IDEAS home Printed from https://ideas.repec.org/a/igg/joris0/v9y2018i3p1-26.html
   My bibliography  Save this article

Solving an Integrated Sales-Leasing Problem With Remanufacturing and Inventory Shortage Using Differential Evolution

Author

Listed:
  • Masoud Rabbani

    (School of Industrial and Systems Engineering, University of Tehran, Tehran, Iran)

  • Sina Keyhanian

    (Department of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran, Iran)

  • Mojtaba Aryaee

    (École Nationale Supérieure d'Arts et Métiers (ENSAM), Paris Tech University, Paris, France)

  • Esmat Sangari

    (Department of Industrial Engineering and Management Sciences, McCormick School of Engineering, Northwestern University, Evanston, USA)

Abstract

In this article, an integrated sales and leasing company is considered. This company remanufactures leased products at the end of operating lease contracts to make them as good as new ones and sell them to the customers. In order to satisfy customers' demand, required products are provided from a third-party when the company meets inventory shortage. Non-linear competitive demand functions are used which are sensitive to manufacturer suggested retail price (MSRP) and inflation rate. A mixed integer non-linear mathematical model (MINLP) is developed to determine optimal price of selling products, optimal amount of monthly payments in leasing contracts, and optimal inventory control planning, i.e. the optimal amount of manufacturing and remanufacturing products and optimal inventory levels. The main objective is to maximize net profit of the company. Small, medium and large-scale sizes of the model are solved to show the applicability of the model. To solve the large-scale problem, differential evolution (DE) algorithm is applied as a meta-heuristic solution approach. Numerical results show high sensitivity of model to demands. Also, optimal trend behaviors of some main variables of the problem seem similar to the competitive behavior of demands.

Suggested Citation

  • Masoud Rabbani & Sina Keyhanian & Mojtaba Aryaee & Esmat Sangari, 2018. "Solving an Integrated Sales-Leasing Problem With Remanufacturing and Inventory Shortage Using Differential Evolution," International Journal of Operations Research and Information Systems (IJORIS), IGI Global, vol. 9(3), pages 1-26, July.
    • Handle: RePEc:igg:joris0:v:9:y:2018:i:3:p:1-26
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJORIS.2018070101
    Download Restriction: no
    ---><---

    More about this item

    Statistics

    Access and download statistics

    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:igg:joris0:v:9:y:2018:i:3:p:1-26. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.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.