An Automated Process Planning System Based on Genetic Algorithm and Simulated Annealing

H., G.; Zhang, F.; Zhang, Y. F.; Nee, A.Y.C.

doi:10.1115/detc2002/dfm-34159

Cited by 10 publications

(8 citation statements)

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“…After that, the SA algorithm was employed to search for alternative optimal or near-optimal process plans based on a few plans selected from the process plans obtained in the first stage. Ma et al [23] presented a CAPP system based on GA and SA. The developed system provided flexible optimization criteria that could satisfy the various needs from different job-shops and/or job-batches.…”

Section: Related Workmentioning

confidence: 99%

“…In recent years, many metaheuristic algorithms have been applied to process planning problem due to their superiority in solving combinatorial optimization problems. These algorithms can be categorized as genetic algorithm (GA) [13][14][15][16][17][18][19][20][21], simulated annealing (SA) [22,2,23], tabu search (TS) [24,25], particle swarm optimization (PSO) [26] and ant colony optimization (ACO) [10].…”

Section: Related Workmentioning

confidence: 99%

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A multi-dimensional tabu search algorithm for the optimization of process planning

Lian

Zhang

Shao

et al. 2011

Sci. China Technol. Sci.

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Computer-aided process planning (CAPP) is an essential component of computer integrated manufacturing (CIM) system. A good process plan can be obtained by optimizing two elements, namely, operation sequence and the machining parameters of machine, tool and tool access direction (TAD) for each operation. This paper proposes a novel optimization strategy for process planning that considers different dimensions of the problem in parallel. A multi-dimensional tabu search (MDTS) algorithm based on this strategy is developed to optimize the four dimensions of a process plan, namely, operation sequence (OperSeq), machine sequence (MacSeq), tool sequence (ToolSeq) and tool approach direction sequence (TADSeq), sequentially and iteratively. In order to improve its efficiency and stability, tabu search, which is incorporated into the proposed MDTS algorithm, is used to optimize each component of a process plan, and some neighbourhood strategies for different components are presented for this tabu search algorithm. The proposed MDTS algorithm is employed to test four parts with different numbers of operations taken from the literature and compared with the existing algorithms like genetic algorithm (GA), simulated annealing (SA), tabu search (TS) and particle swarm optimization (PSO). Experimental results show that the developed algorithm outperforms these algorithms in terms of solution quality and efficiency. process planning, cooperative tabu search, genetic algorithm, simulated annealing, particle swarm optimization Citation:Lian K L, Zhang C Y, Shao X Y, et al. A multi-dimensional tabu search algorithm for the optimization of process planning.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A multi-dimensional tabu search algorithm for the optimization of process planning

Lian

Zhang

Shao

et al. 2011

Sci. China Technol. Sci.

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“…Ma, Zhang, and Nee [21] reported an SA algorithm for operations selection and sequencing. Brown and Cagan [22], Li, Ong, and Nee [23], Shan et al [24], and Ma, Zhang, and Nee [25] developed hybrid GAs-SA optimization approaches to optimize process plans, setup plans, and operations sequencing in multiple domains such as metal cutting and assembly planning.…”

Section: Previous Workmentioning

confidence: 99%

Sequential process planning: A hybrid optimal macro-level approach

Azab

ElMaraghy

2007

Journal of Manufacturing Systems

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“…The operation sequence generation problem can usually be modeled as large-scale and combinational optimization problems [4]. GA, Particle Swarm Optimization(PSO) and Simulated Annealing(SA) approaches have been applied to operation sequencing [5] [6].…”

Section: Relevant Research Workmentioning

confidence: 99%

A Modified Genetic Algorithm(GA) for Optimization of Process Planning

Wang¹,

Du²,

Ding³

2011

JCP

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Computer-aided process planning (CAPP) is an important and complex in the computer integrated manufacturing system(CIMS). The expression methods of process knowledge based on manufacturing feature is established based on the analysis of characteristic of process knowledge. According to the constraints mong process knowledge, decision space of process planning based on process constraints is constructed which improved search efficiency of GA. In allusion to the uncertainty of decision of process planning, the multi-objective optimization function is established, and a GA is applied to optimization of process planning. Process planning is optimized using the reasonable coding strategy, fitness function, selection, crossover and mutation algorithm, etc. An case is offered to illustrate the process of optimization of process planing based on GA

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An Automated Process Planning System Based on Genetic Algorithm and Simulated Annealing

Cited by 10 publications

References 0 publications

A multi-dimensional tabu search algorithm for the optimization of process planning

A multi-dimensional tabu search algorithm for the optimization of process planning

Sequential process planning: A hybrid optimal macro-level approach

A Modified Genetic Algorithm(GA) for Optimization of Process Planning

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