Abstract:Increasing energy price and requirements to reduce emission are new challenges faced by manufacturing enterprises. A considerable amount of energy is wasted by machines due to their underutilisation. Consequently, energy saving can be achieved by turning off the machines when they lay idle for a comparatively long period. Otherwise, turning the machine off and back on will consume more energy than leave it stay idle. Thus, an effective way to reduce energy consumption at the system level is by employing intelligent scheduling techniques which are capable of integrating fragmented short idle periods on the machines into large ones. Such scheduling will create opportunities for switching off underutilised resources while at the same time maintaining the production performance.This paper introduces a model for the bi-objective optimisation problem that minimises the total non-processing electricity consumption and total weighted tardiness in a job shop. The Turn off/Turn on is applied as one of the electricity saving approaches. A novel multi-objective genetic algorithm based on NSGA-II is developed. Two new steps are introduced for the purpose of expanding the solution pool and then selecting the elite solutions. The research presented in this paper is focused on the classical job shop environment, which is widely used in the manufacturing industry and provides considerable opportunities for energy saving. The algorithm is validated on job shop problem instances to show its effectiveness.
:Many manufacturing companies in China currently are suffering from a Rolling Blackout policy for the industry electricity supply which means that the government electricity is cut off several days in every week resulting in manufacturing companies illegally starting their own diesel generators to maintain production. However, the private generation of electricity is more polluting and costly than the government supplied resource. Thus, the increased price of energy and the requirement to become more environmentally sustainable exert substantial pressures on manufacturing enterprises to reduce energy consumption for cost saving and to become more environmentally friendly. Scheduling of less energy consumption critical operations during rolling blackout periods can help minimise the negative effect of this policy. This is a multi-objective optimisation problem as production due dates cannot be ignored and cost is not directly proportional to electricity consumption anymore. Optimal scheduling even of relatively small production orders is clearly beyond the capability of manual tools or common single objective scheduling optimisation methods. Therefore, a multi-objective scheduling optimisation method has been developed which includes reducing electricity consumption and its related cost as part of the objectives in addition to total weighted tardiness. This research focuses on classical job shop environments which are widely used in the manufacturing industry in China and the rest of the world. A mathematical model for the triobjectives problem that minimises total electricity cost, total electricity consumption and total weighted tardiness has been developed. A specific heuristic has been devised for investigating how the Rolling Blackout policy affects the performance of existing scheduling plans. This heuristic can also be used as a remedial measurement by plant managers if they do not have access to multi-objective optimisation tools. The Non-dominant Sorting Genetic Algorithm has been used as the basis for solving the optimisation problem. Case studies based on four modified job shop instances have been studied to show the effectiveness of the proposed heuristic and the algorithm.
A potential mechanism for the global distribution of waterborne pathogens is through carriage by the migratory waterbirds. However, this mode of transmission has yet been confirmed epidemiologically. Here, we conducted whole genome sequencing of Vibrio spp. collected from waterbirds, sediments, and mollusks in the estuary of the Liaohe River in China to investigate this transmission mode. We found that a V. parahaemolyticus strain isolated from a waterbird was clonally related to the other V. parahaemolyticus strains obtained from the sediments and mollusks, and three V. mimicus strains isolated from bird feces were genomically related to those found in the mollusks and upstream groundwater, suggesting that the bird-carried Vibrio strains were acquired through the direct predation of the local mollusks. Surprisingly, two bird-carried V. parahaemolyticus strains belonging to the same clone were identified in Panjin and Shanghai, which are over 1,150 km apart, and another two were found at two locations 50 km apart, further supporting that waterbirds are capable of carrying and disseminating these pathogens over long distances. Our results provide the first evidence of direct transmission from mollusks to waterbirds and confirm that waterbirds act as disseminating vehicles of waterborne pathogens. Effective surveillance of migratory waterbirds along their routes will be valuable for predicting future epidemics of infectious diseases.
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