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Production Planning & Control
The Management of Operations
Volume 24, 2013 - Issue 6
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Original Articles

A simulation testing and analysis of aggregate production planning strategies

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Pages 423-448 | Received 15 Oct 2010, Accepted 29 Sep 2011, Published online: 10 Nov 2011
 

Abstract

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.

Acknowledgements

The authors appreciate the two anonymous referees for their valuable and constructive comments to improve the article.

Notes

Note

1. Authors had previously developed and implemented this model in the aforementioned home equipment producer company and published it in one of the leading journals in the field of production and operations management.

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