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GA-based discrete dynamic programming approach for scheduling in FMS environments.

¹Sch. of Manage., Univ. of Manchester Inst. of Sci. & Technol.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|February 5, 2008

Summary

This summary is machine-generated.

This study introduces a novel genetic algorithm (GA)-based discrete dynamic programming (DDP) method for flexible manufacturing system (FMS) scheduling. The approach efficiently generates static schedules by optimizing job sequences and avoiding common computational issues.

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Area of Science:

Operations Research
Computer Science
Manufacturing Engineering

Background:

Flexible Manufacturing Systems (FMS) require efficient scheduling for optimal performance.
Traditional scheduling methods often face challenges like state explosion and computational inefficiency.

Purpose of the Study:

To present a novel hybrid approach combining genetic algorithms (GA) and discrete dynamic programming (DDP) for static scheduling in FMS.
To develop a sequence-dependent schedule generation strategy that overcomes limitations of existing methods.

Main Methods:

A genetic algorithm (GA) is utilized to generate feasible job sequences, considering sequence-dependent characteristics.
Discrete dynamic programming (DDP) is employed to construct legal schedules for the sequences generated by the GA.
The approach incorporates different performance criteria within the GA formulation.

Main Results:

The developed GA-DDP algorithm effectively generates locally optimized partial schedules.
It demonstrates computational efficiency by leveraging dynamic programming principles.
The method avoids the state explosion problem common in pure dynamic programming for FMS scheduling.

Conclusions:

The GA-DDP approach offers an effective solution for static scheduling in flexible manufacturing systems.
This hybrid method provides a computationally efficient and scalable alternative to traditional scheduling algorithms.
The flexibility in incorporating performance criteria enhances its applicability to diverse FMS environments.