Research article Special Issues

Inventory replenishment decision model for the supplier selection problem using metaheuristic algorithms

  • Received: 12 June 2019 Accepted: 01 December 2019 Published: 23 December 2019
  • In supply chain management, fast and accurate decisions in supplier selection and order quantity allocation have a strong influence on the company's profitability and the total cost of finished products. In this paper, a novel and non-linear model is proposed for solving the supplier selection and order quantity allocation problem. The model is introduced for minimizing the total cost per time unit, considering ordering, purchasing, inventory, and transportation cost with freight rate discounts. Perfect rate and capacity constraints are also considered in the model. Since metaheuristic algorithms have been successfully applied in supplier selection, and due to the non-linearity of the proposed model, particle swarm optimization (PSO), genetic algorithm (GA), and differential evolution (DE), are implemented as optimizing solvers instead of analytical methods. The model is tested by solving a reference model using PSO, GA, and DE. The performance is evaluated by comparing the solution to the problem against other solutions reported in the literature. Experimental results prove the effectiveness of the proposed model, and demonstrate that metaheuristic algorithms can find lower-cost solutions in less time than analytical methods.

    Citation: Avelina Alejo-Reyes, Elias Olivares-Benitez, Abraham Mendoza, Alma Rodriguez. Inventory replenishment decision model for the supplier selection problem using metaheuristic algorithms[J]. Mathematical Biosciences and Engineering, 2020, 17(3): 2016-2036. doi: 10.3934/mbe.2020107

    Related Papers:

  • In supply chain management, fast and accurate decisions in supplier selection and order quantity allocation have a strong influence on the company's profitability and the total cost of finished products. In this paper, a novel and non-linear model is proposed for solving the supplier selection and order quantity allocation problem. The model is introduced for minimizing the total cost per time unit, considering ordering, purchasing, inventory, and transportation cost with freight rate discounts. Perfect rate and capacity constraints are also considered in the model. Since metaheuristic algorithms have been successfully applied in supplier selection, and due to the non-linearity of the proposed model, particle swarm optimization (PSO), genetic algorithm (GA), and differential evolution (DE), are implemented as optimizing solvers instead of analytical methods. The model is tested by solving a reference model using PSO, GA, and DE. The performance is evaluated by comparing the solution to the problem against other solutions reported in the literature. Experimental results prove the effectiveness of the proposed model, and demonstrate that metaheuristic algorithms can find lower-cost solutions in less time than analytical methods.


