Since location and route problems are of equal importance to the cold chain logistics, it is necessary to consider the location and route optimization comprehensively for the benefit of the overall system. In this paper, an optimal location-routing model considered temperature variation among sensitive products for cold chain distribution centers is designed, with the objective function to minimize the overall expenses. As a solution method, a hybrid genetic algorithm-tabu search is proposed, which is applied to location-routing optimization utilizing an efficient integer index encoding and its appropriate parameters. The algorithm verified by an illustrative example with certain scale data reveals to be feasible and effective, which finally identifies and prioritizes potential alternatives for continuous improvement.

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Objective: To facilitate manual diagnosis of lung cancer-caused metastasis, in this work, we propose a deep learning-based method to automatically identify and locate the hotspots in a bone scan image which denote the lesions metastasized from lung cancer. Approach: An end-to-end metastasis lesion detection model is proposed by following the classical object detection framework SSD (Single Shot multibox object Detector).The proposed model casts lesion detection problem into automatically learning the hierarchal representations of lesion features, locating the spatial position of lesion areas, and boxing the detected lesions. Main results: Experimental evaluation conducted on clinical data of retrospective bone scans shows the comparable performance with a mean score of 0.7911 for AP (Average Precision). A comparative analysis between our network and others including SSD shows the feasibility of the proposed detection network on automatically detecting multiple lesions of metastasis lesions caused by lung cancer. Significance: The proposed method has the potential to be used as an auxiliary tool for improving the accuracy and efficiency of metastasis diagnosis routinely conducted by nuclear medicine physicians.

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