Hierarchical Production Planning for Complex Manufacturing Systems

1994 ◽  
Author(s):  
Anshu Mehra ◽  
Ioannis Minis ◽  
Jean M. Proth
Keyword(s):  
Production Planning ◽  
Manufacturing Systems ◽  
Production Management ◽  
Optimization Problems ◽  
Planning Problem ◽  
Manufacturing Cells ◽  
Work In Process ◽  
Time Periods ◽  
Detailed Level ◽  
Complex Manufacturing Systems

Abstract A hierarchical approach to production planning for complex manufacturing systems is presented. A single facility comprising a number of work-centers that produce multiple part types is considered. The planning horizon includes a sequence of time periods, and the demand for all part types is assumed to be known. The production planning problem consists of minimizing the holding costs for all part types as well as the work-in-process, and backlogging cost for the end items. We present a two-level hierarchy that is based on aggregating parts to part families, work-centers to manufacturing cells and time periods to aggregate time periods. The solution at the aggregate level is imposed as a constraint to the detailed level problem which employs a decomposition based on manufacturing cells. This architecture uses a rolling horizon strategy to perform the production management function. We have employed perturbation analysis techniques to adjust certain parameters of the optimization problems at the detailed level to reach a near-optimal detailed production plan.

Petri Net Model Based Design and Control of Robotic Manufacturing Cells

2012 ◽  
pp. 393-408
Author(s):  
Gen’ichi Yasuda
Keyword(s):  
Petri Nets ◽  
Petri Net ◽  
Manufacturing Systems ◽  
Discrete Event ◽  
Manufacturing Cells ◽  
Modeling And Control ◽  
Complex Manufacturing Systems ◽  
Task Specification ◽  
And Control ◽  
The Coordinator

The methods of modeling and control of discrete event robotic manufacturing cells using Petri nets are considered, and a methodology of decomposition and coordination is presented for hierarchical and distributed control. Based on task specification, a conceptual Petri net model is transformed into the detailed Petri net model, and then decomposed into constituent local Petri net based controller tasks. The local controllers are coordinated by the coordinator through communication between the coordinator and the controllers. Simulation and implementation of the control system for a robotic workcell are described. By the proposed method, modeling, simulation, and control of large and complex manufacturing systems can be performed consistently using Petri nets.

Petri Net Model Based Design and Control of Robotic Manufacturing Cells

2013 ◽  
pp. 385-400 ◽  
Author(s):  
Gen’ichi Yasuda
Keyword(s):  
Petri Nets ◽  
Petri Net ◽  
Manufacturing Systems ◽  
Discrete Event ◽  
Manufacturing Cells ◽  
Modeling And Control ◽  
Complex Manufacturing Systems ◽  
Task Specification ◽  
And Control ◽  
The Coordinator

The methods of modeling and control of discrete event robotic manufacturing cells using Petri nets are considered, and a methodology of decomposition and coordination is presented for hierarchical and distributed control. Based on task specification, a conceptual Petri net model is transformed into the detailed Petri net model, and then decomposed into constituent local Petri net based controller tasks. The local controllers are coordinated by the coordinator through communication between the coordinator and the controllers. Simulation and implementation of the control system for a robotic workcell are described. By the proposed method, modeling, simulation, and control of large and complex manufacturing systems can be performed consistently using Petri nets.

scholarly journals A multi-fidelity RBF surrogate-based optimization framework for computationally expensive multi-modal problems with application to capacity planning of manufacturing systems

2020 ◽  
Vol 62 (4) ◽  
pp. 1787-1807
Author(s):  
Jin Yi ◽  
Yichi Shen ◽  
Christine A. Shoemaker
Keyword(s):  
Capacity Planning ◽  
Manufacturing Systems ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Discrete Event ◽  
Optimal Solution ◽  
Planning Problem ◽  
Optimization Framework ◽  
Surrogate Based Optimization ◽  
Computationally Expensive

Abstract This paper presents a multi-fidelity RBF (radial basis function) surrogate-based optimization framework (MRSO) for computationally expensive multi-modal optimization problems when multi-fidelity (high-fidelity (HF) and low-fidelity (LF)) models are available. The HF model is expensive and accurate while the LF model is cheaper to compute but less accurate. To exploit the correlation between the LF and HF models and improve algorithm efficiency, in MRSO, we first apply the DYCORS (dynamic coordinate search algorithm using response surface) algorithm to search on the LF model and then employ a potential area detection procedure to identify the promising points from the LF model. The promising points serve as the initial start points when we further search for the optimal solution based on the HF model. The performance of MRSO is compared with 6 other surrogate-based optimization methods (4 are using a single-fidelity surrogate and the rest 2 are using multi-fidelity surrogates). The comparisons are conducted on a multi-fidelity optimization test suite containing 10 problems with 10 and 30 dimensions. Besides the benchmark functions, we also apply the proposed algorithm to a practical and computationally expensive capacity planning problem in manufacturing systems which involves discrete event simulations. The experimental results demonstrate that MRSO outperforms all the compared methods.

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