Cost-based differential pricing for a make-to-order production system in a competitive segmented market

Ebrahim Teimoury, Mohammad Modarres and Morteza Neishaboori ()
Additional contact information
Ebrahim Teimoury: Iran University of Science & Technology
Mohammad Modarres: Sharif University of Technology
Morteza Neishaboori: Iran University of Science & Technology

Journal of Revenue and Pricing Management, 2020, vol. 19, issue 4, No 7, 266-275

Abstract: Abstract Optimal queuing system design includes deciding about some variables such as location, capacity, price, and delivery time. In order to develop an optimized system, it is essential to solve the optimization problem for all the decision variables simultaneously. In this paper, a make-to-order system is considered. In this system, some facilities are developed in different points. According to the make-to-order system definition, these facilities keep no inventory from final product. Orders are received from customers and the requested products are assembled. Due to existence of some differences among customers, they are divided into two distinct categories including express and regular customers. Each category has purchasing and upgrading demand. Upgrading demand is related to the used products that are referred to the facilities by reverse logistics for upgrade. Profit is maximized through attracting these two types of demand from different demand points. Utilizing market segmentation, different prices can be offered to each category. Difference in price is due to difference in delivery time. It means maximum guaranteed time to deliver the product. This obligation raises customer satisfaction. But lower delivery time causes increase in price and lower power in competition. Increase is price is due to designing a queue with higher capacity. In this problem, location of facilities should be selected among some potential points. There are several competitors in each potential location. Each facility is modeled as a queuing system. In these systems, decision variables are location, capacity, price, and delivery time. Finally, the optimization problem is solved by the genetic algorithm and the optimized value for each variable is gained. Facilities with these variables will have the maximum profit.

Keywords: Optimization; Queuing systems; Differential pricing; Reverse logistics; Genetic algorithm (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1057/s41272-019-00211-8 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pal:jorapm:v:19:y:2020:i:4:d:10.1057_s41272-019-00211-8

Ordering information: This journal article can be ordered from
https://www.palgrave.com/gp/journal/41272

DOI: 10.1057/s41272-019-00211-8

Access Statistics for this article

Journal of Revenue and Pricing Management is currently edited by Ian Yeoman

More articles in Journal of Revenue and Pricing Management from Palgrave Macmillan
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().