Hybrid constrained permutation algorithm and genetic algorithm for process planning problem

Falih, Abdullah; Shammari, Ahmed Z. M.

doi:10.1007/s10845-019-01496-7

Cited by 27 publications

(23 citation statements)

References 32 publications

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“…A priority constraint matrix was used to generate feasible process plans. Falih and Shammari [5] proposed a hybrid constrained permutation algorithm and genetic algorithm approach. A constrained permutation algorithm is employed to generate feasible operation sequences and a genetic algorithm is then used to search for an optimal solution within the feasible search space.…”

Section: Related Workmentioning

confidence: 99%

“…(4) Only one operation can be performed on a single machine. (5) Only one operation can be performed using a single cutting tool. (6) Only one TAD can be used in a given time.…”

Section: Operation Sequencing: Problem Formulationmentioning

confidence: 99%

“…Hence, the total cost of production is adopted as the optimization criterion in this research. The total production cost with weight coefficients consists of the five cost factors: The full description of all cost factors can be found in [4][5][6][7][8]11,14]. The total weighted production cost (TWPC) finally represents the sum of these five cost factors:…”

Section: Evaluation Criterion Of the Operation Sequencing Problemmentioning

confidence: 99%

“…The selection step is followed by the crossover operator whose purpose is to provide information exchange between two crow individuals from a flock. In this case, the threestring crossover (3SX) proposed by Falih and Shammari [5] was adopted. The 3SX operator affects only resource vectors, e.g., machine, tool and TAD vector respectively, leaving the operation sequence vector untouched and thereby assuring the feasibility of a process plan.…”

Section: Genetic Components Of the Gcsa Approachmentioning

confidence: 99%

“…According to many authors such as Duo et al [4], Falih and Shammari [5], Gao et al [6], Petrović et al [7] and Huang et al [8], the operation sequencing is classified as an NP hard optimization problem due to computational ineffectiveness when dealing with these problems using conventional non-heuristic techniques. Milošević et al [9] reported that metaheuristic algorithms have proven to be robust and efficient methods for these challenges and are therefore employed to find near-optimal solutions in an acceptable time period.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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

et al. 2021

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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.

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Section: Related Workmentioning

confidence: 99%

“…(4) Only one operation can be performed on a single machine. (5) Only one operation can be performed using a single cutting tool. (6) Only one TAD can be used in a given time.…”

Section: Operation Sequencing: Problem Formulationmentioning

confidence: 99%