Optimal time-based consolidation policy with price sensitive demand

Ki Sung Hong, Chulung Lee

    Research output: Contribution to journalArticlepeer-review

    20 Citations (Scopus)

    Abstract

    We consider a single-item inventory system where shipments are consolidated to reduce the transportation cost using a time-based consolidation policy and develop a mathematical model to obtain the optimal price, replenishment quantity and dispatch cycle to maximize the total profit. The long-run average profit is computed and the optimality properties are obtained. Using the optimality properties, we develop an efficient algorithm to obtain the optimal values of price, replenishment quantity and dispatch cycle for the proposed policy. We then extend our results to consider quantity discount of unit dispatch cost. In order to compare the performances of the proposed policy with the optimal quantity-based policy, extensive numerical experiments are conducted, and the total profit and the customer waiting time that represents quality of service are compared. We also propose the quantity-time-based policy, which is a hybrid approach, and the numerical results show that additional profit can be obtained without sacrificing quality of service.

    Original languageEnglish
    Pages (from-to)275-284
    Number of pages10
    JournalInternational Journal of Production Economics
    Volume143
    Issue number2
    DOIs
    Publication statusPublished - 2013 Jun

    Bibliographical note

    Funding Information:
    This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2010-0015413). This research was supported by Jungseok Logistics Foundation Grant.

    Keywords

    • Linear demand function
    • Pricing
    • Shipment consolidation
    • Time-based consolidation policy

    ASJC Scopus subject areas

    • General Business,Management and Accounting
    • Economics and Econometrics
    • Management Science and Operations Research
    • Industrial and Manufacturing Engineering

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