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knowledge-based optimization algorithm for the inventory routing problem

SOFT COMPUTING 2023年第22期27卷 16959-16981页

作者： Michalak, Krzysztof Lipinski, Piotr Wroclaw Univ Econ & Business Dept Informat Technol Wroclaw Poland Univ Wroclaw Inst Comp Sci Computat Intelligence Res Grp Wroclaw Poland

The Inventory Routing Problem (IRP) is a combinatorial optimization problem that combines routing decisions with inventory management. In this paper, an approach to solving the IRP is studied, which aims at using an external knowledge source (a known good solution or user interaction) to improve the results attained by an evolutionary algorithm solving an IRP instance. The proposed method improves the best solution found by the evolutionary algorithm by modifying schedules for some of the retailers according to those present in the known good solution or to schedules provided by a domain expert. The experiments shown that to improve the optimization results it suffices to perform a few repetitions of the knowledge import procedure. This observation motivates further research on user-interactive optimization algorithms for the IRP, because the number of interactions needed to improve the results can easily be handled by the user.

关键词： Combinatorial optimization knowledge-based optimization Transportation optimization

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knowledge-based automatic optimization of adaptive early-regression-guided VMAT for rectal cancer

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PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS 2020年 70卷 58-64页

作者： Castriconi, Roberta Fiorino, Claudio Passoni, Paolo Broggi, Sara Di Muzio, Nadia G. Cattaneo, Giovanni M. Calandrino, Riccardo Ist Sci San Raffaele Med Phys Milan Italy Ist Sci San Raffaele Radiotherapy Milan Italy

Purpose: To implement a knowledge-based (KB) optimization strategy to our adaptive (ART) early-regression guided boosting technique in neo-adjuvant radio-chemotherapy for rectal cancer. Material and methods: The protocol consists of a first phase delivering 27.6 Gy to tumor/lymph-nodes (2.3 Gy/fr-PTV1), followed by the ART phase concomitantly delivering 18.6 Gy (3.1 Gy/fr) and 13.8 Gy (2.3 Gy/fr) to the residual tumor (PTVART) and to PTV1 respectively. PTVART is obtained by expanding the residual GTV, as visible on MRI at fraction 9. Forty plans were used to generate a KB-model for the first phase using the RapidPlan tool. Instead of building a new model, a robust strategy scaling the KB-model to the ART phase was applied. Both internal and external validation were performed for both phases: all automatic plans (RP) were compared in terms of OARs/PTVs parameters against the original plans (RA). Results: The resulting automatic plans were generally better than or equivalent to clinical plans. Of note, V-30Gy and V-40Gy were significantly improved in RP plans for bladder and bowel;gEUD analysis showed improvement for KB-modality for all OARs, up to 3 Gy for the bowel. Conclusions: The KB-model generated for the first phase was robust and it was also efficiently adapted to the ART phase. The performance of automatically generated plans were slightly better than the corresponding manual plans for both phases.

关键词： Adaptive radiotherapy Rectal cancer Automatic planning knowledge-based optimization

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Operation optimization of propane pre-cooled mixed refrigerant LNG Process: A novel integration of knowledge-based and constrained Bayesian optimization approaches

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CHEMICAL ENGINEERING SCIENCE 2024年 300卷

作者： Shady, Roba Ahmed, Samer F. Sleiti, Ahmad K. Qatar Univ Coll Engn Mech & Ind Engn Dept POB 2713 Doha Qatar

Liquefied natural gas (LNG) technology, particularly the propane precooled mixed refrigerant (C3MR) process, has demonstrated efficiency and emerged as a distinctive dual-refrigerant technology widely used in LNG production. However, the liquefaction process is the highest energy-intensive stage within its supply chain as it consumes about 8 % of the LNG energy content. Thus, for the first time, this study proposes systematic knowledge-based and constrained Bayesian optimization approaches to identify the optimal operation of the C3MR process. These approaches optimize both the operational parameters (pressures and flow rates) and the composition of the mixed refrigerant with practical equipment specifications and rigorous constraints. The results show that the specific energy consumption (SEC) is reduced to 0.264 kWh/kgLNG, which is 14.6%, and 26% lower than the basic C3MR process (unoptimized case) and typical industrial C3MR processes, respectively. In addition, the optimized SEC in this study is 14.5% to 38.6% lower than those reported in the literature. At largescale LNG production (10,000 tons per day), the reduction in the SEC is translated into an 18 MW decrease in compression power, saving approximately 4.7 million $ per year for each C3MR train. Moreover, the coefficient of performance (COP) of the C3MR process was improved by about 15%, and the CO2 emissions were reduced by 17 % (7 tons per year) compared to the basic C3MR process, indicating potential advancements in large-scale LNG liquefaction processes.

