Volume 30, Issue 2 (IJIEPR 2019)                   IJIEPR 2019, 30(2): 225-239 | Back to browse issues page

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Salehi M, Rezaei H. A Fuzzy Multi-Objective Model for Allocating Orders to Suppliers Under Shortfall and Price-Quantity Discounts: An MPSO and NSGA-II with Tuned Parameters. IJIEPR 2019; 30 (2) :225-239
URL: http://ijiepr.iust.ac.ir/article-1-731-en.html
A Fuzzy Multi-Objective Model for Allocating Orders to Suppliers Under Shortfall and Price-Quantity Discounts: An MPSO and NSGA-II with Tuned Parameters
Mojtaba Salehi 1, Haniyeh Rezaei2
1- Industrial Engineering Department, K. N. Toosi University of Technology , mojtaba.salehi@kntu.ac.ir
2- payame noor university
Abstract:   (3937 Views)

In the new strategies of purchasing and production, suppliers play a key role in achieving of competitive capability for big companies. The selection of suitable suppliers is a critical component of this strategy. The problem of allocation order to supplier is a multi-objective problem that includes fuzzy parameters and also suppliers usually consider discount due to different levels of purchase amount. Since there is not a multi-objective fuzzy model for allocating orders in the literature that consider discount and shortfall simultaneously in the planning horizon of multi-products, this research proposes a new model includes minimization of costs, delays and the percentage of defective parts as objective functions. Jimenez method is used to defuzzy price, demand, delay and percentage of defective parts. Since the model is NP-hard, the two metaheuristic algorithms, NSGAII and MOPSO that their parameters were set by the Taguchi method has been developed. According to the results of numerical problems, the proposed algorithms can provide a good approximation of the optimal solutions for intended objectives.
 

Keywords: supplier selection, order allocation, discount, genetic algorithm, particle swarm optimization algorithm
Full-Text [PDF 903 kb]   (1906 Downloads)    
Type of Study: Research | Subject: Material Managment
Received: 2017/04/3 | Accepted: 2019/05/8 | Published: 2019/06/22
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