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Purpose: This paper studies a 3-dimensional pallet loading problem considering interlock stacking pattern, box dimensions, humidity, and storage time, where overlapping and overhanging are not allowed. Despite the importance of this problem in the literature, our work provides the first method that considers the environmental conditions such as 1) storage time and 2) humidity, and their tremendous impacts on the strength of the boxes, as has been observed widely in the DHL supply chain.Design/methodology/approach: This paper proposes a two-phase heuristic algorithm to solve a 3-dimensional pallet loading problem under real conditions (relative humidity, and storage time) considering interlock stacking patterns, where overlapping and overhanging are not allowed. In phase 1, the horizontal layer configuration is determined by block techniques. Three types of horizontal layers are created based on box dimensions perpendicular to the base. In phase 2, a novel mathematical model is propounded to improve the pallet volume utilization, and stability considering the pallet's maximum allowable height and weight, and the dynamic compression strength of boxes. The dynamic compression strength of boxes is calculated by the modified McKee formula. Two performance measures, pallet volume utilization and stability (load height), are utilized to evaluate the performance of the proposed heuristic algorithm in real-world instances (DHL Supply Chain). Findings: The results illustrated that the dynamic compression strength of boxes decreases as the relative humidity and storage time increase. The load height changes dynamically along with box dimensions, box alignment, direction, relative humidity, and storage time. Increasing relative humidity and storage time and applying an interlock stacking pattern reduce the pallet utilization, however, enhance the pallet stability. Finally, the proposed heuristic algorithm's efficacy increases as the identical boxes dimensions' heterogeneity increases.Originality/value: It is believed in the supply chain where these characteristics are observed, the implementation of the heuristic algorithm will help them improve the pallet volume utilization and stability.

In supply chain management, the location of facilities, inventory control, and vehicle routing are three key components. This paper incorporates a two-warehouse inventory system into the location- inventory-routing problems (LIRPs) and develops LIRP models with two warehouses in one-level, two-level, and three-level supply chain networks. This study aims to minimize the average total costs of the models by reducing their average costs. To handle these models, two innovative hybrid algorithms, viz. Clarke and Wright—genetic algorithm (CW-GA) and Clarke and Wright—firefly algorithm (CW-FA) are put forward. Computational experiments and sensitivity analyses are conducted to compare the proposed two algorithms with Baron and test the algorithms’ effectiveness and the models’ feasibility. The management implications of this study are presented from two dimensions: model and method. Finally, future research directions and the gap between models and reality are discussed.

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.

This paper seeks to effectively realize energy saving and emission reduction in the process of location for fresh agricultural cold chain logistics. It is based on the traditional location for distribution center optimized for both freshness and carbon emissions. A bi-objective function location model was constructed to minimize the total cost and carbon emission and a two-stage heuristic algorithm was designed to solve the model. According to analyzing the location case of Y enterprise in Zhejiang Province, the THA had an average total cost optimization rate of 7.70% and an average carbon emission optimization rate of 10.23% compared to the WOA, while it had a rate of 12.77% and 14.12%, respectively, compared to the PSO. When the unit carbon emission cost increased from 0.02 dollar/kg to 0.06 dollar/kg, the comprehensive carbon emission cost increased by 42.07% and the total cost increased by 3.05%. Therefore, logistics enterprises can achieve the reduction of logistics costs and sustainable development through reasonable location for fresh agricultural products cold chain distribution centers.

With the continuous expansion and increasing operational complexity of high-speed railway networks, there is a growing need for more flexible and efficient EMU (Electric Multiple Unit) routing strategies. To address these challenges, in this paper, we propose a multi-day flexible circulation model that minimizes total connection time and deadheading mileage. A multi-commodity network flow model is formulated, incorporating constraints such as first-level maintenance intervals, storage capacity, train coupling/decoupling operations, and train types, with across-day consistency. To solve this complex model efficiently, a heuristic decomposition algorithm is designed to separate the problem into daily service chain generation and EMU assignment. A real-world case study in the Beijing–Baotou high-speed corridor demonstrates the effectiveness of the proposed approach. Compared to a fixed strategy, the flexible strategy reduces EMU usage by one unit, lowers deadheading mileage by up to 16.4%, and improves maintenance workload balance. These results highlight the practical value of flexible EMU deployment for large-scale, multi-day railway operations.

In the context of the carbon trading mechanism, this study investigated a multi-visit vehicle routing problem with a truck-drone collaborative delivery model. This issue involves the route of a truck fleet and drones, each truck equipped with a drone, allowing drones to provide services to multiple customers. Considering the carbon emissions during both the truck’s travel and the drone’s flight, this study established a mixed integer programming model to minimize the sum of fixed costs, transportation costs, and carbon trading costs. A two-stage heuristic algorithm was proposed to solve the problem. The first stage employed a “Scanning and Heuristic Insertion” algorithm to generate an initial feasible solution. In the second stage, an enhanced variable neighborhood search algorithm was designed with problem-specific neighborhood structures and customized search strategies. The effectiveness of the proposed algorithm was validated with numerical experiments. Additionally, this study analyzed the impact of various factors on carbon trading costs, revealing that there exists an optimal combination of drones and trucks. It was also observed that changes in carbon quotas do not affect carbon emissions but do alter the total delivery costs. These results provide insights for logistics enterprise operations management and government policy-making.

