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Objective: This study aimed to improve the utilization value of scallop processing by-products, by analyzing the antioxidant activity of its protein hydrolysates. Methods: The processing by-products of Argopecten irradians were used as raw materials, and the proteins were extracted by alkali extraction and acid precipitation, alkali extraction and ammonium sulfate precipitation and low temperature ethanol denaturation precipitation. Then the proteins were analyzed by SDS-PAGE. Pepsin, flavourzyme, dispase and alcalase were used to hydrolyze the protein, and the antioxidant activities of the hydrolysate products were analyzed. The molecular weight distribution, amino acid composition and hydroxyproline content of dispase hydrolysates were further analyzed. Results: The purity of protein obtained by alkaline ammonium sulfate precipitation method was the highest, reaching 91.16%, with the optima electrophoretic results. All the four hydrolysates had DPPH, ABTS, hydroxyl radical scavenging ability and reducing ability, and the dispase hydrolysate showed the highest antioxidant ability, especially hydroxyl radical scavenging rate, which reached （77.24±1.87）%. The hydrolysate of dispase contained 15 kinds of amino acids such as aspartic acid, with a total content of 84 g/100 g, and the content of hydroxyproline reached （11.82±0.09）%. Conclusion: The protein obtained from the by-products of scallop processing by alkaline extraction and ammonium sulfate precipitation, and its dispase hydrolysates have optimal antioxidant activity and high nutritional value, which can be further developed as antioxidant functional food.

The efficiency of material supply for workstations using Automatic Guided Vehicles (AGVs) is largely determined by the performance of the AGV dispatching scheme. This paper proposes a new solution approach for the AGV dispatching problem (AGVDP) for material replenishment in a general manufacturing workshop where workstations are in a matrix layout, and where uncertainty in replenishment time of workstations and stochastic unloading efficiencies of AGVs are dynamic contextual factors. We first extend the literature proposing a mixed integer optimisation model with a delivery satisfaction soft constraint of material orders and two objectives: transportation costs and delivery time deviation. We then develop a new knowledge-guided estimation of distribution algorithm with delivery satisfaction evaluation for solving the model. Our algorithm fuses three knowledge-guided strategies to enhance optimisation capabilities at its respective execution stages. Comprehensive numerical experiments with instances built from a real-world scenario validate the proposed model and algorithm. Results demonstrate that the new algorithm outperforms three popular multi-objective evolutionary algorithms, a discrete version of a recent multi-objective particle swarm optimisation, and a multi-objective estimation of distribution algorithm. Findings of this work provide major implications for workshop management and algorithm design. [ABSTRACT FROM AUTHOR]

The widespread adoption of Industry 4.0 technologies is revolutionising how manufacturing operations are managed and done. This revolution drives manufacturing practitioners to reevaluate their current manufacturing planning and control (MPC) strategies to maintain global competitiveness. The production and intralogistics (PiL) operations within traditional MPC systems are organised separately, which results in inferior overall solutions. PiL operations in a single factory are inherently coupled and interact with each other throughout the entire process, which needs synchronous organisation and operations. This paper introduces a novel concept of operations twins (OT), with vertical twinning and horizontal twining, for achieving PiL synchronisation by leveraging Industry 4.0 technologies and innovative operations management strategies. An Internet-of-Things (IoT)-based vertical twinning method is developed for real-time object-level data collection and information-sharing between PiL. A horizontal twinning mechanism is proposed to support real-time coordination of production and intralogistics operations with real-time information-sharing. A numerical study is carried out, and the results show that OT outperforms the widely used static and dynamic methods regarding the overall stability and typical measures such as makespan, average manufacturing time, and average tardiness under different levels of uncertainties. [ABSTRACT FROM AUTHOR]

Throughput bottlenecks remain a main concern for managers in practice since they affect production output and throughput times. A large literature on bottleneck detection and prediction consequently emerged. Bottleneck prediction is specifically important in context where bottlenecks shift since it allows for counteracting the potential impact. The literature on throughput bottleneck prediction largely focusses on temporal aspects. Although this reflects the relation among stations if the routing of jobs is fairly directed, the relative station position constantly changes for more complex routings. A station maybe upstream, downstream or have no relation to another station dependent on the mix of jobs currently on the shop floor. In these high-variety contexts, both temporal and spatial features should be considered when predicting bottlenecks. In response, this study proposes a new neural network model that systematically connects multiple independent workstations into a system by extracting the spatial features between workstations. The new approach is different from traditional stacking mechanisms applied in the literature, and it allows for a better integration of spatial and temporal neural networks. Experimental results show that the proposed model outperforms alternative models and provides good prediction performance. Findings have important implications for research and practice. [ABSTRACT FROM AUTHOR]