Fuzzy Multi-Objective Optimization Algorithms for Solving Multi-Mode Automated Guided Vehicles by Considering Machine Break Time and Artificial Neural Network

Hojat Nabovati, HASSAN HALEH, Behnam vahdani

Abstract


In this paper, a novel model is presented for machines and automated guided vehicles’ simultaneous scheduling, which addresses an extension of the blocking job shop scheduling problem. An artificial neural network approach is used to estimate machine’s breakdown indexes. Since the model is strictly NP-hard and because objectives contradict each other, two developed meta-heuristic algorithms called “fuzzy multi-objective invasive weeds optimization algorithm” and “fuzzy multi-objective cuckoo search algorithm” with a new chromosome structure which guarantees the feasibility of solutions are developed to solve the proposed problem. Since there is no benchmark available on literature, three other metaheuristic algorithms are developed with a similar solution structure to validate performance of the proposed algorithms. Computational results showed that developed fuzzy multi-objective invasive weeds optimization algorithm had the best performance in terms of solving problems compared to four other algorithms.


Keywords


Scheduling, AGV, MOIWO, MOCS, ANN

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References


Abdelmaguid TF, Nassef AO, Kamal BA, Hassan MF. A hybrid GA/heuristic approach to the simultaneous scheduling of machines and automated guided vehicles. International journal of production research2004. p. 267-81.

Reddy BSP, Rao CSP. A hybrid multi-objective GA for simultaneous scheduling of machines and AGVs in FMS. The International Journal of Advanced Manufacturing Technology. 2006;31:602-13.

Corréa AI, Langevin A, Rousseau L-M. Scheduling and routing of automated guided vehicles: A hybrid approach. Computers & operations research. 2007;34:1688-707.

Kesen SE, Baykoç ÖF. Simulation of automated guided vehicle (AGV) systems based on just-in-time (JIT) philosophy in a job-shop environment. Simulation Modelling Practice and Theory. 2007;15:272-84.

Lau HYK, Zhao Y. Integrated scheduling of handling equipment at automated container terminals. International Journal of Production Economics. 2008;112:665-82.

Deroussi L, Gourgand M, Tchernev N. A simple metaheuristic approach to the simultaneous scheduling of machines and automated guided vehicles. International Journal of Production Research. 2008;46:2143-64.

Brauner N, Finke G, Lehoux-Lebacque V, Potts C, Whitehead J. Scheduling of coupled tasks and one-machine no-wait robotic cells. Computers & Operations Research. 2009;36:301-7.

Gnanavel Babu A, Jerald J, Noorul Haq A, Muthu Luxmi V, Vigneswaralu TP. Scheduling of machines and automated guided vehicles in FMS using differential evolution. International Journal of Production Research. 2010;48:4683-99.

Che A, Hu H, Chabrol M, Gourgand M. A polynomial algorithm for multi-robot 2-cyclic scheduling in a no-wait robotic cell. Computers & Operations Research. 2011;38:1275-85.

Chaudhry I, Mahmood S, Shami M. Simultaneous scheduling of machines and automated guided vehicles in flexible manufacturing systems using genetic algorithms. Journal of Central South University of Technology. 2011;18:1473-86.

Brucker P, Burke EK, Groenemeyer S. A mixed integer programming model for the cyclic job-shop problem with transportation. Discrete Applied Mathematics. 2012;160:1924-35.

Batur GD, Karasan OE, Akturk MS. Multiple part-type scheduling in flexible robotic cells. International Journal of Production Economics. 2012;135:726-40.

Lacomme P, Larabi M, Tchernev N. Job-shop based framework for simultaneous scheduling of machines and automated guided vehicles. International Journal of Production Economics. 2013;143:24-34.

Zeng C, Tang J, Yan C. Scheduling of no buffer job shop cells with blocking constraints and automated guided vehicles. Applied Soft Computing. 2014;24:1033-46.

Narendranath S, Ramesh MR, Chakradhar D, Doddamani M, Bontha S, Nageswararao M, et al. International Conference on Advances in Manufacturing and Materials Engineering, ICAMME 2014Simultaneous Scheduling of Machines and AGVs in Flexible Manufacturing System with Minimization of Tardiness Criterion. Procedia Materials Science. 2014;5:1492-501.

Zheng Y, Xiao Y, Seo Y. A tabu search algorithm for simultaneous machine/AGV scheduling problem. International Journal of Production Research. 2014;52:5748-63.

Umar UA, Ariffin MKA, Ismail N, Tang SH. Hybrid multiobjective genetic algorithms for integrated dynamic scheduling and routing of jobs and automated-guided vehicle (AGV) in flexible manufacturing systems (FMS) environment. The International Journal of Advanced Manufacturing Technology. 2015;81:2123-41.

Nouri HE, Driss OB, Ghédira K. Hybrid metaheuristics for scheduling of machines and transport robots in job shop environment. Applied Intelligence. 2016:1-21.

Dang Q-V, Nguyen L. A Heuristic Approach to Schedule Mobile Robots in Flexible Manufacturing Environments. Procedia CIRP. 2016;40:390-5.

Kundu D, Suresh K, Ghosh S, Das S, Panigrahi BK, Das S. Multi-objective optimization with artificial weed colonies. Information Sciences. 2011;181:2441-54.

Yang X-S, Deb S. Cuckoo search: recent advances and applications. Neural Computing and Applications. 2014;24:169-74.

Deb K, Pratap A, Agarwal S, Meyarivan T. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation. 2002;6:182-97.

Zou F, Wang L, Hei X, Chen D, Wang B. Multi-objective optimization using teaching-learning-based optimization algorithm. Engineering Applications of Artificial Intelligence. 2013;26:1291-300.

Coello CAC, Pulido GT, Lechuga MS. Handling multiple objectives with particle swarm optimization. Evolutionary Computation, IEEE Transactions on. 2004;8:256-79.

Akbari M, Saedodin S, Panjehpour A, Hassani M, Afrand M, Torkamany MJ. Numerical simulation and designing artificial neural network for estimating melt pool geometry and temperature distribution in laser welding of Ti6Al4V alloy. Optik - International Journal for Light and Electron Optics. 2016;127:11161-72.

Kumar S. Neural networks: a classroom approach: Tata McGraw-Hill Education; 2004.

Zhang G, Patuwo BE, Hu MY. Forecasting with artificial neural networks:: The state of the art. International journal of forecasting. 1998;14:35-62.

Hecht-Nielsen R. Kolmogorov's mapping neural network existence theorem. Proceedings of the international conference on Neural Networks: IEEE Press; 1987. p. 11-4.

Okabe T, Jin Y, Sendhoff B. A critical survey of performance indices for multi-objective optimisation. Evolutionary Computation, 2003 CEC '03 The 2003 Congress on2003. p. 878-85 Vol.2.

Taguchi G. Introduction to quality engineering: designing quality into products and processes1986.

Rahmati SHA, Hajipour V, Niaki STA. A soft-computing Pareto-based meta-heuristic algorithm for a multi-objective multi-server facility location problem. Applied Soft Computing. 2013;13:1728-40.

Saaty RW. The analytic hierarchy process—what it is and how it is used. Mathematical Modelling. 1987;9:161-76.

Hwang C-L, Yoon K. Lecture Notes in Economics and Mathematical Systems: Multiple Attribute Decision Making: Methods and Appllication: Springer Verlag; 1981.




DOI: http://dx.doi.org/10.14311/NNW.2018.%25x

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