Title: A genetic algorithm for integrated lot sizing and supplier selection with defective items and storage and supplier capacity constraints

Authors: Mohammad Saeid Atabaki; Mohammad Mohammadi

Addresses: Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, 15719 – 14911 Tehran, Iran ' Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, 15719 – 14911 Tehran, Iran

Abstract: The single product, multi-period inventory lot-sizing problem is one of the most common and basic problems in the production and inventory management literature. In this paper, we consider an environment with multiple suppliers and multiple periods with supplier capacity and storage capacity constraints. Moreover, considering defective items, we move one-step toward a real environment of inventory problems. In this paper, we present the nonlinear programming of the problem. Since complexity of lot sizing problems belongs to a class of NP-hard problems, we propose a genetic algorithm to solve the problem. We develop a unique encoding-decoding procedure, which creates feasible solutions. Using the Taguchi experimental design method, the optimum parameters of the proposed genetic algorithm are selected. The result comparison between proposed GA and GAMS software as an exact solution for small and medium size problems shows that we can trust the proposed GA as a solution methodology for larger problems.

Keywords: lot sizing; supplier selection; defective items; genetic algorithms; Taguchi methods; experimental design; GAMS Software; storage capacity; supplier capacity; capacity constraints; inventory management; nonlinear programming.

DOI: 10.1504/IJOR.2017.081474

International Journal of Operational Research, 2017 Vol.28 No.2, pp.183 - 200

Received: 17 Dec 2014
Accepted: 13 May 2015
Published online: 10 Jan 2017 *

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