The effective distribution of relief to an emergency logistics system plays a crucial role during the disaster response phase. Considering stochastic characteristics of relief demand, this study investigates the robust optimization of a multi-objective multi-period location-routing problem for epidemic logistics, a special emergency logistics, with uncertain scenarios. A corresponding robust multi-objective multi-period optimization model is proposed, which aims to determine the optimal location of temporary relief distribution centres and route planning simultaneously. The optimization objectives include the total travel time, the total cost, and the disutility of relief service. To solve the above optimization model, a preference-inspired co-evolutionary algorithm with Tchebycheff decomposition (PICEA-g-td) is given. The performance of the proposed PICEA-g-td is evaluated by comparing it with NSGA-II, MOEA/D and PICEA-g. The experimental results show that the proposed algorithm performs better than the other three algorithms in terms of the solution quality. Finally, some useful management insights are obtained.

In traditional financial analysis discriminant analyses with statistic methods are considered as primary and widely applied corporate credit rating bases in which human impact plays an essential role. Machine learning approaches enormously assist credit rating in reducing human interference and improving rating efficiency. However, existing methods did not efficiently leverage the time series features of financial data that is beneficial for the classification tasks. We propose a novel end-to-end architecture, the SMAGRU, based on self multi-head attention with gated recurrent neural networks, which is capable of evaluating a corporate credit rating by capturing the time series features assigned the weights of market benchmarks. Experiments compared with statistic and machine learning baseline models on different datasets show that our model contains superiority in multi-class credit rating classification. The result of ablation experiments suggests that time series features are crucial in classification performance, and self multi-head attention can significantly enhance these features and thereby improve the accuracy and convergence speed. We show that the SMAGRU generalizes well, even on sparse data during K-fold cross validation experiments.

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.

The particle swarm optimization (PSO) is a wide used optimization algorithm, which yet suffers from trapping in local optimum and the premature convergence. Many studies have proposed the improvements to address the drawbacks above. Most of them have implemented a single strategy for one problem or a fixed neighborhood structure during the whole search process. To further improve the PSO performance, we introduced a simple but effective method, named adaptive particle swarm optimization with Gaussian perturbation and mutation (AGMPSO), consisting of three strategies. Gaussian perturbation and mutation are incorporated to promote the exploration and exploitation capability, while the adaptive strategy is introduced to ensure dynamic implement of the former two strategies, which guarantee the balance of the searching ability and accuracy. Comparison experiments of proposed AGMPSO and existing PSO variants in solving 29 benchmark functions of CEC 2017 test suites suggest that, despite the simplicity in architecture, the proposed AGMPSO obtains a high convergence accuracy and significant robustness which are proven by conducted Wilcoxon’s rank sum test.

The outbreak of an epidemic disease may cause a large number of infections and a slightly higher death rate. In response to epidemic disease, both patient transfer and relief distribution are significant to reduce corresponding damage. This study proposes a two-stage multi-objective stochastic model (TMS-PTRD) considering pre-pandemic preparedness measures and post-pandemic relief operations. The proposed model considers the following four objectives: the total number of untreated infected patients, the total transfer time, the overall cost, and the equity distribution of relief supplies. Before an outbreak, the locations of temporary relief distribution centers (TRDCs) and the inventory levels of established TRDCs should be determined. After an outbreak, the locations of temporary hospitals (THs), the locations of designated hospitals (DHs), the transfer plans for patients, and the relief distribution should be determined. To solve the TMS-PTRD model, we address an improved preference-inspired co-evolutionary algorithm named the PICEA-g-AKNN algorithm, which is embedded with a novel similarity distance and three different tailored evolutionary strategies. A real-world case study of Hunan of China and 18 test instances are randomly generated to evaluate the TMS-PTRD model. The finding shows that the PICEA-g-AKNN algorithm is better than some most widely used multi-objective algorithms.