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Scheduling Mobile Robots in Flexible Manufacturing System by An Adaptive Large Neighborhood Search

Published: 07 March 2020 Publication History

Abstract

Robots play an important role in the production and processing of auxiliary products in flexible manufacturing systems. Mobile robots can quickly and easily transport goods from warehouses to individual production workshops. This paper studies the path planning problem of using mobile robots to provide goods. This problem is called mobile robot path planning (MRPP) problem. An improved algorithm based on adaptive large neighborhood search called ALNSI is proposed. This algorithm integrates the characteristics of this problem into the search framework and has designed various destroy and repair methods according to the problem. Both the destroy and repair methods include strategies for tasks and mobile robots. A path reconstruction algorithm for ensuring the feasibility of scheme is also included in neighborhood search framework. The algorithm proposed in this paper has better performance in experimental verification than comparison algorithms and can be better used in flexible manufacturing systems.

References

[1]
Dang, Q. V., Nielsen, I. E., & Bocewicz, G. A Genetic Algorithm-Based Heuristic for Part-Feeding Mobile Robot Scheduling Problem. Trends in Practical Applications of Agents and Multiagent Systems, 2012.
[2]
Tazaki, Y., & Suzuki, T. Path planning of mobile robots considering position uncertainty and cost of observation. Sice Jcmsi, vol. 7, no. 3, pp. 183--190, 2014.
[3]
Huisman, R. Scheduling the refuelling activities of multiple heterogeneous autonomous mobile robots, 2014.
[4]
Hasgül, S., Saricicek, I., Ozkan, M., & Parlaktuna, O. Project-oriented task scheduling for mobile robot team. Journal of Intelligent Manufacturing, vol. 20, no. 2, pp. 151--158, 2009.
[5]
Mosallaeipour, S., Nejad, M. G., Shavarani, S. M., & Nazerian, R. Mobile robot scheduling for cycle time optimization in flow-shop cells, a case study. Production Engineering, vol. 12, no. 1, pp. 83--94, 2017.
[6]
Liu, S., Wu, H., Xiang, S., & Li, X. Mobile robot scheduling with multiple trips and time windows, advanced data mining and applications, pp. 608--620, 2017.
[7]
Nielsen, I., Do, N. A. D., Nielsen, P., & Khosiawan, Y. Material Supply Scheduling for a Mobile Robot with Supply Quantity Consideration---A GA-based Approach. International Conference Information Systems Architecture & Technology, pp. 41--52, 2016.
[8]
Wang, Q., Luo, H., Xiong, J., Song, Y., Zhang, Z. Evolutionary Algorithm for Aerospace Shell Product Digital Production Line Scheduling Problem. Symmetry, vol. 11, no. 849, 2019.
[9]
Song, Y., Ma, X., Li, X., Xing, L., Wang, P. Learning-guided nondominated sorting genetic algorithm II for multi-objective satellite range scheduling problem. Swarm and Evolutionary Computation. vol. 49, pp. 194--205, 2019.

Cited By

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  • (2021)Bi-objective grey wolf optimization algorithm combined Levy flight mechanism for the FMC green scheduling problemApplied Soft Computing10.1016/j.asoc.2021.107717111:COnline publication date: 1-Nov-2021

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    cover image ACM Other conferences
    ICCDE '20: Proceedings of 2020 6th International Conference on Computing and Data Engineering
    January 2020
    279 pages
    ISBN:9781450376730
    DOI:10.1145/3379247
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 07 March 2020

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    Author Tags

    1. Algorithm
    2. Insertion
    3. Location Selection
    4. Satellite measurement and control
    5. Scheduling

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    • (2021)Bi-objective grey wolf optimization algorithm combined Levy flight mechanism for the FMC green scheduling problemApplied Soft Computing10.1016/j.asoc.2021.107717111:COnline publication date: 1-Nov-2021

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