Nowadays, managers are looking for adequate strategies and advanced logistics management models in order to achieve their competitiveness and improve their overall performance. The current literature on supply network planning has not adequately incorporated sustainability factors, uncertainties, and the integration of various levels of planning (operational, tactical and strategic). In this paper, we address the integrated planning of logistics networks in an uncertain and sustainable environment. The problem is formulated as a two-stage stochastic programming which minimizes the logistics costs and evaluates a posteriori the CO2 emissions and the transport accident risk. This model is solved using the Sample Average Approximation (SAA) approach. Numerical experiments are performed using CPLEX to study the impact of different parameters on sustainability aspects.

Several variants of particle swarm optimization (PSO) have been created to identify various solutions to compli-cated optimization problems. Only a few PSO algorithms exist that can locate and monitor multiple optima in dynamically shifting search landscapes when dealing with dynamic optimization situations. These methods have yet to be thoroughly tested on a large number of dynamic optimization problems. In fact, because there are so many PSO algorithm modifications, it's simple to get stuck in a local optima. To address the aforementioned flaws, this work proposes and evaluates an enhanced version of the multiswarm particle swarm optimization technique (MsPSO) with numerous variations particle swarm optimization published in the literature. Standard tests and indicators provided in the specialized literature are used to verify the effectiveness of the suggested algorithm. Furthermore, on the CEC’ 13 test suite, comparison results between the extended heterogeneous multi swarm PSO algorithm (XMsPSO) and other nine popular PSO show that XMsPSO achieves a very optimistic performance for solving various kinds of problems, contributing to both higher solution accuracy.