<abstract> <p>Swarm intelligence algorithms are relatively simple and highly applicable algorithms, especially for solving optimization problems with high reentrancy, high stochasticity, large scale, multi-objective and multi-constraint characteristics. The sparrow search algorithm (SSA) is a kind of swarm intelligence algorithm with strong search capability, but SSA has the drawback of easily falling into local optimum in the iterative process. Therefore, a sine cosine and firefly perturbed sparrow search algorithm (SFSSA) is proposed for addressing this deficiency. Firstly, the Tent chaos mapping is invoked in the initialization population stage to improve the population diversity; secondly, the positive cosine algorithm incorporating random inertia weights is introduced in the discoverer position update, so as to improve the probability of the algorithm jumping out of the local optimum and speed up the convergence; finally, the firefly perturbation is used to firefly perturb the sparrows, and all sparrows are updated with the optimal sparrows using the firefly perturbation method to improve their search-ability. Thirteen benchmark test functions were chosen to evaluate SFSSA, and the results were compared to those computed by existing swarm intelligence algorithms, as well as the proposed method was submitted to the Wilcoxon rank sum test. Furthermore, the aforesaid methods were evaluated in the CEC 2017 test functions to further validate the optimization efficiency of the algorithm when the optimal solution is not zero. The findings show that SFSSA is more favorable in terms of algorithm performance, and the method's searchability is boosted. Finally, the suggested algorithm is used to the locating problem of emergency material distribution centers to further validate the feasibility and efficacy of SFSSA.</p> </abstract>

Emergencies cause uncertainty in supply chain environment and risks of disruption. Mitigating such risks in emergency supply chain relies on efficient relief material distribution, and in the distribution logistics system, emergency facility location interacts with material allocation clearly. This paper aims to provide a collaborative optimization for the location allocation of temporary emergency distribution centers, with objectives of minimizing rescue time and maximizing demand satisfaction rate. A location allocation model of emergency logistics is formulated by considering uncertain demand and supply information at the response stage of disaster relief. The model is solved by a plant growth simulation algorithm. At last, the feasibility and effectiveness of the model and algorithm in practical application are verified by evaluating a case of COVID-19 prevention and control in Handan city. This paper provides references for decision makers to accomplish the location allocation of emergency facilities and material distribution when dealing with actual situations.

Modern logistics is the supporting industry of the national economy. The synergy between logistics and economy has been highlighted in northern China’s Hebei Province. This study measures the coordination degree between economy and logistics in Hebei, drawing on the grey system theory. Specifically, the entropy weight-grey correlation method was introduced to evaluate the interplay between economic and logistics factors in Hebei between 2011 and 2020. The evaluation suggests that private car ownership has the most significant correlation with the economy and that the total retail sales of social consumer goods are the leading impactor of logistics. Next, the fractional grey model (FGM) (1, 1) was employed to forecast the economic and logistics indices of Hebei in the next five years. The forecast results show that FGM (1, 1) achieved a higher prediction precision than the conventional GM (1, 1) and discrete grey model (DGM) (1, 1). Based on the original data and forecasted results, the coupling coordination degree (CCD) model was adopted to compute the CCD between the economy and logistics in Hebei during 2011–2025. It was calculated that the coupling coordination exhibited a continuous upward trend. From 2011 to 2025, the CCD between economy and logistics in Hebei evolves from moderate incoordination, mild incoordination, weak incoordination, and weak coordination, all the way to moderate coordination. In the light of the analysis results, several suggestions were presented to promote the coordinated development between economy and logistics in Hebei.

<abstract> <p>Due to high requirements of storage, operation and delivery conditions, it is more difficult for cold chain logistics to meet the demand with supply in the course of disruption. And, accurate demand forecasting promotes supply efficiency for cold chain logistics in a changeable environment. This paper aims to find the main influential factors of cold chain demand and presents a prediction to support the resilience operation of cold chain logistics. After analyzing the internal relevance between potential factors and regional agricultural cold chain logistics demand, the grey model GM (1, N) with fractional order accumulation is established to forecast future agricultural cold chain logistics demand in Beijing, Tianjin, and Hebei. The following outcomes have been obtained. (1) The proportion of tertiary industry, per capita disposable income indices for urban households and general price index for farm products are the first three factors influencing the cold chain logistics demand for agricultural products in both Beijing and Tianjin. The GDP, fixed asset investment in transportation and storage, and the proportion of tertiary industry are three major influential factors in Hebei. (2) Agricultural cold chain demand in Beijing and Hebei will grow sustainably in 2021–2025, while the trend in Tianjin remains stable. In conclusion, regional developmental differences should be considered when planning policies for the Beijing-Tianjin-Hebei cold chain logistics system.</p> </abstract>