A Genetic Crow Search Algorithm for Optimization of Operation Sequencing in Process Planning

Mica Djurdjev,Dejan Lukic,Mijodrag Milosevic,Aco Antic,Branislav Popovic,Robert Cep

doi:10.3390/app11051981

Mica Djurdjev, Dejan Lukic + Show 4 more

Open Access

https://doi.org/10.3390/app11051981

Copy DOI

Abstract

Computer-aided process planning represents the main link between computer-aided design and computer-aided manufacturing. One of the crucial tasks in computer-aided process planning is an operation sequencing problem. In order to find the optimal process plan, operation sequencing problem is formulated as an NP hard combinatorial problem. To solve this problem, a novel genetic crow search approach (GCSA) is proposed in this paper. The traditional CSA is improved by employing genetic strategies such as tournament selection, three-string crossover, shift and resource mutation. Moreover, adaptive crossover and mutation probability coefficients were introduced to improve local and global search abilities of the GCSA. Operation precedence graph is adopted to represent precedence relationships among features and vector representation is used to manipulate the data in the Matlab environment. A new nearest mechanism strategy is added to ensure that elements of machines, tools and tool approach direction (TAD) vectors are integer values. Repair strategy to handle precedence constraints is adopted after initialization and shift mutation steps. Minimization of total production cost is used as the optimization criterion to evaluate process plans. To verify the performance of the GCSA, two case studies with different dimensions are carried out and comparisons with traditional and some modern algorithms from the literature are discussed. The results show that the GCSA performs well for operation sequencing problem in computer-aided process planning.

Highlights

Introduction published maps and institutional affilAccording to the Society of Manufacturing Engineers, process planning is considered as a systematic definition of methods for economical and productive manufacturing
Xu et al [1] it is considered that computer-aided process planning (CAPP), as a crucial component of todays advanced manufacturing systems, can be imagined as the “bridge”
To verify the flexibility and feasibility of the genetic crow search algorithm (GCSA) approach, we proposed two case studies with several conditions

Summary

Introduction

According to the Society of Manufacturing Engineers, process planning is considered as a systematic definition of methods for economical and productive manufacturing. In. Xu et al [1] it is considered that computer-aided process planning (CAPP), as a crucial component of todays advanced manufacturing systems, can be imagined as the “bridge”. Between the product design (CAD) and product manufacturing (CAM). CAPP system development has shown to be a very complex task, assuming today’s high diversity of products and complexity of planning numerous activities within a process plan in different manufacturing conditions. The consequence reflects the fact that CAPP systems are at the lower level of implementation in industry and represent a bottleneck in integrated production environment, as stated by Lukić et al [2]. The consequence reflects the fact that CAPP systems are at the lower level of implementation in industry and represent a bottleneck in integrated production environment, as stated by Lukić et al [2]. iations.

Methods

Results

Conclusion