ItemBook Chapter

Real- coded genetic algorithm for optimal ordering and pricing in segmented market with freshness and price- dependent demand, advance payment, and trade credit

Title
Real- coded genetic algorithm for optimal ordering and pricing in segmented market with freshness and price- dependent demand, advance payment, and trade credit
Creator
Parmal, Varun; Banerjee, Snigdha; Agrawal, Swati
Description
We study the inventory model of a product having demand affected by its freshness and selling price in the context of supply chains, freshness, and price-dependent demand, where the supplier is dominated, as is usually the case with producers of agri-based products. The product when received exhibits heterogeneous quality. The retailer subdivides the product into quality-dependent segments, which he sells simultaneously during the selling season at prices commensurate with the quality. The sizes of the segments are random variables. The supplier can get a partial advance payment from the dominant retailer by providing a discount on the partial advance with the proportion of partial payment as well as the epoch of partial payment chosen by the supplier. The retailer can, at times, choose the advance proportion to be paid, and the discounted price which we call the endogenous case but takes a loan for the advance payment from a financer, whom he repays with interest when a delayed payment period permitted by the supplier gets over. The retailer in turn gets some time before he can pay his remaining dues and pays the supplier a fraction of the cost price commensurate with the quality of the product. Lost sales shortages are considered for fresh items. The model is aimed at obtaining optimal values of ordering amount, selling price, and discounted selling prices for the various segments. It is also aimed to obtain advance proportion and the discount on advance payment for the endogenous case. Real-coded genetic algorithm (RCGA) and Hybrid RCGA have been used to obtain the optimal solutions for numerical examples and the results are compared. Finally, sensitivity analysis to evaluate the effects of changes in some parameter values has also been presented. 2025 selection and editorial matter, Sulabh Bansal, Aprna Tripathi, Shilpa Srivastava and Prem Prakash Vuppuluri; individual chapters, the contributors.
Source
Nature-inspired Metaheuristic Algorithms: Solving Real World Engineering Problems;pp.284-306
Date
01-01-2025
Publisher
CRC Press
Coverage
Parmal V., Chameli Devi Group of Institutions, Indore, India; Banerjee S., School of Statistics, Devi Ahilya University, Indore, India; Agrawal S., Christ University, Bengaluru, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 978-104034487-3; 978-103277087-1;
Format
online
Language
English
Type
Book chapter

Citation

Parmal, Varun; Banerjee, Snigdha; Agrawal, Swati, “Real- coded genetic algorithm for optimal ordering and pricing in segmented market with freshness and price- dependent demand, advance payment, and trade credit,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/24428.

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