An Agent Based Optimization Approach to Manufacturing Process Planning

2001 ◽  
Author(s):  
Saurabh Deshpande ◽  
Jonathan Cagan
Keyword(s):  
Stochastic Optimization ◽  
Process Planning ◽  
Manufacturing Process ◽  
Optimization Problems ◽  
Search Space ◽  
Optimization Techniques ◽  
Process Models ◽  
Optimization Approach ◽  
Agent Based ◽  
Manufacturing Process Planning

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.

An Agent Based Optimization Approach to Manufacturing Process Planning

2004 ◽  
Vol 126 (1) ◽  
pp. 46-55 ◽  
Author(s):  
Saurabh Deshpande ◽  
Jonathan Cagan
Keyword(s):  
Stochastic Optimization ◽  
Process Planning ◽  
Manufacturing Process ◽  
Optimization Problems ◽  
Search Space ◽  
Optimization Techniques ◽  
Process Models ◽  
Optimization Approach ◽  
Agent Based ◽  
Manufacturing Process Planning

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.

Optimized process planning by generative simulated annealing

1997 ◽  
Vol 11 (3) ◽  
pp. 219-235 ◽  
Author(s):  
K.N. Brown ◽  
J. Cagan
Keyword(s):  
Simulated Annealing ◽  
Process Planning ◽  
Manufacturing Process ◽  
Formal Language ◽  
Simulated Annealing Algorithm ◽  
Search Space ◽  
Difficult Problem ◽  
Manufacturing Process Planning ◽  
Alternative Approach ◽  
Annealing Algorithm

AbstractManufacturing process planning is a difficult problem with a prohibitively large search space. It is normally tackled by decomposing goal objects into features, and then sequencing features to obtain a plan. This paper investigates an alternative approach. The capabilities of a manufacturing process are represented by a formal language of shape, in which sentences correspond to manufacturable objects. The language is interpreted to describe process plans corresponding to the shape generation, complete with cost estimates. A macro layer that describes single operations of the machine is implemented on top of the formal language. The space it describes is searched by the generative simulated annealing algorithm, a stochastic search technique based on simulated annealing. Plans that are close to the optimum are generated in reasonable time.

On Specifying an Information Management Tool to Support Manufacturing Process Planning in Aerospace: A Case Study

2006 ◽  
Author(s):  
M. A. El Hani ◽  
L. Rivest ◽  
C. Fortin
Keyword(s):  
Information Management ◽  
Process Planning ◽  
Manufacturing Process ◽  
Information Needs ◽  
Research Work ◽  
Resource Planning ◽  
Process Models ◽  
Management Tool ◽  
Management Tools ◽  
Manufacturing Process Planning

Facing increasing product complexity and pressure to reduce time to market, manufacturing process planning (MPP) engineers must be able to quickly access reliable information in order to make swift and correct decisions. Organizations therefore turn to information management tools, such as PDM (Product Data Management), MPM (Manufacturing Process Management) and ERP (Enterprise Resource Planning), to support their product development processes. These various information management tools compete by offering similar features, while MPP engineers have to manipulate multiple tools to access the information they need. This paper aims to take a fresh look at a fundamental question: what are the specifications of an ideal information management tool that would help MPP engineers efficiently define manufacturing work instructions (process plan) from the product definition? This paper thus presents the approach and the results of a research work conducted within the process planning department of a manufacturing company operating in the aerospace sector. The study was conducted so as to direct the effort toward documenting the MPP process and the MPP engineer’s information needs. The approach that is presented primarily relies on a comprehensive documentation and modeling of the MPP development process. Two processes have been modeled, a reference process of the MWI (Manufacturing Work Instructions) development and a change management process impacting the MWI development. These process models offer a sound basis to conduct an analysis of the MPP engineers’ information needs. This analysis next leads to the specifications of a Dashboard solution aimed at MPP engineers.

Automated Reuse of Solutions in Manufacturing Process Planning Through a Case-Based Approach

1996 ◽  
Author(s):  
M. Marefat ◽  
J. Britanik
Keyword(s):  
Process Planning ◽  
Manufacturing Process ◽  
Object Oriented ◽  
Process Plan ◽  
Primary Mechanism ◽  
Prismatic Parts ◽  
Manufacturing Process Planning ◽  
Computer Aided ◽  
Effective Use ◽  
Case Based

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.

scholarly journals A Distributed Multifactorial Particle Swarm Optimization Approach

2021 ◽  
Author(s):  
Ahlem Aboud ◽  
Nizar Rokbani ◽  
Seyedali Mirjalili ◽  
Abdulrahman M. Qahtani ◽  
Omar Almutiry ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Beta Function ◽  
Particle Swarm ◽  
Search Space ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Swarm Optimization ◽  
Multi Objective ◽  
New System

<p>Multifactorial Optimization (MFO) and Evolutionary Transfer Optimization (ETO) are new optimization challenging paradigms for which the multi-Objective Particle Swarm Optimization system (MOPSO) may be interesting despite limitations. MOPSO has been widely used in static/dynamic multi-objective optimization problems, while its potentials for multi-task optimization are not completely unveiled. This paper proposes a new Distributed Multifactorial Particle Swarm Optimization algorithm (DMFPSO) for multi-task optimization. This new system has a distributed architecture on a set of sub-swarms that are dynamically constructed based on the number of optimization tasks affected by each particle skill factor. DMFPSO is designed to deal with the issues of handling convergence and diversity concepts separately. DMFPSO uses Beta function to provide two optimized profiles with a dynamic switching behaviour. The first profile, Beta-1, is used for the exploration which aims to explore the search space toward potential solutions, while the second Beta-2 function is used for convergence enhancement. This new system is tested on 36 benchmarks provided by the CEC’2021 Evolutionary Transfer Multi-Objective Optimization Competition. Comparatives with the state-of-the-art methods are done using the Inverted General Distance (IGD) and Mean Inverted General Distance (MIGD) metrics. Based on the MSS metric, this proposal has the best results on most tested problems.</p>

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