## 1 Introduction  
Less-than-truckload (LTL) freight, also known as \"less-than-truckload cargo\", refers to goods with smaller volumes and weights that cannot fill an entire truckload. Given the small volume and weight of LTL cargo, it must be transported with other goods to save capacity resources and reduce costs. The distribution center usually serves as the loading point for LTL cargo in local surrounding areas, and trucks wait for the cargo to arrive at the distribution centers. The trucks are dispatched only after the loaded LTL cargo satisfies requirements. LTL freight matching refers to the process of pairing goods awaiting delivery with appropriate transport vehicles to optimize the delivery process in logistics applications. Cargo owners will provide information, such as cargo volume, weight, destination, and delivery time windows, while transportation vehicles are characterized by attributes such as capacity, load, and speed. In the process of vehicle-and-cargo matching, it is necessary to consider the attributes of the cargo and the lorry to maximize cost savings while ensuring that the cargo is delivered to its destination. Due to the special nature of LTL logistics, LTL goods do not arrive at the distribution center at the same time, so in addition to matching trucks and goods based on their attributes, LTL truck-and-cargo matching also needs to consider the truck's departure time. Numerous studies have been conducted on the LTL freight matching problem. [PERSON] [1] and others proposed and developed a web application to facilitate freight transportation and secure information about the shipment. The application efficiently minimizes the costs to both the shipper and the freight driver or the transport company. [PERSON] [2] and others proposed inter-related optimization and game models that allow us to analyze vehicle routing optimization for LTL carriers and carrier selection for customers, respectively. [PERSON] [3] and his colleagues proposed a two-phase method for addressing the hub location and routing problem (HLRP) in long-distance less-than-truckload (LTL) freight transportation networks. They validated the effectiveness of their approach using instances based on the AP dataset. [PERSON] et al. [4] utilized a time-space-based model to construct a scheme for lowering shipping transportation costs related to the LTL issue. [PERSON] et al. [5] designed an iterative request exchange module, which provides carriers with choices to find the optimal way to proceed in different situations. Along with handling multiple bundles of requests, the profits of transportation can be significantly improved. [PERSON] et al. [6] proposed a novel time-expanded network system for the less-than-truckload problem. With the employment of timely load plan arrangements, schedules are thoroughly considered in different situations without increasing the costs of transportation. [PERSON] et al. [7] designed an optimization model to find a cost-effective solution to the less-than-truckload issue, which found the key elements among decision-making solutions related to destinations. Similarly, [PERSON] et al. [8] raised an issue in solving dynamic less-than-truckload problems with the development of cost pricing models, and bidding strategies were considered to guide decisions on appointing carriers. [PERSON] et al. [9] designed an online matching algorithm by utilizing a time window to solve the issue of small-scale trucks. The system is available to solve real-life LTL problems. Similarly, the LTL matching problem can also be resolved by building dynamic models, such as a Bayesian network model or a neural network [10; 11], which describe the matching problem hierarchically. Although the LTL freight matching problem has multiple angles for optimization, matching accuracy remains a huge issue in designing algorithms. The Salp Swarm Algorithm (SSA) [12], initially proposed by [PERSON] et al.