The protection, inheritance, and innovation of non-heritage tourism projects need to find a suitable entry point so that more people can participate in the inheritance and innovation of non-heritage in order to promote the long-term stable development of non-heritage tourism projects. The article establishes the framework for the ecological chain of non-heritage tourism projects based on the cultural ecological chain and analyzes the problems that exist in the development process of the ecological chain of non-heritage tourism. The optimization of the ecological chain of non-heritage tourism projects is considered a dynamic multi-objective optimization problem, and a dynamic planning model for tourism paths is established. The tourism path dynamic planning model is solved using a two-stage parallel optimization ant colony algorithm and a two-stage heuristic algorithm based on the ant colony algorithm. A simulation experiment was conducted to analyze the data and evaluate the effectiveness of the algorithm. The results show that the DACS algorithm can solve the optimal path of the ecological chain of non-heritage tourism projects with an average gap value of GAP between [10.88% and 18.71%]. The IGD of the DACS algorithm in the dMOP test case varies within 0.005, the average running time of the DACS algorithm is 29.72s, and the maximum deviation rate between the optimal solution of the algorithm and the actual optimal solution is only 3.85%. The use of the DACS algorithm can optimize the ecological chain model of non-heritage tourism projects and help the healthy development of non-heritage tourism ecological chains.

In the past, most researchers have paid attention to the storage space allocation problem in maritime container terminals, while few have studied this problem in rail–water intermodal container terminals. Therefore, this paper proposes a storage space allocation problem to look for a symmetry point between the efficiency and effectivity of rail–water intermodal container terminals and the unbalanced allocations and reallocation operations of inbound containers in the railway operation area, which are two interactive aspects. In this paper, a two-stage model on the storage space allocation problem is formulated, whose objective is to balance inbound container distribution and minimize overlapping amounts, considering both stacking principles, such as container departure time, weight and stacking height, and containers left in railway container yards from earlier planning periods. In Stage 1, a novel simulated annealing algorithm based on heuristics is introduced and a new heuristic algorithm based on a rolling horizon approach is developed in Stage 2. Computational experiments are implemented to verify that the model and algorithm we introduce can enhance the storage effect feasibly and effectively. Additionally, two comparison experiments are carried out: the results show that the approach in the paper performs better than the regular allocation approach and weight constraint is the most important influence on container storage.

To address the protracted blood transportation time prevalent in contemporary urban settings, we proposed a location-routing optimization problem tailored to the distribution of blood within intricate road networks. This involved a comprehensive assessment that encompassed the judicious selection of sites for both stations and blood centers, coupled with the meticulous planning of delivery routes for unmanned aerial vehicles (UAVs) that orchestrate the transportation of blood. First, a model was formulated to minimize the overall cost, including transportation expenses, costs associated with the site, and other relevant costs related to blood transportation vehicles coordinated by UAVs. Subsequently, a two-stage hybrid heuristic algorithm was designed based on the distinctive characteristics of the problem at hand. Moreover, an enhanced k-means algorithm was employed to generate clustering schemes, utilizing the centroid method to address the challenge of location selection for delivery sites effectively. A genetic algorithm enhanced with adaptive operators was employed to address the challenging large-scale NP-hard problem associated with route planning in intricate urban road networks. The results indicated that, compared to the traditional blood delivery model using vehicles, the total blood transportation cost decreased by 12.65% and the overall delivery time was reduced by 37.5% with the adoption of drone-assisted delivery; ultimately, case and sensitivity analyses were conducted to investigate the impact of variables including the number of blood transportation vehicles, UAVs, driver wages, and unit costs of blood transportation vehicles on the location-routing problem.

Under the trend of developing green transportation in China, tractor-and-trailer transportation has received more attention. This paper focuses on the network-type tractor-and-trailer transportation mode in the port hinterland, aiming to tackle the problems of low efficiency and customer satisfaction in the existing transportation network. The authors recommend considering opening several alternative depots and making vehicle scheduling decisions simultaneous in order to optimize the existing transportation network. Therefore, this paper constructs a bi-level programming model with a generalized total cost minimization as the objective function. The solution to the original problem is divided into two stages: the location-allocation problem and vehicle scheduling; a two-stage hybrid heuristic algorithm is designed to solve the problem. Through the continuous iteration of the upper genetic algorithm and the lower hybrid particle swarm algorithm, the overall optimization of the problem is achieved. Finally, a specific example verifies the model and the algorithm’s effectiveness. The results show that the method proposed in this paper can significantly improve customer satisfaction and reduce transportation costs to a certain extent. It can also provide effective theoretical decision support for logistics enterprises to carry out tractor-and-trailer transportation business and develop green transportation.