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Optimizing a Just-In-Time logistics network problem under fuzzy supply and demand: two parameter-tuned metaheuristics algorithms

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Abstract

Just-In-Time (JIT) is a popular philosophy in many industrial practices. The concept of JIT in early studies concerned with improving operational efficiency and waste minimization. In recent decades, however, JIT principles have also connected to logistics efficiency particularly for distribution of raw materials and finished goods. In the literature, several attempts have been made to optimize JIT logistics networks. On the one hand, most studies have typically focused on deterministic and small-scale problems which have been solved by exact algorithms. On the other hand, when large-scale problems were considered and usually were solved by metaheuristics algorithms, uncertainty sources and fine-tuning of the metaheuristics parameters were generally ignored. In this paper, we develop a mixed-integer linear optimization model to investigate a large-scale JIT logistics problem with 15 different sizes. To deal with different uncertainty sources, the customers demand and suppliers’ capacity as the two main sources of uncertainty in practice are considered as triangular fuzzy parameters. The proposed model aims to minimize total logistics cost including costs of transportation, inventory holding and backorders. A particle swarm optimization algorithm is applied to solve the problem, and its results are then validated by a harmony search algorithm. Both algorithms parameters are tuned using response surface methodology and Taguchi method. Finally, the conclusion and some directions for future research are proposed.

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Acknowledgements

The authors would like to thank Universiti Teknologi Malaysia (UTM) and Ministry of Higher Education (MOHE) Malaysia under Fundamental Research Grant Scheme (FRGS) Vot 4F850 for financial support provided throughout the course of this research. In addition, the first author is a Researcher of Universiti Teknologi Malaysia (UTM) Under the Post-Doctoral Fellowship Scheme (PDRU Grant) for the project: "A Tuned NSGA-II for Optimizing JIT Distribution Networks" (Vot No. Q.J130000.21A2.03E46).

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Authors and Affiliations

  1. Department of Engineering, UTM Razak School of Engineering and Advanced Technology, Universiti Teknologi Malaysia International Campus, 54100, Kuala Lumpur, Malaysia

    Ashkan Memari, Robiah Ahmad & Abd. Rahman Abdul Rahim

  2. Department of Materials, Manufacturing and Industrial Engineering, Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, 81310, Skudai, Malaysia

    Adnan Hassan

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  1. Ashkan Memari
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  2. Robiah Ahmad
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Correspondence to Ashkan Memari.

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On behalf of all coauthors, this research has not been submitted for publication nor has it been published in whole or in part elsewhere. We attest to the fact that all authors listed on the title page have contributed significantly to the work, have read the manuscript, attest to the validity and legitimacy of the data and its interpretation, and agree to its submission to the Journal of Neural Computing and Applications.

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Memari, A., Ahmad, R., Rahim, A.R.A. et al. Optimizing a Just-In-Time logistics network problem under fuzzy supply and demand: two parameter-tuned metaheuristics algorithms. Neural Comput & Applic 30, 3221–3233 (2018). https://doi.org/10.1007/s00521-017-2920-0

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