Title: A new multi-objective model for supplier selection

Authors: Seyed Farid Shaikhahmadi; Rahman Soofifard; Seyed Fuad Qurayshi; Fariborz Jolai

Addresses: Department of Education, Research Institute of Petroleum Industry (RIPI), NIOC, West Blvd. Azadi Sport Complex, P.O. Box 14665-1998, Tehran, Iran ' Department of Education, Research Institute of Petroleum Industry (RIPI), NIOC, West Blvd. Azadi Sport Complex, P.O. Box 14665-1998, Tehran, Iran ' Faculty of Engineering, University of Tehran, Number 42, Bahman alley, Blvd. shebli, Sanandaj, Iran ' Department of Industrial Engineering, College of Engineering, University of Tehran, P.O. Box 11365/4563, Tehran, Iran

Abstract: In an order allocation model, selected evaluation criteria in most of the available papers are price, quality and delay in fulfilling the order. In most previous works done in this field the model has been composed of three objective functions that focus just on the mentioned criteria. But in recent years the concept of security risk has been brought up especially in category of strategy security. This kind of risk can adversely affect the entry flow of each one of the inflow sources to the organisation (including materials or services, manpower and …). The aim of the present work is to offer an approach for the purpose of optimising all purposes such as security risk in the form of independent targets. For an efficient search through the solution space we use a multi-objective genetic algorithm which allows us to identify a set of Pareto optimal solutions providing the decision maker with the complete spectrum of optimal solutions with respect to the various targets. Based on this information, the decision maker can select the best compromise among these objectives, without a priori introducing arbitrary weights. Finally, a real case study from Emerson Company is presented to display the efficiency of the proposed model.

Keywords: supplier selection; multiobjective optimisation; multiobjective genetic algorithms; MOGA; non-dominated sorting genetic algorithms; NSGA; order allocation; security risk; case study.

DOI: 10.1504/IJSOM.2015.065971

International Journal of Services and Operations Management, 2015 Vol.20 No.1, pp.43 - 58

Published online: 17 Apr 2015 *

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