Application of intelligent water drop algorithm in process planning optimization

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

“…Using these parameter settings, Part 1 was considered for three different conditions: The calculation results were obtained for each of the defined conditions and the best process schemes with minimized production cost are shown in Table 6. To present the experimental results more clearly, the comparison with results obtained by traditional and modern approaches from the literature is shown in Table 7 and includes the following comparative algorithms: IWD by Gao et al [6], CPAGA by Falih and Shammari [5], HBMO by Wen et al [14] and GA, SA and TS results also adopted from Wen et al [14]. The GCSA was run 20 times to verify the consistency of the obtained results.…”

“…Moreover, the tournament selection with the varying tournament size was included to prevent the algorithm from premature convergence and increased randomness. Gao et al [6] proposed the intelligent water drop algorithm in process planning optimization. A priority constraint matrix was used to generate feasible process plans.…”

“…Numerous studies conducted to this day have taken into account two most common evaluation criteria for operation sequencing, manufacturing (production) time and cost. According to [6,8], detailed information about tool paths and machining parameters is not available and therefore it is not possible to accurately determine the machining cost and machining time. Hence, the total cost of production is adopted as the optimization criterion in this research.…”

“…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:…”

“…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.…”