Traditionally, process planning and scheduling functions are performed sequentially, where scheduling is implemented after process plans has been generated. Recent research works have shown that the integration of these two manufacturing system functions can significantly improve scheduling objectives. In this paper, we present a new hybrid method that integrates the two functions in order to minimize the makespan. This method is made up of a Shifting Bottleneck Heuristic as a starting solution, Tabu Search (TS) and the Kangaroo Algorithm metaheuristics as a global search. The performance of this newly hybrid method has been evaluated and compared with an integrated approach based on a Genetic Algorithm. Thereby, the characteristics and merits of the proposed method are highlighted.
The management of the queues by rules of priorities constitutes one of the simplest approaches and most used to dynamically schedule the tasks in a job shop. Unfortunately, one of the most important problems concerning the use of the rules of priority is the fact that no rule seems overall better than the others. To regulate these problems, we will combine rules of priorities and scheduling with selection of alternative routings in real time rule DMM. This combination will be realized according to the operating conditions, of the production targets and the state of the job-shop. Since the state of the workshop changes during time, we propose to analyze the state of the system each time a decision of scheduling must be taken, in order to take into account the real state of the job-shop. This approach will be implemented on a model of job-shop and will simulate by the simulation software ARENA.
Objective:Solving optimization problems is a key point in the constant improving of productivity in the industries. Introduction:When traditional methods fail, it is natural to look towards some approximate resolution methods. Memetic algorithm with population management is a Metaheuristic that has been conceived in the last years, and proved its power in the resolution of the difficult optimization problems. Material and Methods:In this paper, our interest is focused on the adaptation of an optimization algorithm called memetic Algorithm with Population Management based on the strategy of management of population to avoid slow or premature convergence and to carry out the excellent executions to solve real time alternative routings selection problem in a Flexible Manufacturing System (FMS), that consists of seven machining centres, a loading and an unloading area, and six different part types which have alternative routings. Results:Simulation results based on two performance indicators which are the production rate, cycle time, work in process and machines utilization rates show that the proposed algorithm performs the best compared to the genetic algorithm. Conclusion:Then we will make a comparison between this algorithm and the genetic algorithm previously used to solve the same problem to get an idea on the most efficient methods for this problem and choose the most effective.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2025 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.