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Hybrid multiobjective genetic algorithms for integrated dynamic scheduling and routing of jobs and automated-guided vehicle (AGV) in flexible manufacturing systems (FMS) environment

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Abstract

The paper presents an algorithm for integrated scheduling, dispatching, and conflict-free routing of jobs and AGVs in FMS environment using a hybrid genetic algorithm. The algorithm generates an integrated schedule and detail routing paths while optimizing makespan, AGV travel time, and penalty cost due to jobs tardiness and delay as a result of conflict avoidance. The multi-objective fitness function use adaptive weight approach to assign weights to each objective for every generation based on objective improvement performance. Fuzzy expert system is used to control genetic operators using the overall population performance improvements of the last two previous generations. Computational experiments was conducted on the developed algorithm coded in Matlab to test the effectiveness of the algorithm. Integrated scheduling of jobs in FMS which are in synchrony with AGV dispatching, scheduling, and routing proved to ensure the feasibility and effectiveness of all the solutions of the integrated constituent elements.

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Authors and Affiliations

  1. Department of Mechanical and Manufacturing Engineering, Universiti Putra Malaysia, 43400 UPM, Serdang, Malaysia

    Umar Ali Umar, M. K. A. Ariffin, N. Ismail & S. H. Tang

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  1. Umar Ali Umar
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  2. M. K. A. Ariffin
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  3. N. Ismail
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  4. S. H. Tang
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Correspondence to Umar Ali Umar.

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Umar, U.A., Ariffin, M.K.A., Ismail, N. et al. Hybrid multiobjective genetic algorithms for integrated dynamic scheduling and routing of jobs and automated-guided vehicle (AGV) in flexible manufacturing systems (FMS) environment. Int J Adv Manuf Technol 81, 2123–2141 (2015). https://doi.org/10.1007/s00170-015-7329-2

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  • DOI: https://doi.org/10.1007/s00170-015-7329-2

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