An integrated multi-objective decision-making process for multi-period lot-sizing with supplier selection


Ustun O., Demirtas E.

OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE, vol.36, no.4, pp.509-521, 2008 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 36 Issue: 4
  • Publication Date: 2008
  • Doi Number: 10.1016/j.omega.2006.12.004
  • Title of Journal : OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE
  • Page Numbers: pp.509-521
  • Keywords: analytic network process, multi-objective mixed integer linear programming, reservation level driven Tchebycheff procedure, multi-criteria decision making, supplier selection, multi-period lot-sizing, INTEGER, ALGORITHM

Abstract

Supplier selection is a multi-criteria problem which includes both tangible and intangible factors. In these problems if suppliers have capacity or other different constraints two problems will exist: which suppliers are the best and how much should be purchased from each selected supplier? In this paper an integrated approach of analytic network process (ANP) and multiobjective mixed integer linear programming (MOMILP) is proposed. This integrated approach considers both tangible and intangible factors in choosing the best suppliers and defines the optimum quantities among selected suppliers to maximize the total value of purchasing (TVP), and to minimize the total cost and total defect rate and to balance the total cost among periods. The priorities are calculated for each supplier by using ANP. Four different plastic molding firms working with a refrigerator plant are evaluated according to 14 criteria that are involved in the four clusters: benefits, opportunities, costs and risks (BOCR). The priorities of suppliers will also be used as the parameters of the first objective function. This multi-objective and multiperiod real-life problem is solved by using previous techniques and a reservation level driven Tchebycheff procedure (RUP). Finally the most preferred nondominated solutions are determined by considering the decision maker's (DM's) preferences and the results obtained by these techniques are compared. (c) 2007 Elsevier Ltd. All rights reserved.