J
Jinliang Ding
Researcher at Northeastern University (China)
Publications - 91
Citations - 1663
Jinliang Ding is an academic researcher from Northeastern University (China). The author has contributed to research in topics: Computer science & Evolutionary algorithm. The author has an hindex of 16, co-authored 63 publications receiving 1007 citations. Previous affiliations of Jinliang Ding include Chinese Ministry of Education & Northeastern University.
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Surrogate-Assisted Cooperative Swarm Optimization of High-Dimensional Expensive Problems
TL;DR: Empirical studies demonstrate that the proposed surrogate-assisted cooperative swarm optimization algorithm is able to find high-quality solutions for high-dimensional problems on a limited computational budget.
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Two-objective stochastic flow-shop scheduling with deteriorating and learning effect in Industry 4.0-based manufacturing system
TL;DR: This study investigates a flow-shop scheduling problem under the consideration of multiple objectives, time-dependent processing time and uncertainty, and a mixed integer programming model is formulated and a fireworks algorithm is developed where some special strategies are designed.
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Toward a Resilient Holistic Supply Chain Network System: Concept, Review and Future Direction
TL;DR: The objectives of this paper are to provide a classification of different SCNs in literature, leading to the identification of a new type of SCN system, i.e., an H-SCN, and to discuss the state of knowledge on the resilience of SCNs, particularly of an H -SCN.
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On domain modelling of the service system with its application to enterprise information systems
TL;DR: A domain modelling framework for the service system is proposed and its application to the enterprise information system is outlined and the FCBPSS is applied to both infrastructure and substance systems, which is novel and effective to modelling of service systems including enterprise information systems.
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Multitasking Multiobjective Evolutionary Operational Indices Optimization of Beneficiation Processes
TL;DR: A multiobjective multitasking framework is developed to address the operational indices optimization, which includes a multitasking multi objective operational indices optimize problem formulation and a multitasks multiobjectives evolutionary optimization to solve the above-formulated optimization problem.