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In this study, the occurrence and sources of five cataloged antibiotics and metabolites were studied in Jiulongjiang River basin, south China. Nineteen antibiotics and 13 metabolites were detected in water samples from 16 river sampling sites, wastewater from 5 swine-raising facilities, and effluent from 5 wastewater treatment plants (WWTPs). The results showed that 12 antibiotics and 6 metabolites were detected in river water samples. Sulfonamides (SAs) and their metabolites were detected at high concentrations (8.59-158.94 ng/L). Tetracyclines (TCs) and their metabolites were frequently detected in swine wastewater, and the maximum concentration was up to the level in milligram per liter. Macrolides (MLs) and β-lactams (β-Ls) were found in all WWTP effluent samples and some river samples, while they were never found in any of the swine wastewater samples. SAs and quinolones (QNs) were detected in all samples. Hierarchical cluster analysis of 16 surface water samples was applied to achieve the spatial distribution characteristics of antibiotics in the Jiulongjiang River. As a result, two categories were obviously obtained. Principal component analysis and redundancy analysis showed that TCs and SAs as well as their metabolites were the major antibiotics in Jiulongjiang River, and they mainly originated from swine wastewater, while the QNs, MLs, and β-Ls in the Jiulongjiang River came from WWTP effluent.

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This study focuses on surrogate measures (SMs) of robustness for the stochastic job shop scheduling problems (SJSSP) with uncertain processing times. The objective is to provide the robust predictive schedule to the decision makers. The mathematical model of SJSSP is formulated by considering the railway execution strategy, which defined that the starting time of each operation cannot be earlier than its predictive starting time. Robustness is defined as the expected relative deviation between the realized makespan and the predictive makespan. In view of the time-consuming characteristic of simulation-based robustness measure (RMsim), this paper puts forward new SMs and investigates their performance through simulations. By utilizing the structure of schedule and the available information of stochastic processing times, two SMs on the basis of minimizing the robustness degradation on the critical path and the non-critical path are suggested. For this purpose, a hybrid estimation of distribution algorithm (HEDA) is adopted to conduct the simulations. To analyze the performance of the presented SMs, two computational experiments are carried out. Specifically, the correlation analysis is firstly conducted by comparing the coefficient of determination between the presented SMs and the corresponding simulation-based robustness values with those of the existing SMs. Secondly, the effectiveness and the performance of the presented SMs are further validated by comparing with the simulation-based robustness measure under different uncertainty levels. The experimental results demonstrate that the presented SMs are not only effective for assessing the robustness of SJSSP no matter the uncertainty levels, but also require a tremendously lower computational burden than the simulation-based robustness measure.

摘要：提出一种混合分布估计算法用于求解具有随机工时的 Job shop 调度问题。建立随机 Job shop 调度问题(Stochastic Job shop scheduling problem, SJSSP)数学模型并给出随机期望值模型的评价方法。为提高种群多样性，将(μ+λ)-进化策略