A Hybrid Grey Wolf Optimizer for Process Planning Optimization with Precedence Constraints

Process planning optimization is a well-known NP-hard combinatorial problem extensively studied in the scientific community. Its main components include operation sequencing, selection of manufacturing resources and determination of appropriate setup plans. These problems require metaheuristic-based approaches in order to be effectively and efficiently solved. Therefore, to optimize the complex process planning problem, a novel hybrid grey wolf optimizer (HGWO) is proposed. The traditional grey wolf optimizer (GWO) is improved by employing genetic strategies such as selection, crossover and mutation which enhance global search abilities and convergence of the traditional GWO. Precedence relationships among machining operations are taken into account and precedence constraints are modeled using operation precedence graphs and adjacency matrices. Constraint handling heuristic procedure is adopted to move infeasible solutions to a feasible domain. Minimization of the total weighted machining cost of a process plan is adopted as the objective and three experimental studies that consider three different prismatic parts are conducted. Comparative analysis of the obtained cost values, as well as the convergence analysis, are performed and the HGWO approach demonstrated effectiveness and flexibility in finding optimal and near-optimal process plans. On the other side, comparative analysis of computational times and execution times of certain MATLAB functions showed that the HGWO have good time efficiency but limited since it requires more time compared to considered hybrid and traditional algorithms. Potential directions to improving efficiency and performances of the proposed approach are given in conclusions.

Optimization of operation sequencing based on GA-Jaya algorithm

To solve the operation sequencing problem in CAPP that is a difficult problem, combining the idea of genetic algorithm, an GA-Jaya algorithm is proposed to minimize the total cost. In the GA-Jaya, the population is initialized according to the procedure priority adjacency matrix which makes the population all meet the process priority relationship. Mutation iteration operator and two kinds of crossover iteration operator are designed for process sequence and processing resource evolution. The GA-Jaya algorithm is applied to a typical case, and compared with the existing genetic algorithm, particle swarm optimization algorithm and ant colony optimization algorithm. The results show that the average quality of the solution obtained by the GA-Jaya algorithm is better than the existing genetic algorithm, particle swarm optimization algorithm and ant colony optimization algorithm.

Knowledge-Based Adaptation of Product and Process Design in Blisk Manufacturing

Early and efficient harmonization between product design and manufacturing represents one of the most challenging tasks in engineering. Concepts such as simultaneous engineering aim for a product creation process, which addresses both, functional requirements as well as requirements from production. However, existing concepts mostly focus on organizational tasks and heavily rely on the human factor for the exchange of complex information across different domains, organizations or systems. Nowadays product and process design make use of advanced software tools such as computer-aided design, manufacturing and engineering systems (CAD/CAM/CAE). Modern systems already provide a seamless integration of both worlds in a single digital environment to ensure a continuous workflow. Yet, for the holistic harmonization between product and process design, a complete and data consistent digital twin, an adaptation of product and process design for a balanced functionality and manufacturability, as well as systematic long-term data analytics across different product and process designs are missing. This paper presents an exploration concept which couples product design (CAD), process design (CAM), process simulation (CAE) and process adaptation in a single software system. The approach provides insights into correlations and dependencies between input parameters of product/process design and the process output. The insights potentially allow for a knowledge-based adaptation, tackling well-known optimization issues such as parameter choice or operation sequencing. First results are demonstrated using the example of a blade integrated disk (blisk).

Unified Genetic Algorithm Approach for Solving Flexible Job-Shop Scheduling Problem

This paper proposes a novel genetic algorithm (GA) approach that utilizes a multichromosome to solve the flexible job-shop scheduling problem (FJSP), which involves two kinds of decisions: machine selection and operation sequencing. Typically, the former is represented by a string of categorical values, whereas the latter forms a sequence of operations. Consequently, the chromosome of conventional GAs for solving FJSP consists of a categorical part and a sequential part. Since these two parts are different from each other, different kinds of genetic operators are required to solve the FJSP using conventional GAs. In contrast, this paper proposes a unified GA approach that enables the application of an identical crossover strategy in both the categorical and sequential parts. In order to implement the unified approach, the sequential part is evolved by applying a candidate order-based GA (COGA), which can use traditional crossover strategies such as one-point or two-point crossovers. Such crossover strategies can also be used to evolve the categorical part. Thus, we can handle the categorical and sequential parts in an identical manner if identical crossover points are used for both. In this study, the unified approach was used to extend the existing COGA to a unified COGA (u-COGA), which can be used to solve FJSPs. Numerical experiments reveal that the u-COGA is useful for solving FJSPs with complex structures.

