scholarly journals Agent-based distributed manufacturing process planning and scheduling: a state-of-the-art survey

Author(s):  
Weiming Shen ◽  
Lihui Wang ◽  
Qi Hao
Author(s):  
Saurabh Deshpande ◽  
Jonathan Cagan

Abstract Many optimization problems, such as manufacturing process planning optimization, are difficult problems due to the large number of potential configurations (process sequences) and associated (process) parameters. In addition, the search space is highly discontinuous and multi-modal. This paper introduces an agent based optimization algorithm that combines stochastic optimization techniques with knowledge based search. The motivation is that such a merging takes advantage of the benefits of stochastic optimization and accelerates the search process using domain knowledge. The result of applying this algorithm to computerized manufacturing process models is presented.


2010 ◽  
Vol 20-23 ◽  
pp. 28-33
Author(s):  
Da Wei Liu ◽  
Hong Bin Liu

Traditionally, Models of IT manufacturing process planning and scheduling were carried out in a sequential way, where scheduling was done after process plans had been delivered. Since the two functions are usually complementary, it is necessary to integrate them correctly so that performance of an IT manufacturing system can be improved efficiently. In the thesis, a new integration model focused on key factors has been developed to facilitate the integration and optimization. The practice of the models show that the proposed approaches are promising and very effective methods for the integration of process planning and scheduling in IT manufacturing processes.


2004 ◽  
Vol 126 (1) ◽  
pp. 46-55 ◽  
Author(s):  
Saurabh Deshpande ◽  
Jonathan Cagan

Many optimization problems, such as manufacturing process planning optimization, are difficult problems due to the large number of potential configurations (process sequences) and associated (process) parameters. In addition, the search space is highly discontinuous and multi-modal. This paper introduces an agent based optimization algorithm that combines stochastic optimization techniques with knowledge based search. The motivation is that such a merging takes advantage of the benefits of stochastic optimization and accelerates the search process using domain knowledge. The result of applying this algorithm to computerized manufacturing process models is presented.


Author(s):  
M. Marefat ◽  
J. Britanik

Abstract This research focuses on the development of an object-oriented case-based process planner which combines the advantages of the variant and generative approaches to process planning. The case-based process planner operates on general 3D prismatic parts, represented by a collection of features (eg: slots, pockets, holes, etc.). Each feature subplan is developed by the case-based planner. Then the feature subplans are combined into the global process plan for the part via a hierarchical plan merging mechanism. Abstracted feature subplans correspond to cases, which are used in subsequent planning operations to solve new problems. The abstracting and storing of feature subplans as cases is the primary mechanism by which the planner learns from its previous experiences to become more effective and efficient. The computer-aided process planner is designed to be extensible and flexible through the effective use of object-oriented principles.


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