The distribution of emergency perishable materials after a disaster, such as an earthquake, is an essential part of emergency resource dispatching. However, the traditional single-period distribution model can hardly solve this problem because of incomplete demand information for emergency perishable materials in affected sites. Therefore, for such problems we firstly construct a multi-period vehicle path distribution optimization model with the dual objectives of minimizing the cost penalty of distribution delay and the total corruption during delivery, and minimizing the total amount of demand that is not met, by applying the interval boundary and most likely value weighting method to make uncertain demand clear. Then, we formulate the differential evolutionary whale optimization algorithm (DE-WOA) combing the differential evolutionary algorithm with the whale algorithm to solve the constructed model, which is an up-and-coming algorithm for solving this type of problem. Finally, to validate the feasibility and practicality of the proposed model and the novel algorithm, a comparison between the proposed model and the standard whale optimization algorithm is performed on a numerical instance. The result indicates the proposed model converges faster and the overall optimization effect is improved by 23%, which further verifies that the improved whale optimization algorithm has better performance.

Green innovation has emerged as a key catalyst for the sustainable growth of logistics enterprises. Green innovation not only helps logistics enterprises reduce operating costs but also enhances their competitiveness and promotes the entire industry’s transformation towards environmental protection and efficiency. However, logistics enterprises encounter technical bottlenecks, capital shortages, and insufficient talent and infrastructure when implementing green innovation. Collaboration between universities and industries serves as a crucial method for logistics companies to access external resources and plays a significant role in promoting technological progress, knowledge transfer, and innovation capability enhancement of enterprises. This research, grounded in the theories of social capital and dynamic capabilities, explores the mechanism from the perspective of resources and capabilities, and examines how university–industry collaboration affects green innovation. This research employs a hierarchical regression model to evaluate the proposed hypotheses. The research results show that university–industry collaboration has a positive impact on social capital, slack resources, and dynamic capabilities, and social capital, slack resources, and dynamic capabilities positively influence green innovation. The research results have certain reference value for logistics enterprises to promote green innovation.

A good port terminal is not only a major economic multiplier for the nation’s prosperity by being a gateway for trading, but is also an attractor for other commercial infrastructure development such as banks, logistics agencies, and manufacturing and trading investments. A measurement of the efficiency of a terminal is the duration-of-stay of visiting vessels. A quick and efficient loading/unloading process can increase productivity and thus reduce the waiting time for a vessel. In this study, we address the space allocation for stacking export containers. If the storage layout and the loading plan work well together, the productivity of the terminal can be increased and the duration-of-stay needed for each visiting vessel is reduced. In this paper, we propose a hybrid storage policy combining class-dedicated and sharing strategies, and construct a stochastic programming model using the concept of recourse.

As the logistics industry modernizes, living standards improve, and consumption patterns shift, the demand for fresh food continues to grow, making cold chain logistics for perishable goods a critical component in ensuring food quality and safety. However, the presence of both soft and hard time windows among demand nodes can complicate the single-network distribution of perishable goods. In response to these challenges, this paper proposes an optimization model for multi-distribution center perishable goods delivery, considering both one-echelon and two-echelon network joint distributions. The model aims to minimize total costs, including transportation, fixed, refrigeration, goods damage, and penalty costs, while measuring customer satisfaction by the start time of service at each demand node. A two-stage heuristic algorithm is designed to solve the model. In the first stage, an initial solution is constructed using a greedy approach based on the principles of the k-medoids clustering algorithm, which considers both spatial and temporal distances. In the second stage, the initial routing solution is optimized using a linear programming approach from the Ortools solver combined with an Improved Adaptive Large Neighborhood Search (IALNS) algorithm. The effectiveness of the proposed model and algorithm is validated through a case study analysis. The results demonstrate that the initial solutions obtained through the k-medoids clustering algorithm based on spatio-temporal distance improved the overall cost optimization by 1.85% and 4.74% compared to the other two algorithms. Among the three two-stage heuristic algorithms, the Ortools-IALNS proposed here showed enhancements in the overall cost optimization over the IALNS, with improvements of 3.24%, 1.12%, and 0.41%, respectively. The two-stage heuristic algorithm designed in this study also converged faster than the other two heuristic algorithms, with overall optimization improvements of 1.55% and 1.28%, further validating the superior performance of the proposed heuristic algorithm.

In this study, an effective travel cost (ETC) traffic equilibrium model is proposed for multi-modal networks with uncertain demands. The multi-modal networks are transformed into supernetworks and travel demands are assumed to be closed intervals. Passenger flows and travel costs are also formulated as closed intervals to capture the effects of uncertain demands. The ETC concept is introduced and regarded as a choice criterion to develop an equilibrium model which captures travellers’ travel mode and route choice behavior under interval travel costs. The model is formulated as a variational inequality problem, and the method of successive average algorithm is adapted to interval mathematics to obtain the results in the form of interval variables. Illustrative examples are also presented to demonstrate the model’s characteristics and its differences from the traditional equilibrium model, in which the expected travel cost is regarded as the choice criterion.

In city traffic management, traffic breakdown is a very important issue, which is defined as a speed drop of a certain amount within a dense traffic situation. In order to predict city traffic flow breakdown accurately, in this paper, we propose a novel city traffic flow breakdown prediction algorithm based on fuzzy rough set. Firstly, we illustrate the city traffic flow breakdown problem, in which three definitions are given, that is, 1) Pre-breakdown flow rate, 2) Rate, density, and speed of the traffic flow breakdown, and 3) Duration of the traffic flow breakdown. Moreover, we define a hazard function to represent the probability of the breakdown ending at a given time point. Secondly, as there are many redundant and irrelevant attributes in city flow breakdown prediction, we propose an attribute reduction algorithm using the fuzzy rough set. Thirdly, we discuss how to predict the city traffic flow breakdown based on attribute reduction and SVM classifier. Finally, experiments are conducted by collecting data from I-405 Freeway, which is located at Irvine, California. Experimental results demonstrate that the proposed algorithm is able to achieve lower average error rate of city traffic flow breakdown prediction.