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Abstract
Recently, many production systems that work in dynamic environment are widely used in high volume industries. Increasing the product varieties in dynamic environment Flexible Manufacturing Systems (FMS) have gained more importance due to growing production lines' complexity. Increasing the customers demand to diversified products and rapid changing of the need to certain types of products require an FMS with high ability to adapt any change in production ratio during production. This paper introduces a Two Stages Approach (TSA) to increase the flexibility of the FMS to any change of the production ratios during production. The first stage of TSA is to propose a Genetic Algorithm (GA) based Production Simulator (GA-PS) to maximize the FMS throughput by optimizing the FLXible Routes (FLXR) for all products type at a given production ratio. The second stage is to propose a Neural Network (NN) approach to predict the routes of all products in FMS that can be used efficiently instead of a long, time-consuming GA-PS. TSA can improve the response of FMS to any change of the production ratio by finding a new FLXR that achieves the optimal throughput of the FMS within the new production ratios. Numerical examples will be applied to demonstrate applicability of the proposed approach. As a result, it could be ascertained that the proposed TSA is useful.
Highlights
Variety of products produce by Flexible Manufacturing Systems (FMS) have gained more and more importance because of growing production lines’ complexity and production costs and because an increasing demands for customized products
Rapid change in the mood of consumers and change the need of the markets to certain types of products required an FMS with high ability adapt to any required change in production ratio
In order to find the number of part that can be produced by a given time, the total time, TTpp, which the product type p spends during its trip through the FMS from the moment it leaves the load/unload station until it returns back to load/unload station is needed
Summary
Variety of products produce by Flexible Manufacturing Systems (FMS) have gained more and more importance because of growing production lines’ complexity and production costs and because an increasing demands for customized products. Production scheduling is still one of the most important factors that can improve the productivity of FMS because the FMS is complicated system and have a many unpredictable conditions, especially in dynamic environments. These characteristics of FMS required an artificial intelligence and heuristic-based approaches to optimize the FMS scheduling. A Two Stages Approach (TSA) is proposed to increase the flexibility of the FMS to any change of the production ratio. The well-known Genetic Algorithms based on Production Simulator (GA-PS) is proposed. No of operations of part p. time to move from load\unload station to machine of first operation time to move from last operation to load\unload station time to move from location a to location b fitness of individual number i
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