| Popis: |
In light of the escalating global concerns surrounding climate change, the significance of sustainable development in the realm of logistics cannot be overstated. This study undertakes the imperative task of devising strategies aimed at mitigating carbon emissions, reducing logistics costs, minimizing transportation time, and enhancing customer satisfaction. The research delves into the intricacies of an optimization model tailored for a specific iteration of the Location-Routing Problem (LRP), namely the Multi-Objective Multi-Period Low-Carbon Location-Routing Problem (MMLCLRP). This variant of the LRP takes into meticulous consideration several crucial parameters, such as the overall logistics cost, the arrival times of demand points, and carbon emissions. These factors are pivotal in determining both the optimal location for depots and the programming of routes within a multi-period planning horizon. The proposed model guarantees the long-term sustainability of logistics operations while flexibly adapting location routing decisions for each period in response to evolving market demands. To tackle the inherent complexity of this problem, an improved version of the Non-dominated Sorting Genetic Algorithm (NSGA-II) was employed. This approach integrates a pioneering similarity distance metric to quantify the resemblance between potential solutions. Additionally, a crowding clustering strategy was implemented to enhance the diversity within the NSGA-II. Empirical results illustrate the capability of the proposed optimization model in effectively harmonizing various objectives, encompassing economic, efficiency, and environmental aspects within the logistics domain. Additionally, the enhanced algorithm exhibits notable advantages in addressing the complexities inherent in the optimization model. |
