Volume 28, Number 1 (IJIEPR 2017)                   IJIEPR 2017, 28(1): 9-20 | Back to browse issues page



DOI: 10.22068/ijiepr.28.1.9

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Khalilzadeh M, Hajikhani A, Sadjadi S J. A Fuzzy Multi-Objective Supplier Selection Model with price and wastages considerations. IJIEPR. 2017; 28 (1) :9-20
URL: http://ijiepr.iust.ac.ir/article-1-729-en.html

PHD Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Abstract:   (552 Views)

The present paper aims to propose a fuzzy multi-objective model to allocate order to supplier in uncertainty conditions and for multi-period, multi-source, and multi-product problems at two levels with wastages considerations.  The cost including the purchase, transportation, and ordering costs, timely delivering or deference shipment quality or wastages which are amongst major quality aspects, partial coverage of suppliers in respect of distance and finally, suppliers weights which make the products orders more realistic are considered as the measures to evaluate the suppliers in the proposed model. Supplier's weights in the fifth objective function are obtained using fuzzy TOPSIS technique. Coverage and wastes parameters in this model are considered as random triangular fuzzy number. Multi-objective imperial competitive optimization (MOICA) algorithm has been used to solve the model,. To demonstrate applicability of MOICA, we applied non-dominated sorting genetic algorithm (NSGA-II). Taguchi technique is executed to tune the parameters of both algorithms and results are analyzed using quantitative criteria and performing parametric. 

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Highlights:

1-formulate of problem of multi-objective supplier selection problem in SCM considering coverage from supplier’s       side.

2- Price discount for products by suppliers which are calculated using signal function.

3- supplier's weights in the fifth objective function are obtained using fuzzy TOPSIS technique.

4-delay and wastes from supplier side are considered as fuzzy parameters and produced as fuzzy random parameters.


Type of Study: Research | Subject: Operations Research
Received: 2017/03/14 | Accepted: 2017/06/11 | Published: 2017/06/18

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