Abstract
In this paper, an improved Non-dominated Sorting Genetic Algorithm NSGA-II is proposed to solve the capacitated lot-sizing problem (CLSP) with backlogging, which is proved to be NP-hard problem. It consists to find the optimal production plan while minimizing simultaneously the total inventory level and the total cost, under capacity restriction. Based on the basic NSGA-II algorithm that is population-based metaheuristic, two main contributions are integrated. As first contribution, in order to solve the CLSP, effectively, an efficient chromosome representation is designed to determine the different decision variables related to the target problem. Moreover, to enhance the quality of the initial solutions, a new procedure that generates the initial population is developed and presented as second contribution of this work. The performance of the proposed improved NSGA-II algorithm is tested using different instances sizes (small, medium and large). Four metrics, which are commonly used in the literature, are considered to evaluate and compare the performance of the developed algorithm with the basic NSGA-II. These metrics are, namely, number of non-dominated solutions (NPS), maximum spread (MS), solutions quality (SQ) and execution time. Regarding the considered performance metrics, experimental results proved the effectiveness of the proposed improved NSGA-II algorithm compared to the basic one.
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Ben Ammar, H., Ben Yahia, W., Ayadi, O., Masmoudi, F. (2023). An Improved NSGA-II Algorithm for Solving Capacitated Lot-Sizing Problem. In: Walha, L., et al. Design and Modeling of Mechanical Systems - V. CMSM 2021. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-14615-2_34
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