income ◽  
Optimal Production ◽  
Maximum Profit ◽  
Net Profit ◽  
Before And After ◽  
Production Strategy ◽  
Programming Analysis

Production planning is series of activities to determine a production strategy to meet consumer demand. An optimal production resulting in maximum profit. The purpose of this study is to determine the optimal production combination at UD. Serayu in Pejaten Village, Kerambitan District, Tabanan Regency. This study uses linear programming analysis and analysis of calculating business net income. Based on the results of linear programming analysis using POMQM for Windows, the company's optimal production combination are 30,000 units, Terracotta Bricks 4,536 units, Pressed Roof Tile 24,600 units and Bubungan (roof) 20,400 units. The net profit generated for one month is Rp. 71,208,038.02 while the net profit generated by producing the optimal number of product combinations is Rp. 75,849,726.02. Linear programming analysis helps companies determine the optimal production combination for limited resources and analysis of net profit helps to compare operating profits before and after the optimal production combination. Keywords: production optimization, linear programming, maximum profit

Optimal dynamic pricing, preservation technology investment and periodic ordering policies for agricultural products

2019 ◽  
Vol 53 (3) ◽  
pp. 731-747 ◽  
Author(s):  
Jing Lu ◽  
Jianxiong Zhang ◽  
Xinyun Jia ◽  
Guowei Zhu
Keyword(s):  
Inventory Management ◽  
Dynamic Pricing ◽  
Optimal Strategies ◽  
Solution Procedure ◽  
Agricultural Products ◽  
Pricing Policy ◽  
Technology Investment ◽  
Deterioration Rate ◽  
Preservation Technology ◽  
Preservation Technology Investment

This paper focuses on the inventory management of agricultural products, a specific type of perishable items carrying the deterioration property. In practice, the deterioration rate of agricultural products is varying with time and can be slowed downviainvesting in the preservation technology. This objective of this paper is to maximize the firm’s total profit per unit time by simultaneously determining dynamic pricing, replenishment cycle length, replenishment quantity and preservation technology investment. We first derive pricing policy by solving a dynamic optimization problem and then propose a solution procedure to obtain the optimal strategies that maximize profit. Furthermore, numerical examples and sensitivity analysis are conducted to gain more managerial insights. We find that the firm should take a penetration pricing policy. In addition, if the shelf life of products is very long, the firm should not take preservation technology investment. When the unit holding cost is relatively small or the unit purchasing cost is relatively large, the firm should increase preservation technology investment.

scholarly journals Dynamic pricing with demand disaggregation for hotel revenue management

2021 ◽  
Author(s):  
Andrei M. Bandalouski ◽  
Natalja G. Egorova ◽  
Mikhail Y. Kovalyov ◽  
Erwin Pesch ◽  
S. Armagan Tarim
Keyword(s):  
Dynamic Pricing ◽  
Programming Model ◽  
Computer Experiments ◽  
Price Policy ◽  
Linear Constraints ◽  
Economic Consequences ◽  
Elastic Model ◽  
Computationally Efficient ◽  
Data Set ◽  
Dynamic Price

AbstractIn this paper we present a novel approach to the dynamic pricing problem for hotel businesses. It includes disaggregation of the demand into several categories, forecasting, elastic demand simulation, and a mathematical programming model with concave quadratic objective function and linear constraints for dynamic price optimization. The approach is computationally efficient and easy to implement. In computer experiments with a hotel data set, the hotel revenue is increased by about 6% on average in comparison with the actual revenue gained in a past period, where the fixed price policy was employed, subject to an assumption that the demand can deviate from the suggested elastic model. The approach and the developed software can be a useful tool for small hotels recovering from the economic consequences of the COVID-19 pandemic.

SEMIPARAMETRIC CLUSTERING METHOD FOR MICROARRAY DATA ANALYSIS

2008 ◽  
Vol 06 (02) ◽  
pp. 261-282 ◽  
Author(s):  
AO YUAN ◽  
WENQING HE
Keyword(s):  
Data Analysis ◽  
Microarray Data ◽  
Mixture Distribution ◽  
Information Criterion ◽  
Optimal Number ◽  
Microarray Data Analysis ◽  
Parametric Methods ◽  
Clustering Methods ◽  
Microarray Gene Expression ◽  
Data Set

Clustering is a major tool for microarray gene expression data analysis. The existing clustering methods fall mainly into two categories: parametric and nonparametric. The parametric methods generally assume a mixture of parametric subdistributions. When the mixture distribution approximately fits the true data generating mechanism, the parametric methods perform well, but not so when there is nonnegligible deviation between them. On the other hand, the nonparametric methods, which usually do not make distributional assumptions, are robust but pay the price for efficiency loss. In an attempt to utilize the known mixture form to increase efficiency, and to free assumptions about the unknown subdistributions to enhance robustness, we propose a semiparametric method for clustering. The proposed approach possesses the form of parametric mixture, with no assumptions to the subdistributions. The subdistributions are estimated nonparametrically, with constraints just being imposed on the modes. An expectation-maximization (EM) algorithm along with a classification step is invoked to cluster the data, and a modified Bayesian information criterion (BIC) is employed to guide the determination of the optimal number of clusters. Simulation studies are conducted to assess the performance and the robustness of the proposed method. The results show that the proposed method yields reasonable partition of the data. As an illustration, the proposed method is applied to a real microarray data set to cluster genes.

Design Chaotic Neural Network from Discrete Time Feedback Function

2013 ◽  
Vol 339 ◽  
pp. 366-371
Author(s):  
Jin Sheng Ren ◽  
Guang Chun Luo ◽  
Ke Qin
Keyword(s):  
Discrete Time ◽  
Design Methodology ◽  
Universal Design ◽  
Jacobian Matrix ◽  
Sufficient Conditions ◽  
Neural Net ◽  
Chaotic Neural Network ◽  
Data Set ◽  
Dominant Matrix ◽  
Theoretical Results

The goal of this paper is to give a universal design methodology of a Chaotic Neural Net-work (CNN). By appropriately choosing self-feedback, coupling functions and external stimulus, we have succeeded in proving a dynamical system defined by discrete time feedback equations possess-ing interesting chaotic properties. The sufficient conditions of chaos are analyzed by using Jacobian matrix, diagonal dominant matrix and Lyapunov Exponent (LE). Experiments are also conducted un-der a simple data set. The results confirm the theorem's correctness. As far as we know, both the experimental and theoretical results presented here are novel.

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