1,999 research outputs found
Process Knowledge-guided Autonomous Evolutionary Optimization for Constrained Multiobjective Problems
Various real-world problems can be attributed to constrained multi-objective optimization problems. Although there are various solution methods, it is still very challenging to automatically select efficient solving strategies for constrained multi-objective optimization problems. Given this, a process knowledge-guided constrained multi-objective autonomous evolutionary optimization method is proposed. Firstly, the effects of different solving strategies on population states are evaluated in the early evolutionary stage. Then, the mapping model of population states and solving strategies is established. Finally, the model recommends subsequent solving strategies based on the current population state. This method can be embedded into existing evolutionary algorithms, which can improve their performances to different degrees. The proposed method is applied to 41 benchmarks and 30 dispatch optimization problems of the integrated coal mine energy system. Experimental results verify the effectiveness and superiority of the proposed method in solving constrained multi-objective optimization problems.The National Key R&D Program of China, the National Natural Science Foundation of China, Shandong Provincial Natural Science Foundation, Fundamental Research Funds for the Central
Universities and the Open Research Project of The
Hubei Key Laboratory of Intelligent Geo-Information Processing.http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4235hj2023Electrical, Electronic and Computer Engineerin
Application of lean scheduling and production control in non-repetitive manufacturing systems using intelligent agent decision support
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Lean Manufacturing (LM) is widely accepted as a world-class manufacturing paradigm, its currency and superiority are manifested in numerous recent success stories. Most lean tools including Just-in-Time (JIT) were designed for repetitive serial production systems. This resulted in a substantial stream of research which dismissed a priori the suitability of LM for non-repetitive non-serial job-shops. The extension of LM into non-repetitive production systems is opposed on the basis of the sheer complexity of applying JIT pull production control in non-repetitive systems fabricating a high variety of products. However, the application of LM in job-shops is not unexplored. Studies proposing the extension of leanness into non-repetitive production systems have promoted the modification of pull control mechanisms or reconfiguration of job-shops into cellular manufacturing systems. This thesis sought to address the shortcomings of the aforementioned approaches. The contribution of this thesis to knowledge in the field of production and operations management is threefold:
Firstly, a Multi-Agent System (MAS) is designed to directly apply pull production control to a good approximation of a real-life job-shop. The scale and complexity of the developed MAS prove that the application of pull production control in non-repetitive manufacturing systems is challenging, perplex and laborious. Secondly, the thesis examines three pull production control mechanisms namely, Kanban, Base Stock and Constant Work-in-Process (CONWIP) which it enhances so as to prevent system deadlocks, an issue largely unaddressed in the relevant literature. Having successfully tested the transferability of pull production control to non-repetitive manufacturing, the third contribution of this thesis is that it uses experimental and empirical data to examine the impact of pull production control on job-shop performance. The thesis identifies issues resulting from the application of pull control in job-shops which have implications for industry practice and concludes by outlining further research that can be undertaken in this direction
Feedback Control of an Exoskeleton for Paraplegics: Toward Robustly Stable Hands-free Dynamic Walking
This manuscript presents control of a high-DOF fully actuated lower-limb
exoskeleton for paraplegic individuals. The key novelty is the ability for the
user to walk without the use of crutches or other external means of
stabilization. We harness the power of modern optimization techniques and
supervised machine learning to develop a smooth feedback control policy that
provides robust velocity regulation and perturbation rejection. Preliminary
evaluation of the stability and robustness of the proposed approach is
demonstrated through the Gazebo simulation environment. In addition,
preliminary experimental results with (complete) paraplegic individuals are
included for the previous version of the controller.Comment: Submitted to IEEE Control System Magazine. This version addresses
reviewers' concerns about the robustness of the algorithm and the motivation
for using such exoskeleton
Structural optimisation of diffusion-driven degradation processes
In the field of structural engineering, structures are developed and calculated. The stresses
and deformations resulting from mechanical loads are determined, and the structures are
dimensioned to ensure load-bearing capacity, usability and durability in accordance with
standards. The application of structural optimisation algorithms enables the development
of more efficient and economical building structures, whereby maximum permissible
stresses can be exhausted. However, standardised calculations take environmental influences,
such as chemical impact, only via so-called exposure classes and resulting material
properties into account. Detailed calculations on the influence of stresses and deformations
of the structures, especially due to the long-term chemical influence and resulting
material degradation, are often neglected. For example, specific stress constraints may be
exceeded.