    加载中


    [1] S. C. P. Meindl, Supply chain management: Strategy, planning, and operations, Tsinghua University Press, (2016).
    [2] J. T. Mentzer, W. DeWitt, J. S. Keebler, S. Min, N. W. Nix, C. D. Smith, et al., Defining supply chain management, J. Bus. Logist., 22 (2001), 1-25.
    [3] S. Pazhani, J. A. Ventura, A. Mendoza, A serial inventory system with supplier selection and order quantity allocation considering transportation costs, Appl. Math. Model., 40 (2016), 612-634.
    [4] R. M. Monczka, R. Trent, R. B. Handfield, Purchasing & Supply Chain Management, Cengage Learning, 2015.
    [5] A. Mendoza, J. A. Ventura, Modeling actual transportation costs in supplier selection and order quantity allocation decisions, Oper. Res., 13 (2013), 5-25.
    [6] A. Fallahpour, E. U. Olugu, S. N. Musa, D. Khezrimotlagh, K. Y. Wong, An integrated model for green supplier selection under fuzzy environment: Application of data envelopment analysis and genetic programming approach, Neural Comput. Appl., 27 (2016), 707-725.
    [7] D. Zhang, J. Zhang, K. Lai, Y. Lu, An novel approach to supplier selection based on vague sets group decision, Expert Syst. Appl., 36 (2009), 9557-9563.
    [8] Y. L. Tsai, Y. J. Yang, C. H. Lin, A dynamic decision approach for supplier selection using ant colony system, Expert Syst. Appl., 37 (2010), 8313-8321.
    [9] A. M. Zeydan, C. Çolpan, C. Çobanoģlu, A combined methodology for supplier selection and performance evaluation, Expert Syst. Appl., 38 (2011), 2741-2751.
    [10] R. Verma, M. E. Pullman, An analysis of the supplier selection process, Omega, 26 (1998), 739-750.
    [11] L. D. Boer, E. Labro, P. Morlacchi, A review of methods supporting supplier selection, Eur. J. Purch. Supply Manag., 7 (2001), 75-89.
    [12] J. J. Bravo, C. J. Vidal, Freight transportation function in supply chain optimization models: A critical review of recent trends, Expert Syst. Appl., 40 (2013), 6742-6757.
    [13] H. Meersman, V. Charlotte, D. Bruckmann, M. Chen, J. Francke, P. Hillf, et al., Challenges and future research needs towards international freight transport modelling, Case Stud. Transp. Policy, 4 (2016), 3-8.
    [14] O. Ottemöller, H. Friedrich, Opportunities of sectoral freight transport demand modelling, Case Stud. Transp. Policy, 4 (2016), 9-12.
    [15] W. J. Baumol, H. D. Vinod, An inventory-theoretic model of freight transport demand, Manag. Sci., 16 (1970), 413-421.
    [16] J. E. Tyworth, Transport selection: Computer modeling in a spreadsheet environment, Int. J. Phys. Distrib. Logist. Manag., 21 (1991), 28-36.
    [17] J. R. Carter, B. G. Ferrin, Transportation costs and inventory management: Why transportation costs matter, Prod. Inventory Manag. J., 37 (1996), 58-62.
    [18] T. H. Burwell, D. S. Dave, K. E. Fitzpatrick, M. R. Roy, Economic lot size model for price-dependent demand under quantity and freight discounts, Int. J. Prod. Econ., 48 (1997), 141-155.
    [19] M. A. Darwish, Joint determination of order quantity and reorder point of continuous review model under quantity and freight rate discounts, Comput. Oper. Res., 35 (2008), 3902-3917.
    [20] O. K. Gupta, A lot-size model with discrete transportation costs, Comput. Ind. Eng., 22 (1992), 397-402.
    [21] B. Q. Rieksts, J. A. Ventura, Optimal inventory policies with two modes of freight transportation, Eur. J. Oper. Res., 186 (2008), 576-585.
    [22] Z. Zhang, X. Yan, S. Li, Analysis of a dynamic lot-sizing problem with production capacity constraint, Int. Trans. Oper. Res., 23 (2016), 813-833.
    [23] A. A. Reyes, A. Mendoza, E. O. Benitez, Inventory Replenishment Decisions for the Supplier Selection Problem Considering Transportation Freight Rates and Quality, OPENAIRE, (2018).
    [24] H. Alfares, R. Turnadi, Lot sizing and supplier selection with multiple items, multiple periods, quantity discounts, and backordering, Comput. Ind. Eng., 116 (2018), 59-71.
    [25] J. Y. Chai, J. N. K. Liu, E. W. T. Ngai, Application of decision-making techniques in supplier selection: a systematic review of literature, Expert Syst. Appl., 40 (2013), 3872-3885.
    [26] E. Triantaphyllou, Multi-Criteria Decision Making Methods, in Multi-criteria Decision Making Methods: A Comparative Study (ed. E. Triantaphyllou), Springer, (2000), 5-21.
    [27] J. Mula, D. Peidro, M. Díaz-Madroñero, E. Vicens, Mathematical programming models for supply chain production and transport planning, Eur. J. Oper. Res., 204 (2010), 377-390.