Knowledge-Based Adaptation of Product and Process Design in Blisk Manufacturing

Early and efficient harmonization between product design and manufacturing represents one of the most challenging tasks in engineering. Concepts such as simultaneous engineering aim for a product creation process, which addresses both, functional requirements as well as requirements from production. However, existing concepts mostly focus on organizational tasks and heavily rely on the human factor for the exchange of complex information across different domains, organizations or systems. Nowadays product and process design make use of advanced software tools such as computer-aided design, manufacturing and engineering systems (CAD/CAM/CAE). Modern systems already provide a seamless integration of both worlds in a single digital environment to ensure a continuous workflow. Yet, for the holistic harmonization between product and process design, the following aspects are missing: • The digital environment does not provide a complete and data consistent digital twin of the component; this applies especially to the process design and analysis environment • Due to the lack of process and part condition data in the manufacturing environment an adaptation of product and process design for a balanced functionality and manufacturability is hindered • Systematic long-term data analytics across different product and process designs with the ultimate goal to transfer knowledge from one product to the next and to accelerate the entire product development process is not considered This paper presents an exploration concept which couples product design (CAD), process design (CAM), process simulation (CAE) and process adaptation in a single software system. The approach provides insights into correlations and dependencies between input parameters of product/process design and the process output. The insights potentially allow for a knowledge-based adaptation, tackling well-known optimization issues such as parameter choice or operation sequencing. First results are demonstrated using the example of a blade integrated disk (blisk).

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

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.

Guided Subtree Selection for Genetic Operators in Genetic Programming for Dynamic Flexible Job Shop Scheduling

© 2020, Springer Nature Switzerland AG. Dynamic flexible job shop scheduling (DFJSS) has been widely studied in both academia and industry. Both machine assignment and operation sequencing decisions need to be made simultaneously as an operation can be processed by a set of machines in DFJSS. Using scheduling heuristics to solve the DFJSS problems becomes an effective way due to its efficiency and simplicity. Genetic programming (GP) has been successfully applied to evolve scheduling heuristics for job shop scheduling automatically. However, the subtrees of the selected parents are randomly chosen in traditional GP for crossover and mutation, which may not be sufficiently effective, especially in a huge search space. This paper proposes new strategies to guide the subtree selection rather than picking them randomly. To be specific, the occurrences of features are used to measure the importance of each subtree of the selected parents. The probability to select a subtree is based on its importance and the type of genetic operators. This paper examines the proposed algorithm on six DFJSS scenarios. The results show that the proposed GP algorithm with the guided subtree selection for crossover can converge faster and achieve significantly better performance than its counterpart in half of the scenarios while no worse in all other scenarios without increasing the computational time.

Guided Subtree Selection for Genetic Operators in Genetic Programming for Dynamic Flexible Job Shop Scheduling

© 2020, Springer Nature Switzerland AG. Dynamic flexible job shop scheduling (DFJSS) has been widely studied in both academia and industry. Both machine assignment and operation sequencing decisions need to be made simultaneously as an operation can be processed by a set of machines in DFJSS. Using scheduling heuristics to solve the DFJSS problems becomes an effective way due to its efficiency and simplicity. Genetic programming (GP) has been successfully applied to evolve scheduling heuristics for job shop scheduling automatically. However, the subtrees of the selected parents are randomly chosen in traditional GP for crossover and mutation, which may not be sufficiently effective, especially in a huge search space. This paper proposes new strategies to guide the subtree selection rather than picking them randomly. To be specific, the occurrences of features are used to measure the importance of each subtree of the selected parents. The probability to select a subtree is based on its importance and the type of genetic operators. This paper examines the proposed algorithm on six DFJSS scenarios. The results show that the proposed GP algorithm with the guided subtree selection for crossover can converge faster and achieve significantly better performance than its counterpart in half of the scenarios while no worse in all other scenarios without increasing the computational time.