خلاصة:
Through the lens of supply chain management, integrating process planning decisions and scheduling plans becomes an issue of great challenge and importance. Dealing with the problem paves the way to devising operation schedules with minimum makespan; considering the flexible process sequences, it can be viewed as a fundamental tool for achieving the scheme, too. To deal with this integration, the modeling approach to problem with MIP structure is common in the literature. These models take precedence constraints into consideration to select machines and to determine sequences. In order to obtain viable sequences, we employed a proposed transformation matrix (TM). We also took advantage of an evolutionary search, called Learnable genetic Architecture (LEGA). Based on LEGA, we developed an integrated process planning and scheduling learnable genetic algorithm (IPPSLEGA). Our approach was evaluated with problems with various sizes. The experimental results show that our proposed architecture outperforms prior approaches, or it performs, at least, as efficiently as they do.
ملخص الجهاز:
Dealing with the problem paves the way to devising operation schedules with minimum makespan; considering the flexible process sequences, it can be viewed as a fundamental tool for achieving the scheme, too.
Based on LEGA, we developed an integrated process planning and scheduling learnable genetic algorithm (IPPSLEGA).
The rest of the paper is presented by the following briefly-introduced sections: Section 2 defines the studied problem; section 3 describes an integrated mathematical model dealing with process planning and scheduling; in section 4 the proposed architecture, IPPLEGA, is introduced; section 5 reports computational results, and the conclusion of this study is given in section 6.
IPPS involves determining the best schedules along with process plans with respect to precedence constraints, set of operations, given processing times, and the counterpart routes.
(View the image of this page) (View the image of this page) This problem can be described like this: A set of n jobs must be processed using m machines with alternative operation andcounterpart sequences.
(View the image of this page) Module 1: Population Generator A heuristic algorithm known as composite dispatching rules (CDR) is employed to produce proper initial solutions to elaborate the scheduling suitable flexible job-shop problem.
Gantt chart for experiment 1 Our result and Figure 11 show that the best sequence of operations is selected to perform each job and our proposed architecture significantly improves the makespan of the schedule.
Optimization of multi-objective integrated process planning and scheduling problem using a priority based optimization algorithm.
Integration of process planning and scheduling-A modified genetic algorithm-based approach.