Joint optimization of steel plate shuffling and truck loading sequencing based on deep reinforcement learning

Xu, Zhezhuang; Wang, Jinlong; Yuan, Meng; Yuan, Yazhou; Chen, Boyu; Zhang, Qingdong; Chen, Cailian; Guan, Xinping

doi:10.1016/j.aei.2024.102392

Cited by 2 publications

(4 citation statements)

References 32 publications

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“…Figure 3 displays an instance of chromosome encoding, where the OSC is [1, 3, 2, 1, 4, 3] and the the YAC is [1,2,4,3,2,2]. In the YAC, all operations of vehicle I 1 to vehicle I 4 are arranged in sequence.…”

Section: Particle Coding For Pickup Vehicle Arrangementmentioning

confidence: 99%

“…Validation of the proposed MILP model. (2) Effectiveness analysis of the improved strategies in the proposed ESPEA. (3) Comparison of the ESPEA with other multi-objective optimization algorithms on the PVSP-MSS.…”

Section: Numerical Experimentsmentioning

confidence: 99%

“…In this paper, we use C max , SC max , CT(s) and the hypervolume (HV) [23] as the performance indicators. C max is the makespan of steel logistics park, which is defined in (2). SC max is the maximum makespan of all vehicles, which is defined in (3).…”

Section: Numerical Experimentsmentioning

confidence: 99%

“…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].…”

Section: Introductionmentioning

confidence: 99%

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Optimization of Pickup Vehicle Scheduling for Steel Logistics Park with Mixed Storage

Wang,

Xu,

et al. 2024

Applied Sciences

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Pickup vehicle scheduling in steel logistics parks is an important problem for determining the outbound efficiency of steel products. In a steel logistics park, each yard contains different types of steel products, which provides 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 greatly increases the scheduling complexity. To overcome this challenge, in this paper, we propose a pickup vehicle scheduling problem with mixed steel storage (PVSP-MSS) to optimize the makespan of pickup vehicles and the makespan of steel logistics parks simultaneously. The optimization problem is formulated as a multi-objective mixed-integer linear programming model, and an enhanced algorithm based on SPEA2 (ESPEA) is proposed to solve the problem with a high efficiency. In the ESPEA, a cooperative initialization strategy is firstly proposed to initialize the vehicle pickup sequence for each yard. Then, an insertion decoding method is designed to improve the scheduling efficiency, utilizing the idle time of a yard. Furthermore, local search technology based on critical paths is proposed for the ESPEA to improve the solution quality. Experiments are executed based on data collected from a real steel logistics park. The results confirm that the ESPEA can significantly reduce both the makespan of each pickup vehicle and the makespan of the steel logistics park.

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