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An automated storage and retrieval system (AS/RS) is a key component of enterprise logistics. Its performance metrics include, e.g., order fulfillment time and energy consumption. A crane-based automated storage and retrieval system (CB-AS/RS) is used as the study subject in this paper to build a location allocation model with the goal of minimizing order fulfillment time and minimizing energy consumption. The two-objective problem is transformed into a single-objective problem by the weight method. A genetic algorithm (GA) is used to optimize and simulate the model using spatial mapping coding. A permutation-combination heuristics (PCH) is proposed that follows the coding method and cross-operation of the GA and conducts both arrange-operation and change-operation. During the simulation, the influence of different storage utilization rates and different output and input instruction quantities in a batch of orders on the results is considered. Experimental results show that the results of the PCH algorithm are better than the GA and the optimization results are more stable. In this paper, we provide an optimization idea for the CB-AS/RS researchers and managers.

Despite the fact that point cloud registration under noisy conditions has recently begun to be tackled by several non-correspondence algorithms, they neither struggle to fuse the global features nor abandon early state estimation during the iterative alignment. To solve the problem, we propose a novel method named R-PCR (recurrent point cloud registration). R-PCR employs a lightweight cross-concatenation module and large receptive network to improve global feature performance. More importantly, it treats the point registration procedure as a high-order Markov decision process and introduces a recurrent neural network for end-to-end optimization. The experiments on indoor and outdoor benchmarks show that R-PCR outperforms state-of-the-art counterparts. The mean average error of rotation and translation of the aligned point cloud pairs are, respectively, reduced by 75% and 66% on the indoor benchmark (ScanObjectNN), and simultaneously by 50% and 37.5% on the outdoor benchmark (AirLoc).