    [28] R. Eberhart, J. Kennedy, A new optimizer using particle swarm theory, Proceedings of the sixth international symposium on micro machine and human science, 1995. Available from: https://ieeexplore_ieee.xilesou.top/abstract/document/494215/.
    [29] J. R. Sampson, Adaptation in natural and artificial systems (John H. Holland), The University of Michigan Press, (1975).
    [30] C. K. H. Lee, A review of applications of genetic algorithms in operations management, Eng. Appl. Artif. Intell., 76 (2018), 1-12.
    [31] J. Luan, Z. Yao, F. Zhao, X. Song, A novel method to solve supplier selection problem: Hybrid algorithm of genetic algorithm and ant colony optimization, Math. Compu.t Simulat., 156 (2019), 294-309.
    [32] P. Mishra, I. Talati, Optimizing Integrated Production--Inventory Model for Time-Dependent Deteriorating Items Using Analytical and Genetic Algorithm Approach, in Soft Computing for Problem Solving (eds. J.C. Bansal, K. N. Das, A. Nagar, et al.), Springer, (2019), 535-546.
    [33] A. A. Taleizadeh, S. T. A. Niaki, N. Shafii, R. Ghavamizadeh, M. Jabbarzadeh, A particle swarm optimization approach for constraint joint single buyer-single vendor inventory problem with changeable lead time and (r,Q) policy in supply chain, Int. J. Adv. Manuf. Tech., 51 (2010), 1209-1223.
    [34] A. A. Taleizadeh, S. T. A Niaki, M. B. Aryanezhad, A. Fallah-Tafti, A genetic algorithm to optimize multi-product multi-constraint inventory control systems with stochastic replenishments and discount, Int. J. Adv. Manuf. Tech., 51 (2010), 311-323.
    [35] S. M. Mousavi, A. Bahreininejad, S. N. Musa, F. Yusof, A modified particle swarm optimization for solving the integrated location and inventory control problems in a two-echelon supply chain network, J. Intell. Manuf., 28 (2017), 191-206.
    [36] Y. Li, K. Ding, L. Wang, W. Zheng, Z. Peng, S. Guo, An optimizing model for solving outsourcing supplier selecting problem based on particle swarm algorithm, J. Ind. Prod. Eng., 35 (2018), 526-534.
    [37] F. Xiong, P. Gong, P. Jin, J. F. Fan, Supply chain scheduling optimization based on genetic particle swarm optimization algorithm, Cluster Comput., 22 (2019), 14767-14775.
    [38] J. Rezaei, M. Davoodi, A deterministic, multi-item inventory model with supplier selection and imperfect quality, Appl. Math. Model., 32 (2008), 2106-2116.
    [39] Z. Liao, J. Rittscher, A multi-objective supplier selection model under stochastic demand conditions, Int. J. Prod. Econ., 105 (2007), 150-159.
    [40] H. Kang, A. H. I. Lee, C. Wu, C. H. Lee, An efficient method for dynamic-demand joint replenishment problem with multiple suppliers and multiple vehicles, Int. J. Prod. Res., 55 (2017), 1065-1084.
    [41] J. Xu, Z. Zeng, B. Han, X. Lei, A dynamic programming-based particle swarm optimization algorithm for an inventory management problem under uncertainty, Eng. Optim., 45 (2013), 851-880.
    [42] Y. Wang, X. Geng, F. Zhang, J. Ruan, An Immune Genetic Algorithm for Multi-Echelon Inventory Cost Control of IOT Based Supply Chains, IEEE Access, 6 (2018), 8547-8555.
    [43] P. Radhakrishnan, V. M. Prasad, M. R. Gopalan, Genetic Algorithm Based Inventory Optimization Analysis in Supply Chain Management, 2019 IEEE International Advance Computing Conference, 2009. Available from: https://ieeexplore_ieee.xilesou.top/abstract/document/4809047.
    [44] Y. B. Woo, B. S. Kim, A genetic algorithm-based matheuristic for hydrogen supply chain network problem with two transportation modes and replenishment cycles, Comput. Ind. Eng., 127 (2019), 981-997.
    [45] R. Storn, K. Price, Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces, J. Global Optim., 11 (1997), 341-359.
    [46] A. Mendoza, J. A. Ventura, Estimating freight rates in inventory replenishment and supplier selection decisions, Logist. Res., 1 (2009), 185-196.
    [47] S. García, D. Molina, M. Lozano, F. Herrera, A study on the use of non-parametric tests for analyzing the evolutionary algorithms' behaviour: a case study on the CEC'2005 special session on real parameter optimization, J. Heuristics, 15 (2009). 617-644.
  • Reader Comments
  • © 2020 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(4621) PDF downloads(639) Cited by(11)

Article outline

Figures and Tables

Figures(5)  /  Tables(11)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog