IDEAS home Printed from https://ideas.repec.org/a/pal/jorapm/v19y2020i4d10.1057_s41272-019-00211-8.html
   My bibliography  Save this article

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

Author

Listed:
  • Ebrahim Teimoury

    (Iran University of Science & Technology)

  • Mohammad Modarres

    (Sharif University of Technology)

  • Morteza Neishaboori

    (Iran University of Science & Technology)

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.

Suggested Citation

  • Ebrahim Teimoury & Mohammad Modarres & Morteza Neishaboori, 2020. "Cost-based differential pricing for a make-to-order production system in a competitive segmented market," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 19(4), pages 266-275, August.
  • Handle: RePEc:pal:jorapm:v:19:y:2020:i:4:d:10.1057_s41272-019-00211-8
    DOI: 10.1057/s41272-019-00211-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/s41272-019-00211-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1057/s41272-019-00211-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Opher Baron & Oded Berman & Dmitry Krass, 2008. "Facility Location with Stochastic Demand and Constraints on Waiting Time," Manufacturing & Service Operations Management, INFORMS, vol. 10(3), pages 484-505, August.
    2. Hossein Abouee-Mehrizi & Sahar Babri & Oded Berman & Hassan Shavandi, 2011. "Optimizing capacity, pricing and location decisions on a congested network with balking," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 74(2), pages 233-255, October.
    3. repec:bla:jregsc:v:44:y:2004:i:3:p:489-515:2 is not listed on IDEAS
    4. Pangburn, Michael S. & Stavrulaki, Euthemia, 2008. "Capacity and price setting for dispersed, time-sensitive customer segments," European Journal of Operational Research, Elsevier, vol. 184(3), pages 1100-1121, February.
    5. Marianov, Vladimir & Rí­os, Miguel & Icaza, Manuel José, 2008. "Facility location for market capture when users rank facilities by shorter travel and waiting times," European Journal of Operational Research, Elsevier, vol. 191(1), pages 32-44, November.
    6. Gregory Dobson & Euthemia Stavrulaki, 2007. "Simultaneous price, location, and capacity decisions on a line of time‐sensitive customers," Naval Research Logistics (NRL), John Wiley & Sons, vol. 54(1), pages 1-10, February.
    7. McFadden, Daniel, 1974. "The measurement of urban travel demand," Journal of Public Economics, Elsevier, vol. 3(4), pages 303-328, November.
    8. Pierre Hanjoul & Pierre Hansen & Dominique Peeters & Jacques-Francois Thisse, 1990. "Uncapacitated Plant Location Under Alternative Spatial Price Policies," Management Science, INFORMS, vol. 36(1), pages 41-57, January.
    9. Qian Wang & Rajan Batta & Christopher Rump, 2002. "Algorithms for a Facility Location Problem with Stochastic Customer Demand and Immobile Servers," Annals of Operations Research, Springer, vol. 111(1), pages 17-34, March.
    10. Abdullah Dasci & Gilbert Laporte, 2004. "Location and Pricing Decisions of a MultiStore Monopoly in a Spatial Market," Journal of Regional Science, Wiley Blackwell, vol. 44(3), pages 489-515, August.
    11. Robert Aboolian & Oded Berman & Zvi Drezner, 2009. "The multiple server center location problem," Annals of Operations Research, Springer, vol. 167(1), pages 337-352, March.
    12. repec:bla:jregsc:v:44:y:2004:i:3:p:489-515:1 is not listed on IDEAS
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Teodora Dan & Andrea Lodi & Patrice Marcotte, 2020. "Joint location and pricing within a user-optimized environment," EURO Journal on Computational Optimization, Springer;EURO - The Association of European Operational Research Societies, vol. 8(1), pages 61-84, March.
    2. Hoseinpour, Pooya & Ahmadi-Javid, Amir, 2016. "A profit-maximization location-capacity model for designing a service system with risk of service interruptions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 96(C), pages 113-134.
    3. Zhang, Yue, 2015. "Designing a retail store network with strategic pricing in a competitive environment," International Journal of Production Economics, Elsevier, vol. 159(C), pages 265-273.
