Browsing by Author "Zhang, Jihui"

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    A hybrid genetic programming algorithm for the distributed assembly scheduling problems with transportation and sequence-dependent setup times
    (Taylor and Francis, 2024-04-23) Deng, Jiawen; Zhang, Jihui; Yang, Shengxiang
    This paper investigates a distributed assembly permutation flow-shop scheduling problem with transportation and sequence-dependent set-up times (DAPFSP-TSDST). A hybrid genetic programming (HGP) algorithm is proposed to optimize the makespan of the assembly stage, which inherits the merits of genetic programming (GP) and neighbourhood search operators. In HGP, a hybrid problem-specific initialization heuristic is developed to make populations more diverse. Multiple neighbourhood search operators are employed as the leaf nodes, which are vital for the success of GP. A product shift strategy is proposed to strengthen its exploitability. In addition, a simulated annealing criterion is adopted to make the HGP explore more thoroughly. Finally, statistical and computational experiments are carried out on the benchmark instances. The results exhaustively identify the notable competitiveness of the HGP algorithm in coping with the DAPFSP-TSDST.
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    Energy-aware integrated scheduling for container terminals with conflict-free AGVs
    (Springer, 2023-04-13) Zhong, Zhaolin; Guo, Yiyun; Zhang, Jihui; Yang, Shengxiang
    For automated container terminals, the effective integrated scheduling of different kinds of equipment such as quay cranes (QCs), automated guided vehicles (AGVs), and yard cranes (YCs) is of great significance in reducing energy consumption and achieving sustainable development. Aiming at the joint scheduling of AGVs and YCs with consideration of conflict-free path planning for AGVs as well as capacity constraints on AGV-mate which is also called buffer bracket in blocks, a mixed integer programming model is established to minimize the energy consumption of AGVs and YCs for the given loading/unloading task. A solution method based on a novel bi-level genetic algorithm (BGA), in which the outer and the inner layer search the optimal dispatching strategy for QCs and YCs, respectively, is designed. The validity of the model and the algorithm is verified by simulation experiments, which take the Port of Qingdao as an example and the performance under different conflicting resolution strategies is compared. The results show that, for the given task, the proposed solution to conflict-free path and the schedule provided by the algorithm can complete the task with minimum energy consumption without loss of AGVs utilization, and the number of AGV-mates should be adjusted according to the task rather than keeping unchanged. Comparison results indicate that our proposed approach could efficiently find solutions within 6% optimality gaps. Energy consumption is dropped by an average of 15%.
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    An improved particle swarm optimization algorithm for dynamic job shop scheduling problems with random job arrivals
    (Elsevier, 2019-10-18) Wang, Zhen; Zhang, Jihui; Yang, Shengxiang
    Random job arrivals that happen frequently in manufacturing practice may create a need for dynamic scheduling. This paper considers an issue of how to reschedule the randomly arrived new jobs to pursue both performance and stability in a job shop. Firstly, a mixed integer programming model is established to minimize three objectives, including the discontinuity rate of new jobs during the processing, the makespan deviation of initial schedule, and the sequence deviation on machines. Secondly, four match-up strategies from references are modified to determine the rescheduling horizon. Once new jobs arrive, the rescheduling process is immediately triggered with ongoing operations remain. The ongoing operations are treated as machine unavailable constraints (MUC) in the rescheduling horizon. Then, a particle swarm optimization (PSO) algorithm with improvements is proposed to solve the dynamic job shop scheduling problem. Improvement strategies consist of a modified decoding scheme considering MUC, a population initialization approach by designing a new transformation mechanism, and a novel particle movement method by introducing position changes and a random inertia weight. Lastly, extensive experiments are conducted on several instances. The experiments results show that the modified rescheduling strategies are statistically and significantly better than the compared strategies. Moreover, comparative studies with five variants of PSO algorithm and three state-of-the-art meta-heuristics demonstrate the high performance of the improved PSO algorithm.
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    Optimizing electric vehicle routing with nonlinear charging and time windows using improved differential evolution algorithm
    (Springer, 2024-01-28) Deng, Jiawen; Zhang, Jihui; Yang, Shengxiang
    In real-life, green logistics is prevalent with the advent of pollution reduction; therefore, electric vehicle routing problem (EVRP) gains more focus. However, nonlinear charging technique has not obtained adequate attention for EVRP with time windows. In this study, an improved differential evolution (IDE) algorithm is introduced to address a variant of EVRP, which involves time windows and partial recharging policy with nonlinear charging. In IDE, a productive approach is developed to instruct the electric vehicle to charge in advance. A modified crossover operator is proposed to make populations more diverse. Five local neighborhood operators and a simulated annealing algorithm are embedded to enhance search quality. Further, we generate 55 instances based on Solomon benchmark and Analysis of Variance is leveraged to manifest the efficacy. Similarly, three algorithms are selected for comparison, i.e., genetic algorithm, artificial bee colony algorithm, and adaptive large neighborhood search. Experimental comparisons reveal that IDE outperforms others.