[PERSON] et al. (2016) [17] introduced the constructed optimization model based on the water flow algorithm (WFA) to address the intermittent street gridlock issue. [PERSON] et al. (2018) [18] proposed a pickup and drop-off issue for the cross-docking technique, in which shipments from providers to retailers are directly cross-docked. [PERSON] and [PERSON] (2018) [19] presented a MILP model of an exceptional multiple cross-docked VRP. [PERSON] et al. (2018) [20] proposed a vehicle routing and scheduling issue with cross-docking and time windows in a three-echelon store network. [PERSON] et al. (2018) [21] introduced truck scheduling in a two-touch cross-dock with hard due dates for outbound trucks. [PERSON] et al. (2018) [22] proposed a Lagrangian metaheuristic methodology for the cross-dock scheduling issue with parallel docks. [PERSON] et al. (2018) [23] proposed eight new MIP models and exhibited the numerical identicalness of every one of the 11 models, along with thoroughly demonstrating a portion of their properties. [PERSON] et al. (2018) [24] introduced a bi-objective model for a green truck scheduling and routing problem at a cross-docking framework. [PERSON] et al. (2018) [25] introduced two new MIP mathematical formulations for cross-dock door assignment problems (CDAPs). [PERSON] et al. (2018) [26] considered a production network including the multi-provider and a solitary client. They explored the impact of joining on parts transport. [PERSON] et al. (2019) [27] proposed a practical version of a vehicle routing problem with cross-docking. They planned three metaheuristic algorithms, namely a hybrid GA with modified VNS, ABC, and SA to solve large-scale problems. [PERSON] et al. (2019) [28] created five heuristic algorithms for managing the truck planning issue in a cross-docking framework. [PERSON] et al. (2019) [29] proposed an integrated scheduling and assignment of trucks at unit-load cross-dock terminals with mixed service mode dock doors. They created an adaptive large neighborhood search algorithm for the integrated problem. [PERSON] et al. (2019) [30] proposed an optimization model, which coordinated the planning tasks of truck and workforce scheduling in a cross-dock for a solitary working day. [PERSON] (2019) [31] concentrated on a truck scheduling problem at a cross-docking facility. They proposed a delayed start parallel evolutionary algorithm to solve the issue. [PERSON] et al. (2019) [32] extended the applicability of the cross-dock to disperse perishable items. [PERSON] et al. (2019) [33] proposed a bi-objective mathematical model to improve the dependability of a confounded framework. They utilized distinctive repetition systems with part blending in every subsystem. [PERSON] et al. (2019) [34] proposed a bi-objective mathematical model to coordinate truck scheduling and transportation planning in a cross-docking framework in a forward/invert coordination organization. [PERSON] and [PERSON] (2019) [35] proposed a bi-objective optimization model for the issue of scheduling, the succession of trucks, and the task of trucks and forklifts to the doors in a multi-door cross-dock with flexible doors. [PERSON] et al. (2020) [36] considered a vehicle routing problem with cross-docking and parting pickup. They introduced a new bi-objective mixed-integer linear programming model and a multi-objective evolutionary algorithm to find high-quality solutions. [PERSON] and [PERSON] (2020) [37] presented a mixed-integer, non-linear programming model to solve cross-dock scheduling problems. [PERSON] and [PERSON] (2020) [38] considered an inventory-routing problem in a distribution network in which kanban is utilized as a way to execute a just-in-time strategy. [PERSON] et al. (2020) [39] proposed the truck-to-door sequencing problem. They focused on optimizing truck-to-door sequencing with the intention of rehashing the truck holding pattern in inbound trucks in order to minimize makespan. [PERSON] et al. (2020) [40] proposed an approach dependent on the parallel machine environment for the cross-docking issue.