    4. Ralf Krohn & Sven Müller & Knut Haase, 2021. "Preventive healthcare facility location planning with quality-conscious clients," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 43(1), pages 59-87, March.
    5. Hoseinpour, Pooya & Jalili Marand, Ata, 2022. "Designing a service system with price- and distance-sensitive demand: A case study in mining industry," European Journal of Operational Research, Elsevier, vol. 303(3), pages 1355-1371.
    6. Azad, Nader & Hassini, Elkafi, 2019. "Recovery strategies from major supply disruptions in single and multiple sourcing networks," European Journal of Operational Research, Elsevier, vol. 275(2), pages 481-501.
    7. Jianpei Wen & Hanyu Jiang & Jie Song, 2019. "A Stochastic Queueing Model for Capacity Allocation in the Hierarchical Healthcare Delivery System," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 36(01), pages 1-24, February.
    8. R Aboolian & O Berman & D Krass, 2008. "Optimizing pricing and location decisions for competitive service facilities charging uniform price," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(11), pages 1506-1519, November.
    9. Jayaswal, Sachin, 2014. "Emergency Medical Service System Design under Service Level Constraints for Heterogeneous Patients," IIMA Working Papers WP2014-11-04, Indian Institute of Management Ahmedabad, Research and Publication Department.
    10. Cornelia Schön & Pratibha Saini, 2018. "Market-Oriented Service Network Design When Demand is Sensitive to Congestion," Transportation Science, INFORMS, vol. 52(5), pages 1253-1275, October.
    11. Ahmadi-Javid, Amir & Hoseinpour, Pooya, 2019. "Service system design for managing interruption risks: A backup-service risk-mitigation strategy," European Journal of Operational Research, Elsevier, vol. 274(2), pages 417-431.
    12. Sachin Jayaswal & Navneet Vidyarthi, 2017. "Facility location under service level constraints for heterogeneous customers," Annals of Operations Research, Springer, vol. 253(1), pages 275-305, June.
    13. Hoon Jang, 2019. "Designing capacity rollout plan for neonatal care service system in Korea," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 41(3), pages 809-830, September.
    14. Zhang, Yue & Berman, Oded & Verter, Vedat, 2009. "Incorporating congestion in preventive healthcare facility network design," European Journal of Operational Research, Elsevier, vol. 198(3), pages 922-935, November.
    15. Hossein Abouee-Mehrizi & Sahar Babri & Oded Berman & Hassan Shavandi, 2011. "Optimizing capacity, pricing and location decisions on a congested network with balking," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 74(2), pages 233-255, October.
    16. Teodora Dan & Patrice Marcotte, 2019. "Competitive Facility Location with Selfish Users and Queues," Operations Research, INFORMS, vol. 67(2), pages 479-497, March.
    17. Jang, Hoon & Lee, Jun-Ho, 2019. "A hierarchical location model for determining capacities of neonatal intensive care units in Korea," Socio-Economic Planning Sciences, Elsevier, vol. 68(C).
    18. Robert Aboolian & Oded Berman & Dmitry Krass, 2012. "Profit Maximizing Distributed Service System Design with Congestion and Elastic Demand," Transportation Science, INFORMS, vol. 46(2), pages 247-261, May.
    19. Zhang, Yue & Liang, Liping & Liu, Emma & Chen, Chong & Atkins, Derek, 2016. "Patient choice analysis and demand prediction for a health care diagnostics company," European Journal of Operational Research, Elsevier, vol. 251(1), pages 198-205.
    20. repec:iim:iimawp:13011 is not listed on IDEAS
    21. Mengying Xue & Long He, 2020. "Spatial pricing and product allocation in online retailing," Naval Research Logistics (NRL), John Wiley & Sons, vol. 67(8), pages 670-684, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pal:jorapm:v:19:y:2020:i:4:d:10.1057_s41272-019-00211-8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.palgrave.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.