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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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References

  1. Song D, Hicks C, Earl C (2002) Product due date assignment for complex assemblies. Int J Prod Econ 76(3):243–256

    Article  Google Scholar 

  2. Memari A, Rahim ARA, Ahmad R (2014) Multi-objective genetic algorithm in green just-in-time logistics. In: 2014 IEEE international conference on industrial engineering and engineering management, pp 1239–1243. IEEE

  3. Jayaraman V (1998) Transportation, facility location and inventory issues in distribution network design: an investigation. Int J Oper Prod Manag 18(5):471–494

    Article  Google Scholar 

  4. Shahvari O, Logendran R (2016) Hybrid flow shop batching and scheduling with a bi-criteria objective. Int J Prod Econ 179:239–258

    Article  Google Scholar 

  5. Memari A, Rahim ARA, Ahmad RB (2015) An integrated production-distribution planning in green supply chain: a multi-objective evolutionary approach. Procedia CIRP 26:700–705

    Article  Google Scholar 

  6. Al-e-Hashem SMJM, Aryanezhad MB, Sadjadi SJ (2012) An efficient algorithm to solve a multi-objective robust aggregate production planning in an uncertain environment. Int J Adv Manuf Technol 58(5–8):765–782

    Article  Google Scholar 

  7. Shahvari O, Salmasi N, Logendran R, Abbasi B (2012) An efficient tabu search algorithm for flexible flow shop sequence-dependent group scheduling problems. Int J Prod Res 50(15):4237–4254

    Article  Google Scholar 

  8. Pasandideh SHR, Niaki STA, Mousavi SM (2013) Two metaheuristics to solve a multi-item multiperiod inventory control problem under storage constraint and discounts. Int J Adv Manuf Technol 69(5–8):1671–1684

    Article  Google Scholar 

  9. Nikakhtar A, Wong KY, Zarei MH, Memari A (2011) Comparison of two simulation software for modeling a construction process. In: 2011 Third international conference on computational intelligence, modelling and simulation, pp 200–205. IEEE

  10. Shahvari O, Logendran R (2017) An enhanced tabu search algorithm to minimize a bi-criteria objective in batching and scheduling problems on unrelated-parallel machines with desired lower bounds on batch sizes. Comput Oper Res 77:154–176

    Article  MathSciNet  Google Scholar 

  11. Lalmazloumian M, Wong KY, Ahmadi M (2014) A mathematical model for supply chain planning in a build-to-order environment. In: Enabling manufacturing competitiveness and economic sustainability, pp 315–320. Springer International Publishing, Berlin

    Chapter  Google Scholar 

  12. Nikakhtar A, Abbasian-Hosseini SA, Gazula H, Hsiang SM (2015) Social network based sensitivity analysis for patient flow using computer simulation. Comput Ind Eng 88:264–272

    Article  Google Scholar 

  13. Ardjmand E, Young WA, Weckman GR, Bajgiran OS, Aminipour B, Park N (2016) Applying genetic algorithm to a new bi-objective stochastic model for transportation, location, and allocation of hazardous materials. Expert Syst Appl 51:49–58

    Article  Google Scholar 

  14. Lalmazloumian M, Wong KY, Govindan K, Kannan D (2016) A robust optimization model for agile and build-to-order supply chain planning under uncertainties. Ann Oper Res 240(2):435–470

    Article  MathSciNet  Google Scholar 

  15. Memari A, Rahim ARBA, Ahmad RB (2014) Production planning and inventory control in automotive supply chain networks. In: International conference on industrial, engineering and other applications of applied intelligent systems, pp 430–439. Springer International Publishing

  16. Lalmazloumian M, Wong KY (2012) A review of modelling approaches for supply chain planning under uncertainty. In: ICSSSM12, pp 197–203. IEEE

  17. Wang W, Fung RY, Chai Y (2004) Approach of just-in-time distribution requirements planning for supply chain management. Int J Prod Econ 91(2):101–107

    Article  Google Scholar 

  18. Farahani RZ, Elahipanah M (2008) A genetic algorithm to optimize the total cost and service level for just-in-time distribution in a supply chain. Int J Prod Econ 111(2):229–243

    Article  Google Scholar 

  19. Ding H, Benyoucef L, Xie X (2009) Stochastic multi-objective production distribution network design using simulation-based optimization. Int J Prod Res 47(2):479–505

    Article  Google Scholar 

  20. Ghasimi SA, Ramli R, Saibani N (2014) A genetic algorithm for optimizing defective goods supply chain costs using JIT logistics and each-cycle lengths. Appl Math Model 38(4):1534–1547

    Article  MathSciNet  Google Scholar 

  21. Memari A, Rahim ARA, Hassan A, Ahmad R (2016) A tuned NSGA-II to optimize the total cost and service level for a just-in-time distribution network. Neural Comput Appl 27(2):1–15

  22. Memari A, Rahim ARA, Absi N, Ahmad R, Hassan A (2016) Carbon-capped distribution planning: a JIT perspective. Comput Ind Eng 97:111–127

    Article  Google Scholar 

  23. Torabi SA, Hassini E (2009) Multi-site production planning integrating procurement and distribution plans in multi-echelon supply chains: an interactive fuzzy goal programming approach. Int J Prod Res 47(19):5475–5499

    Article  Google Scholar 

  24. Sadeghi J, Niaki STA (2015) Two parameter tuned multi-objective evolutionary algorithms for a bi-objective vendor managed inventory model with trapezoidal fuzzy demand. Appl Soft Comput 30:567–576

    Article  Google Scholar 

  25. Mousavi SM, Sadeghi J, Niaki STA, Alikar N, Bahreininejad A, Metselaar HSC (2014) Two parameter-tuned meta-heuristics for a discounted inventory control problem in a fuzzy environment. Inf Sci 276:42–62

    Article  MathSciNet  Google Scholar 

  26. Sadeghi J, Niaki STA, Malekian MR, Sadeghi S (2016) Optimising multi-item economic production quantity model with trapezoidal fuzzy demand and backordering: two tuned meta-heuristics. Eur J Ind Eng 10(2):170–195

    Article  Google Scholar 

  27. Lai Y-J, Hwang C-L (1992) A new approach to some possibilistic linear programming problems. Fuzzy Sets Syst 49(2):121–133

    Article  MathSciNet  Google Scholar 

  28. Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68

    Article  Google Scholar 

  29. Yeniay Ö (2005) Penalty function methods for constrained optimization with genetic algorithms. Math Comput Appl 10(1):45–56

    MathSciNet  Google Scholar 

  30. Tavana M, Li Z, Mobin M, Komaki M, Teymourian E (2016) Multi-objective control chart design optimization using NSGA-III and MOPSO enhanced with DEA and TOPSIS. Expert Syst Appl 50:17–39

    Article  Google Scholar 

  31. Khuri AI, Mukhopadhyay S (2010) Response surface methodology. Wiley Interdiscip Rev Comput Stat 2(2):128–149

    Article  Google Scholar 

  32. Taguchi G (1986) Introduction to quality engineering: designing quality into products and processes. Tokyo, Asian productivity organization, Bunkyo-ku, p 191. doi:10.1002/qre.4680040216

    Book  Google Scholar 

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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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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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