Optimization of flexible process planning by genetic programming

Li, X. Y.; Shao, Xinyu; Gao, Liang

doi:10.1007/s00170-007-1069-x

Cited by 42 publications

(18 citation statements)

References 18 publications

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“…New representations and genetic operators were proposed to cope with specific scheduling problems [38]. Experiments to compare different GP methods were also conducted [59,73]. Since 2010, there have been a dramatic growth in the number of studies on this topic.…”

Section: Genetic Programming For Production Schedulingmentioning

confidence: 99%

“…For example, Tay and Ho [135] applied GP to evolve dispatching [73] rules for flexible job shop scheduling and use the least waiting time assignment [49] to find a suitable machine to process an operation. Similarly, Pickardt et al [117] evolved dispatching rules for semiconductor manufacturing and used two existing heuristics, i.e.…”

Section: Component(s) To Be Evolvedmentioning

confidence: 99%

“…Yin et al [142] Ho and Tay [50] Geiger et al [38] Jakobovic and Budin [59] Jakobovic et al [60] Tay and Ho [134] Beham et al [14] Geiger and Uzsoy [37] Baykasoglu [9] Li et al [73] Tay and Ho [135] Yang et al [141] Mucientes et al [83] Baykasolu and Gken [12] Kofler et al [66] Furuholmen et al [36] Hildebrandt et al [46] Kuczapski et al…”

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confidence: 99%

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Genetic programming for production scheduling: a survey with a unified framework

Nguyen

Mei

Zhang

2017

Complex Intell. Syst.

214

101

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Genetic programming has been a powerful technique for automated design of production scheduling heuristics. Many studies have shown that heuristics evolved by genetic programming can outperform many existing heuristics manually designed in the literature. The flexibility of genetic programming also allows it to discover very sophisticated heuristics to deal with complex and dynamic production environments. However, as compared to other applications of genetic programming or scheduling applications of other evolutionary computation techniques, the configurations and requirements of genetic programming for production scheduling are more complicated. In this paper, a unified framework for automated design of production scheduling heuristics with genetic programming is developed. The goal of the framework is to provide the researchers with the overall picture of how genetic programming can be applied for this task and the key components. The framework is also used to facilitate our discussions and analyses of existing studies in the field. Finally, this paper shows how knowledge from machine learning and operations research can be employed and how the current challenges can be addressed.

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Section: Genetic Programming For Production Schedulingmentioning

confidence: 99%

Section: Component(s) To Be Evolvedmentioning

confidence: 99%

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Genetic programming for production scheduling: a survey with a unified framework

Nguyen

Mei

Zhang

2017

Complex Intell. Syst.

214

101

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“…The IPPS problem will be subjected to the following assumptions [13]  All parts are independent of each other and each machine can handle only one part at a time.  Multiple operations of the same part cannot be performed simultaneously, even on the same machine.…”

Section: A Problem Statementmentioning

confidence: 99%

A New Priority-Sort Based Optimization Algorithm for Integrated Process Planning and Scheduling

Aqel¹,

Ausaf²,

Li³

et al. 2013

IJMO

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Abstract-Modern manufacturing systems emphasize the need to improve the overall efficiency of the system and achieve global optimality rather than striving for excellence in isolated individual components. Integration of process planning and scheduling, which were previously treated as spate entities, has become an important area of research for accomplishing this goal. This paper presents a novel optimization algorithm for integrated process planning and scheduling (IPPS) problems. The algorithm is based on sorting the operations into different priorities. The experimental results show that the proposed algorithm can effectively solve IPPS problems.Index Terms-Distributed process planning, Integrated process planning and scheduling, Optimization algorithm, Priority-sort based optimization algorithm.

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“…[4][5][6][7] Process planning could be treated as traveling salesman problem (TSP). It is also a NP-hard problem [8][9][10] . In recent years, heuristic algorithms have shown their advantages in solving this kind of combinatorial optimization problems, and many approaches had been proposed for the optimization of process planning.…”

Section: Introductionmentioning

confidence: 99%

Optimization of process planning for cylinder block based on feature machining elements

Deng

Liu

et al. 2014

2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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For the high machining precision and various complicated constraints, process planning of cylinder block always receives lots of attention from industry and academic fields. FMEs (feature machining elements, FMEs) is proposed in this paper, which subdivided from design features and had a close correlation with machining methods according to machining accuracies and manufacturing resources. Therefore, the problem of process planning could be represented by an optimal sequencing and balancing of those FMEs. After analysis of constraints of features and FMEs, an optimal objective is established during a certain stage, and a process planning is generated and optimized by using an improved genetic simulated annealing algorithm (GSA). Finally, a case of cylinder block reveals that the proposed method is feasible and efficient.

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Optimization of flexible process planning by genetic programming

Cited by 42 publications

References 18 publications

Genetic programming for production scheduling: a survey with a unified framework

Genetic programming for production scheduling: a survey with a unified framework

A New Priority-Sort Based Optimization Algorithm for Integrated Process Planning and Scheduling

Optimization of process planning for cylinder block based on feature machining elements

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