Within the scope of the present work, a numerical programme is developed, enabling
an efficient optimisation of mechanical structures that are additionally burdened by
degradation processes due to diffusive concentrations. For this purpose, a mechanicalchemical-
degradation coupled model is developed. Within the framework of classical
structural mechanics, the developed material behaviour is presented, taking into account
modified physical principles of continuum mechanics to describe a mechanical-chemicaldegradation
coupled processes. With the help of the fundamentals of the Finite Element
Method (FEM), the solution of the non-linear problem is outlined in detail. Furthermore,
the developed structural analysis is embedded in a mathematical algorithm of
gradient-based structural optimisation. The optimisation allows a deeper analysis and
reduction of the harmful effects due to the influence of acting chemical concentrations.
A variational approach to structural optimisation provides the simultaneous integration
of analytically prepared sensitivity analysis with the structural analysis for embedding
the continuum mechanical formulations. Thus, efficient structural optimisation of the
introduced mechanical-chemical-degradation model is comprehensively presented. The
mathematical model with the required derivations as well as discretisation is documented
and implemented in a computer-based model
Hybrid Data-driven Framework for Shale Gas Production Performance Analysis via Game Theory, Machine Learning and Optimization Approaches
A comprehensive and precise analysis of shale gas production performance is
crucial for evaluating resource potential, designing field development plan,
and making investment decisions. However, quantitative analysis can be
challenging because production performance is dominated by a complex
interaction among a series of geological and engineering factors. In this
study, we propose a hybrid data-driven procedure for analyzing shale gas
production performance, which consists of a complete workflow for dominant
factor analysis, production forecast, and development optimization. More
specifically, game theory and machine learning models are coupled to determine
the dominating geological and engineering factors. The Shapley value with
definite physical meanings is employed to quantitatively measure the effects of
individual factors. A multi-model-fused stacked model is trained for production
forecast, on the basis of which derivative-free optimization algorithms are
introduced to optimize the development plan. The complete workflow is validated
with actual production data collected from the Fuling shale gas field, Sichuan
Basin, China. The validation results show that the proposed procedure can draw
rigorous conclusions with quantified evidence and thereby provide specific and
reliable suggestions for development plan optimization. Comparing with
traditional and experience-based approaches, the hybrid data-driven procedure
is advanced in terms of both efficiency and accuracy.Comment: 37 pages, 15 figures, 6 table
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Computer Science Research Institute 2005 annual report of activities.
This report summarizes the activities of the Computer Science Research Institute (CSRI) at Sandia National Laboratories during the period January 1, 2005 to December 31, 2005. During this period, the CSRI hosted 182 visitors representing 83 universities, companies and laboratories. Of these, 60 were summer students or faculty. The CSRI partially sponsored 2 workshops and also organized and was the primary host for 3 workshops. These 3 CSRI sponsored workshops had 105 participants, 78 from universities, companies and laboratories, and 27 from Sandia. Finally, the CSRI sponsored 12 long-term collaborative research projects and 3 Sabbaticals
Quantum and Classical Multilevel Algorithms for (Hyper)Graphs
Combinatorial optimization problems on (hyper)graphs are ubiquitous in science and industry. Because many of these problems are NP-hard, development of sophisticated heuristics is of utmost importance for practical problems. In recent years, the emergence of Noisy Intermediate-Scale Quantum (NISQ) computers has opened up the opportunity to dramaticaly speedup combinatorial optimization. However, the adoption of NISQ devices is impeded by their severe limitations, both in terms of the number of qubits, as well as in their quality. NISQ devices are widely expected to have no more than hundreds to thousands of qubits with very limited error-correction, imposing a strict limit on the size and the structure of the problems that can be tackled directly. A natural solution to this issue is hybrid quantum-classical algorithms that combine a NISQ device with a classical machine with the goal of capturing âthe best of both worldsâ.
Being motivated by lack of high quality optimization solvers for hypergraph partitioning, in this thesis, we begin by discussing classical multilevel approaches for this problem. We present a novel relaxation-based vertex similarity measure termed algebraic distance for hypergraphs and the coarsening schemes based on it. Extending the multilevel method to include quantum optimization routines, we present Quantum Local Search (QLS) â a hybrid iterative improvement approach that is inspired by the classical local search approaches. Next, we introduce the Multilevel Quantum Local Search (ML-QLS) that incorporates the quantum-enhanced iterative improvement scheme introduced in QLS within the multilevel framework, as well as several techniques to further understand and improve the effectiveness of Quantum Approximate Optimization Algorithm used throughout our work
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