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The energy problem in green manufacturing has attracted enormous attention from researchers and practitioners in the manufacturing domain with the global energy crisis and the aggravation of environmental pollution. The distributed blocking flow shop scheduling problem (DBFSP) has considerable application scenarios in connection with its widespread application in the industry under the background of intelligent manufacturing. A multi-objective discrete differential evolution (MODE) algorithm is proposed to solve the energy-efficient distributed blocking flow shop scheduling problem (EEDBFSP) with the objectives of the makespan and total energy consumption (TEC) in this paper. The cooperative initialisation strategy is proposed to generate the initial population of the EEDBFSP. The mutation, crossover, and selection operators are redesigned to enable the MODE algorithm as applied to discrete space. A local search strategy based on the knowledge of five operators is introduced to enhance the exploitation capability of the MODE algorithm in the EEDBFSP. The non-critical path energy-efficient strategy is proposed to reduce energy consumption according to the specific constraints in the EEDBFSP. The effectiveness of each strategy in the MODE algorithm is verified and compared with the state-of-the-art algorithms. The numerical results demonstrate that the MODE algorithm is the efficient optimiser for solving the EEDBFSP. [ABSTRACT FROM AUTHOR]

A reinforcement learning-driven brain storm optimisation idea (RLBSO) is proposed in this paper to solve multi-objective energy-efficient distributed assembly no-wait flow shop scheduling problem. The objectives of the problem include minimising the maximum assembly completion time ( C max ), minimising the total energy consumption (TEC) and achieving resource allocation balanced. Four operations, which are critical factory insert, critical factory swap, critical factory insert to other factories, critical factory swap with other factories, are designed to optimise the objective of maximum assembly completion time. Q-learning mechanism is utilised to guide the selection of operations to avoid blind search in the iteration process. The learning mechanism based on clustering mechanism in brain storm optimisation algorithm is utilised to assign products to factories in the objective space according to the processing time of products to balance the resources allocation. The speed of operations on non-critical path is slowed down to reduce TEC regarded with the characteristics of no-wait flow shop scheduling problem. The experimental results under 810 large-scale instances by RLBSO show that the RLBSO outperforms the comparison algorithm for addressing the problem. [ABSTRACT FROM AUTHOR]

BackgroundRemote, large-scale, rapid and real-time detection of radioactive sources is of great significance for industrial investigation and environmental remediation. Gama camera can extract information such as the location, intensity and category of radioactive substances from a long distance, and is an effective detection tool. Due to the small detector area and the limited field of view (FOV) of the coded aperture, the detection efficiency of the current coded aperture γ cameras is low.PurposeThis study aims to propose a prototype of large area and highly sensitive coded aperture gamma imaging system based on a SPECT probe.MethodsFirst of all, the Hamamatsu BHP6601 single photon emission computed tomography (SPECT) probe with large area of 510 mm×390 mm and high intrinsic spatial resolution of 3.55 mm was selected as the detector of the system. Based on the detector by tectonic analysis, modified uniform redundant arrays (MURA) coding nesting mode was adopted with a rank of 23 and each unit aperture size of 22.1 mm×17 mm. Then, the system transmission matrix is obtained by using the gamma rays emitted from the radioactive sources at different angles of the FOV to reach the detector geometry through the coded aperture. Then, the geometric relationship between the radioactive source and the system is used to analyze and construct different radioactive source events. Next, the cumulative probability based on the system transmission matrix simulates the photon distribution on the detector. Finally, using Maximum Likelihood Expectation Maximization (MLEM) algorithm is built source image.ResultsThe results indicate that the non artifact field of view (NAFOV) is 57.32° in the X direction and 47.3° in the Y direction; and the spatial angle resolution is 2.94° in the X direction and 2.28° in the Y direction. The γ camera can accurately locate 3.7×107 Bq 137Cs single point source in 1 s at the distance of 18 m and 2 s to accurately reconstruct the position of 1.11×106 Bq 137Cs at the distance of 4 m away from the camera.ConclusionsThe γ camera of this study has a high detection capability for distant or low-activity radiation sources in large range.