A simulated annealing-based optimization algorithm for process planning

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

doi:10.1080/002075400411420

Cited by 93 publications

(45 citation statements)

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“…Note that OperSeq indicates the position of an operation in a process plan. For example, operations 1-5 locate in the 1, 3, 5, 4, 2 of the process plan given in Table 2, so the OperSeq is made up of (1,3,5,4,2). MacSeq, ToolSeq and TADSeq store the information of the operation that the operation number indicates.…”

Section: Encoding Schemementioning

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%

“…Ma et al [2] described an approach that modeled the constraints of process planning problems in a concurrent manner and SA was developed to search for the optimal solution. Li et al [22] developed a hybrid GA and SA approach to solve the process planning problem with constraints.…”

Section: Related Workmentioning

confidence: 99%

“…It links Computer Aided Design (CAD) and Computer Aided Manufacturing (CAM) by transforming a product design into a set of instructions (machines, tool, setups, etc.) [2]. During the past three decades, CAPP has received much attention from researchers and numerous CAPP systems have been reported.…”

Section: Introductionmentioning

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: Encoding Schemementioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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“…Process planning is the task of planning the various processes or operations to be made on raw materials to transform it into the desired part. The output of this task is a plan that contains the routes, processes, process parameters, machines, tools and set-ups to be used for manufacturing parts (Ma et al 2000;Nau and Chang 1983). While computer aided hierarchical process planning has advanced significantly in its duty of creating the primitive process plans with alternatives, human intervention is still needed as decision mechanisms to finalize process plans by serializing the process plans after considering alternate sequences of the operation to be performed, and then allocate the resources based on this final process plan.…”

Section: Hierarchical Decision-makingmentioning

confidence: 99%

Impact of information sharing in hierarchical decision-making framework in manufacturing supply chains

2010

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This paper presents a comprehensive framework for the analysis of the impact of information sharing in hierarchical decision-making in manufacturing supply chains. In this framework, the process plan selection and real-time resource allocation problems are formulated as hierarchical optimization problems, where problems at each level in the hierarchy are solved by separate multi-objective genetic algorithms. The considered multi-objective genetic algorithms generate near optimal solutions for NP-hard problems with less computational complexity. In this work, a four-level hierarchical decision structure is considered, where the decision levels are defined as enterprise level, shop level, cell level, and equipment level. Using this framework, the sources of information affecting the achievement of best possible decisions are then identified at each of these levels, and the extent of their effects from sharing them are analyzed in terms of the axis, degree and the content of information. The generality and validity of the proposed approach have been successfully tested for diverse manufacturing systems generated from a designed experiment.

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