# A Genetic Crow Search Algorithm for Optimization of Operation Sequencing in Process Planning
###### 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. c 2021 16 February 2021 24 February 202  
As reported in [PERSON] et al. [3], the main activities of process planning, whether computer-aided or manual, are the following: receiving order and design details, selecting raw material and shape, selecting process technology, determining process order, preparing a process plan (including activities such as the selection of machining operations, sequencing of machining operations, selection of cutting tools, determination of setup requirements, calculations of cutting parameters, selection and design of jigs and fixtures, planning tool paths, estimating processing setup costs and times) and generating planning output (CNC programs, routing sheets, operation sheets etc.). The aim of these stages of process planning is to transform the part design (drawing of a part or a product) into a finished physical part or product in a cost-effective and competitive way. Among the mentioned activities, the sequencing of machining operations, i.e., the operation sequencing, has been considered as one of the most complex optimization tasks in scientific community. Two main steps of the operation sequencing task are taken into account: (1) selecting the most suitable machining resources such as machines, tools and tool approach directions based on the specific machining features that have to be machined and (2) finding the sequence of all machining operations for a considered part with respect to precedence relationships among operations and features. Such tasks, when handled simultaneously, form a process plan which is evaluated using certain optimization criteria, such as the minimization of production time or minimization of production cost. According to many authors such as [PERSON] et al. [4], [PERSON] and [PERSON] [5], [PERSON] et al. [6], [PERSON] et al. [7] and [PERSON] 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. [PERSON] 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. Many intelligent metaheuristics have been applied in operation sequencing and process planning optimization in the last few decades.