DOI: 10.1109/CSCWD.2013.6581034 Conference: Proceedings of the 2013 IEEE 17th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2013, At Wistler, Volume: Article number 6581034, Pages 633-638 A bi-objective model for collaborative planning in dyadic supply chain Hamza Ben Abdallah Université de Tunis El Manar, Faculté des Sciences de Tunis, LIP2-LR99ES18, 2092, Tunis, Tunisia Hamzabenabdallah88@yahoo.fr Zied Bahroun ESM Graduate Program, College of Engineering American University of Sharjah, P.O. Box 26666, Sharjah, United Aarab Emirates zbahroun@aus.edu Naoufel Cheikhrouhou Laboratory for Production Management and Processes Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland naoufel.cheikhrouhou@epfl.ch Mansour Rached Université de Tunis El Manar, Faculté des Sciences de Tunis, LIP2-LR99ES18, 2092 Tunis, Tunisia mansour.rached@hotmail.com Abstract—The collaborative planning and the management of production and storage processes are important components in supply chain management. The goal of this paper is to present the reliability of genetic algorithms on solving bi-objective models compared to mono-objective models. To do this we will be based initially on the mono-objective Dudek’s model and then we propose a division of the objective function in two objective functions. Finally we compare the results given by the genetic algorithms with the optimality result obtained using the LINGO solver on the mono-objective Dudek’s model. . This model aims at simultaneously minimizing the total production cost and the total holding cost. To solve the proposed model, we use a genetic algorithm NSGA-II. The proposed several test provide results that demonstrate and validate the effectiveness of the multi- objective approach and elitists genetic algorithms in solving this type of problem, compared to the literature in the proposed test. The validation of our approach will allow us later to use this algorithm in solving complex multi-objective models approaching the real context. Keywords— Supply Chain Management, Collaborative Planning, Mathematical Programming, Multi-Objective Optimization Model, Elitist Genetic Algorithm NSGA II I. INTRODUCTION Tactical planning is the determination of the quantities of products to be manufactured per period in order to meet as well as possible the demand at lower costs. The issues differ mainly according to two criteria: mono-level planning (Master Production Scheduling of finished products) or multi-level planning (finished products and components planning) and mono-site or multi-site planning. Commonly, the planning problems are formalized by mathematical models known as "lot sizing" problems. Among them, the "Capacitated Lot Sizing Problem" (CLSP) is considered as a reference model to treat the problems of generation of master production scheduling in a mono-site context. For the multi-level planning, "Multi Level Capacitated Lot Sizing Problem" (MLCLSP) is recognized as the reference model. If the mono- site problems were largely studied in the literature, the absence of a reference model for the multi-site issues is highlighted in [19]. This can be explained by the diversity of supply chains and treated problems. Nevertheless, from the multilevel nature of the multisite production, the models of the literature (see for instance [21] and [5]) are derived from the MLCLSP model. Integral part of the multisite planning problems, collaboration of the production plans of the various production sites has represented for several years a major stake of the supply chain optimization. The theoretical contribution of this work is to validate the elitists’ genetic algorithms to solving multi-objective problems in supply chain management, especially for the collaborative planning problem. Given that the proposed model is that the Dudek’s model developed in 2007 with the duplication of the objective function so as to obtain a bi-objective model. So, our experimental study is based, firstly, on the comparison of results obtained from the resolution of the mono-objective Dudek’s model and the bi-objective Dudek’s model (which is the proposed model) by the NSGA-II algorithm to validate the