This paper proposes an integrated scheduling optimization model based on mixed integer programming to analytically characterize the U-shaped automated container terminal layout and handling technology. We focus on dua...
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This paper proposes an integrated scheduling optimization model based on mixed integer programming to analytically characterize the U-shaped automated container terminal layout and handling technology. We focus on dual trolley quay cranes, conflict-free automated guided vehicles (AGVs) and dual cantilever rail cranes under loading and unloading mode, which have rarely been simultaneously studied in the literature, as most prior research has addressed traditional container terminals. We eliminate the waiting time during the interaction between AGV and dual cantilever rail crane to realize spatiotemporal synchronization and minimize the completion time of all tasks. We employ a reinforcement learning based hyper-heuristic genetic algorithm to solve the model, specifically, better solution results for reward and punishment mechanism incorporating reinforcement learning, higher versatility independent of specific problems, stronger scalability of low-level algorithms. We investigate which algorithm is better by comparing the proposed algorithm with bi-level genetic algorithm, adaptive genetic algorithm, hybrid genetic algorithm and cuckoo search algorithm. We conduct small-sized and large-sized experiments to validate the performance of the proposed model and algorithm. The results show that the proposed model and algorithm can not only avoid the conflicts among AGVs but also significantly improve handling efficiency.
Engineer to order is a highly customized production pattern. A flexible job shop scheduling problem with lot streaming is introduced in the discrete intelligent workshop, which considers setup time, changeover time, a...
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Engineer to order is a highly customized production pattern. A flexible job shop scheduling problem with lot streaming is introduced in the discrete intelligent workshop, which considers setup time, changeover time, and single-piece flowing workpieces under engineer to order. For lot streaming, the advantages of variable sublots are analyzed by comparing different lot splitting strategies. Upon variable sublots, a mixed-integer linear programming model is formulated, but gets restricted in solving large-size problems. Then a hyper-heuristic improved genetic algorithm consisting of high and low levels is proposed. An improved niche genetic algorithm is employed for the high level due to its excellent global search performance, while the low level encapsulates the particle swarm optimization as its perturbation operator. Furthermore, several problem-oriented components are designed to enhance its performance, for example, a high-low level coordination mechanism with clearly global-local division, a decoding method based on greedy and saving rules, a self-adaptive earliest start time, and a population initialization and resetting method. Finally, experiments are organized, and the result shows the proposed algorithm performs up to 24.46% better than the reference group in solving benchmark instances, for which several components are tested to distinguish their main contributions. The general computational performance is also examined through the CEC'17 test suite. For enterprise examples with medium to large sizes, an average 99.52% operation rate demonstrates its high practicality.
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