Recently, incorporating carbon emissions into order allocation decisions has attracted considerable attention among scholars and industrialists. Moreover, affected by the random fluctuations of the man, machine, material, method, and environment (4M1E), the production process is usually imperfect with defective products. Reducing product defective rates can effectively improve the quality of the order allocation process. Therefore, considering product defective rate and carbon emission, a multiobjective integer nonlinear programming (INLP) formulation is presented to address this multiproduct, multiperiod, and multi-OEM order allocation problem. Furthermore, exploring the existing literatures, an improved genetic algorithm using priority encoding (IGAUPE) is put forward as a novel optimization technique. Finally, numerical experiments are conducted to validate the correctness of the proposed INLP model as well as the effectiveness of the proposed algorithm. Compared with the genetic algorithm using binary encoding (GAUBE), genetic algorithm using two-layer encoding (GAUTE), and LINGO software, the experiment results show that IGAUPE can improve the efficiency and effectiveness within the predetermined time limit when solving large-scale instances.
We present a novel vocabulary tree data structure for adaptive SIFT matching. Our matching process contains an offline module to cluster features from a group of reference images and an online module to match them to the live images in order to enhance matching robustness. The main contribution lies in constructing two different vocabulary structures cascaded in one tree, which we have called cascading vocabulary tree that can be used to not only cluster features but also implement exact feature matching as k-d tree does. Cascading keyframe selection using our vocabulary structure can be put the matching process forward, which gives us a way to employ a cascading feature matching strategy to combine matching results of cascading vocabulary tree and keyframe. Experimental results show that our method not only dramatically enhances matching robustness but also has enough flexibility to adaptively adjust itself to meet diverse requirements of domain applications for efficiency and robustness of SIFT matching.
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