Abstract
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.
Highlights
Process planning optimization (PPO) problems consist of two subproblems: operations selection and operations sequencing [1]
We present a novel hybrid grey wolf optimizer (HGWO) approach to address the PPO problem with various precedence constraints focusing on prismatic parts
Number 2 is added to the dOPG
Summary
Process planning optimization (PPO) problems consist of two subproblems: operations selection and operations sequencing [1]. Operations sequencing represents a task of finding the order of selected machining operations with respect to the predetermined precedence constraints based on relationships between machining features. Tackling with such a problem requires dealing with a number of alternatives which makes the PPO a combinatorial optimization problem. As a number of machining features increases, so does a number of machining operations This leads to exponential time growth required to find optimal or near-optimal process plans, which means that the PPO problem belongs to the class of NP-hard (non-deterministic polynomial) optimization problems
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.