# Optimization of Pickup Vehicle Scheduling for Steel Logistics Park with Mixed Storage
## 1 Introduction  
Pickup vehicle scheduling is important for steel logistics parks, and can greatly reduce the waiting time of vehicles and improve the logistics throughput [1; 2]. On the other hand, optimizing vehicle scheduling in the industrial or logistics field is also an important way to reduce CO\({}_{2}\) emissions [3; 4]. Traditionally, pickup vehicles are manually scheduled, which is inefficient and always leads to scheduling conflicts since it is too complex to be efficiently scheduled by humans [5; 6]. Therefore, it is essential to develop an automatic pickup vehicle scheduling algorithm based on the data of pickup vehicles to improve the outbound efficiency of steel logistics parks. In recent years, some strategies have been developed to improve the efficiency of vehicle scheduling. [PERSON] et al. (2018) in [7] proposed a timetabled assignment for the multi-warehouse vehicle scheduling problem and designed a formula for the temporal-spatial network flow and solved it using the column generation algorithm. [PERSON] et al. (2021) in [8] proposed a path optimization problem for pickup vehicles in warehouses and designed a mathematical model and a double-layer coding genetic algorithm to solve the problem. [PERSON] et al. (2022) in [9] proposed an integrated problem of pickup vehicle scheduling and route decision making to reduce the delay time of service customers in multiple docks and designed a mixed-integer linear programming model and twoheuristic methods to solve the problem. [PERSON] et al. (2020) in [PERSON] et al. (2020) proposed a dynamic bus vehicle scheduling approach to efficiently generate scheduling solutions, addressing the problem of traffic congestion. [PERSON] (2020) in [PERSON] (2020) proposed a newly formulated model and a hybrid optimization method for solving the integrated vehicle routing and scheduling problem in a multi-door cross-dock terminal. [PERSON] and [PERSON] (2021) in [PERSON] and Kesen (2021) addressed a joint production scheduling and outbound distribution planning problem, considering a permutation flow shop setting and multiple heterogeneous capacitated vehicles. The objective is to minimize the total tour time of the vehicles while considering the tardiness resulting from late deliveries. However, the aforementioned studies did not consider the vehicle scheduling problem in the steel logistics field. To solve the pickup vehicle scheduling problem in steel logistics parks, [PERSON] et al. (2022) in [PERSON] et al. (2022) proposed a constrained clustering of vehicle batching algorithm, which optimized the sequence of vehicles entering a steel logistics park and the selection of the operation yard to minimize the makespan. [PERSON] et al. (2009) in [PERSON] et al. (2009) considered a pickup vehicle and trailer vehicle scheduling problem and developed an inherited tabu search algorithm to solve the problem. [PERSON] and [PERSON] (2021) in [PERSON] and [PERSON] (2021) addressed the capacitated vehicle routing problem in the steel industry by utilizing [PERSON] and [PERSON]'s saving algorithm. These works provide inspiration for solving the pickup vehicle scheduling problem for steel logistics parks. However, they are not applicable to pickup vehicle scheduling in a steel logistics park with mixed storage. In a steel logistics park, each yard contains different types of steel products ([PERSON] and [PERSON], 2021), which provides the flexible yard selection for each pickup operation. In this case, the yard allocation and the loading sequence for each vehicle must be considered simultaneously in pickup vehicle scheduling, which significantly increases the complexity of the problem. To cope with these problems, in this paper, we propose to optimize the pickup vehicle scheduling problem with mixed steel storage (PVSP-MSS) based on the data of pickup vehicles collected by the IIoT. Firstly, a multi-objective mixed-integer linear programming model is formulated to describe the PVSP-MSS.