Research Output
A simulation testing and analysis of aggregate production planning strategies
  In this study, a hybrid discrete event simulation (DES) and system dynamics (SD) methodology is applied to model and simulate aggregate production planning (APP) problem for the first time. DES is used to simulate operational-level and shop-floor activities incorporated into APP and estimate critical time-based control parameters used in SD model of APP and SD is used to simulate APP as a collection of aggregate-level strategic decisions. The main objective of this study is to determine and analyse the effectiveness of APP strategies regarding the Total Profit criterion by developing a hybrid DES–SD simulation model for APP in a real-world manufacturing company. The simulation results demonstrated that the priority of APP strategies with regards to Total Profit criterion is: (1) the pure chase strategy, (2) the modified chase strategy, (3) the pure level strategy, (4) the modified level strategy, (5) the mixed strategy and (6) the demand management strategy, respectively. The APP system is first simulated under mixed strategy (basic scenario) conditions to include all APP capacity and demand options in constructed SD simulation model to show a comprehensive view of APP components and their interdependent interactions. Then, the obtained results will be used as Total Profit measure to compare with system's performance under some experimental scenarios applying different APP strategies.

  • Type:

    Article

  • Date:

    10 November 2011

  • Publication Status:

    Published

  • Publisher

    Informa UK Limited

  • DOI:

    10.1080/09537287.2011.631595

  • ISSN:

    0953-7287

  • Funders:

    Historic Funder (pre-Worktribe)

Citation

Jamalnia, A., & Feili, A. (2013). A simulation testing and analysis of aggregate production planning strategies. Production Planning and Control, 24(6), 423-448. https://doi.org/10.1080/09537287.2011.631595

Authors

Keywords

aggregate production planning, discrete event simulation, system dynamics, aggregate production planning strategies, level (stock) variables, rate (flow) variables

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