scholarly journals Continuous Dynamic Models, Clearing Functions, and Discrete-Event Simulation in Aggregate Production Planning

2012 ◽  
pp. 103-126 ◽  
Author(s):  
Dieter Armbruster ◽  
Reha Uzsoy
Keyword(s):  
Production Planning ◽  
Discrete Event Simulation ◽  
Dynamic Models ◽  
Discrete Event ◽  
Aggregate Production Planning ◽  
Aggregate Production ◽  
Event Simulation ◽  
Clearing Functions ◽  
Continuous Dynamic

scholarly journals Research on Lean Planning and Optimization for Precast Component Production Based on Discrete Event Simulation

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Zhenmin Yuan ◽  
Yaning Qiao ◽  
Yaru Guo ◽  
Yaowu Wang ◽  
Chen Chen ◽  
...  
Keyword(s):  
Production Planning ◽  
Discrete Event Simulation ◽  
Manufacturing Industry ◽  
Lean Production ◽  
Discrete Event ◽  
Production Optimization ◽  
Multidimensional Analysis ◽  
Production Time ◽  
Event Simulation ◽  
Component Production

Component factories are experiencing the problems associated with lean production, especially the accuracy of production time prediction and the unnecessary waste in terms of time and resource utilization. In order to solve these problems, a discrete event simulation- (DES-) based lean planning and optimization method for precast component production is proposed by integrating the complexity assessment (CS), discrete event simulation (DES), and lean management (LM). The method includes three submodels: improved production planning, DES, and lean analysis and optimization. In the submodel of improved production planning, a complexity evaluation index system for precast components is established through investigating five component factories, consulting seven domain experts and analysing relevant literature. In the submodel of DES, the DES technique is adopted to simulate and analyse the production activities of precast components. The submodel of lean analysis and optimization provides multidimensional analysis, comparative analysis, and suggestions. Finally, a detailed production case is selected to simulate and test the proposed method. The important findings are as follows: (1) this method can minimize the difference between the processing time of each workstation to avoid bottleneck stations as much as possible; (2) this method can capture the uncertainty during precast component production, and the most likely production time calculated by the method is 12.05 hours instead of the 11.50 hours originally estimated by the component factory; (3) this method can identify some unnecessary waste in the production process of precast components, including less than 50% utilization of workstations and unnecessary equipment purchases; (4) this method also provides some suggestions regarding production optimization. Due to the particularity of precast component production, it further expands the boundary of lean production methodology from the perspective of the construction industry rather than the manufacturing industry. The proposed method assists component factories in planning and optimizing the precast component production when they make detailed production plans.

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