Title: A multi-objective optimisation model for cooperative supply chain planning

Authors: Wafa Ben Yahia; Naoufel Cheikhrouhou; Omar Ayadi; Faouzi Masmoudi

Addresses: Mechanics, Modelling and Production Research Laboratory, National School of Engineers of Sfax (ENIS), University of Sfax, Tunisia, Route de Sokra B.P.1173 – 3038, Sfax, Tunisia ' University of Applied Sciences Western Switzerland // HES-SO, Haute école de gestion de Genève, Switzerland Route de Drize 7, 1227 Geneva, Switzerland ' Mechanics, Modelling and Production Research Laboratory, National School of Engineers of Sfax (ENIS), University of Sfax, Tunisia, Route de Sokra B.P.1173 – 3038, Sfax, Tunisia ' Mechanics, Modelling and Production Research Laboratory, National School of Engineers of Sfax (ENIS), University of Sfax, Tunisia, Route de Sokra B.P.1173 – 3038, Sfax, Tunisia

Abstract: Generally, each member of a supply chain (SC) optimises his own individual objective and accordingly, plans his activities (e.g. production operations, inventories) without considering a global perspective. The goal of this work is the development of a multi-objective optimisation model for cooperative planning between different manufacturing plants belonging to the same SC. The model aims at minimising simultaneously the total production cost and the average of inventory level for several items and over a multi-period horizon. To solve this problem, a non-dominated sorting elitist genetic algorithm (NSGA-II) is developed to derive the Pareto front solutions. Several tests are developed to show the performance of the solution method and the behaviour of the cooperative planning model with respect to different demand patterns. The proposed model shows high performance in the tested cases with comparison to the literature.

Keywords: multi-objective optimisation; modelling; cooperative planning; supply chain management; SCM; genetic algorithms; NSGA-II; supply chain cooperation; supply chain planning; production costs; inventory levels.

DOI: 10.1504/IJSOM.2017.081491

International Journal of Services and Operations Management, 2017 Vol.26 No.2, pp.211 - 237

Received: 14 May 2015
Accepted: 10 Aug 2015
Published online: 10 Jan 2017 *

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