关键词： Propane pre-cooled mixed refrigerant LNG Mixed refrigerants knowledge-based optimization Constrained Bayesian optimization Large-scale C3MR

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A user-guided innovization-based evolutionary algorithm framework for practical multi-objective optimization problems

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ENGINEERING optimization 2023年第12期55卷 2084-2096页

作者： Ghosh, Abhiroop Deb, Kalyanmoy Goodman, Erik Averill, Ronald Michigan State Univ E Lansing MI 48824 USA

The knowledge of experienced users in solving real-world optimization problems can be formulated as inter-variable relationships to guide an optimization algorithm towards good solutions faster. Alternatively, such interactions can be learned algorithmically during the optimization by analysing good solutions-a process called innovization. Any common pattern extracted from good solutions can be used as a repair operator to modify candidate solutions. The key aspect is to strike a balance between the relevance of the pattern and the extent of its use in the repair operator. This article proposes a multi-objective evolutionary algorithm framework that combines problem-specific knowledge and online innovization approaches to solve two real-world large-scale multi-objective problems: a 879- and 1479-variable truss design and a 544-variable solid fuel rocket design. Four repair operators suitable for uncovering monotonic relations involving multiple decision variables are proposed. Performance variations resulting from different combinations of initial user knowledge and repair operators are also presented.

关键词： Multi-objective optimization 'innovization' knowledge-based optimization

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Global EM-driven optimization of multi-band antennas using knowledge-based inverse response-feature surrogates

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knowledge-based SYSTEMS 2021年 227卷 107189-107189页

作者： Koziel, Slawomir Pietrenko-Dabrowska, Anna Reykjavik Univ Engn Optimizat & Modeling Ctr IS-102 Reykjavik Iceland Gdansk Univ Technol Fac Elect Telecommun & Informat PL-80233 Gdansk Poland

Electromagnetic simulation tools have been playing an increasing role in the design of contemporary antenna structures. The employment of electromagnetic analysis ensures reliability of evaluating antenna characteristics but also incurs considerable computational expenses whenever massive simulations are involved (e.g., parametric optimization, uncertainty quantification). This high cost is the most serious bottleneck of simulation-driven design procedures, and may be troublesome even for local tuning of geometry parameters, let alone global optimization. On the one hand, globalized search is often necessary because the design problem might be multimodal (i.e., the objective function features multiple local optima) or a reasonably good initial design may not be available. On the other hand, the computational efficiency of popular algorithmic approaches, primarily, nature-inspired population-based algorithms, is generally poor. Combining metaheuristics procedures with surrogate modelling techniques and sequential sampling methods alleviates the problem to a certain extent but modelling of nonlinear antenna responses over broad frequency ranges is extremely challenging, and the aforementioned solutions are normally limited to rather simple structures described by a few parameters. This paper proposes a novel approach to global optimization of multi-band antennas. The major component of the presented framework is the knowledge-based inverse surrogate constructed at the level of response features (e.g., frequency and level locations of the antenna resonances). The surrogate facilitates decision-making process of inexpensive identification of the most promising regions of the parameter space, and a rendition of the good-quality initial design for further local tuning. Our methodology is validated using three examples of dual-and triple-band antennas. The average optimization cost is only 150 full-wave antenna analyses while ensuring precise allocation of the antenna reso

关键词： Antenna design EM-driven design Global optimization knowledge-based optimization Data-driven optimization Response features Surrogate modelling Inverse modelling

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Fast and reliable knowledge-based design closure of antennas by means of iterative prediction-correction scheme

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ENGINEERING COMPUTATIONS 2021年第10期38卷 3710-3731页

作者： Koziel, Slawomir Pietrenko-Dabrowska, Anna Ctr Reykjavik Univ Dept Engn Optimizat & Modeling Reykjavik Iceland Gdansk Univ Technol Fac Elect Telecommun & Informat Gdansk Poland

Purpose A novel framework for expedited antenna optimization with an iterative prediction-correction scheme is proposed. The methodology is comprehensively validated using three real-world antenna structures: narrow-band, dual-band and wideband, optimized under various design scenarios. Design/methodology/approach The keystone of the proposed approach is to reuse designs pre-optimized for various sets of performance specifications and to encode them into metamodels that render good initial designs, as well as an initial estimate of the antenna response sensitivities. Subsequent design refinement is realized using an iterative prediction-correction loop accommodating the discrepancies between the actual and target design specifications. Findings The presented framework is capable of yielding optimized antenna designs at the cost of just a few full-wave electromagnetic simulations. The practical importance of the iterative correction procedure has been corroborated by benchmarking against gradient-only refinement. It has been found that the incorporation of problem-specific knowledge into the optimization framework greatly facilitates parameter adjustment and improves its reliability. Research limitations/implications The proposed approach can be a viable tool for antenna optimization whenever a certain number of previously obtained designs are available or the designer finds the initial effort of their gathering justifiable by intended re-use of the procedure. The future work will incorporate response features technology for improving the accuracy of the initial approximation of antenna response sensitivities. Originality/value The proposed optimization framework has been proved to be a viable tool for cost-efficient and reliable antenna optimization. To the knowledge, this approach to antenna optimization goes beyond the capabilities of available methods, especially in terms of efficient utilization of the existing knowledge, thus enabling reliable parameter tuning

关键词： Kriging interpolation EM simulation Design closure Iterative correction knowledge-based optimization Parameter tuning Antenna design

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Recent advances in accelerated multi-objective design of high-frequency structures using knowledge-based constrained modeling approach

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knowledge-based SYSTEMS 2021年 214卷 106726-106726页

作者： Koziel, Slawomir Pietrenko-Dabrowska, Anna Reykjavik Univ Engn Optimizat & Modeling Ctr IS-101 Reykjavik Iceland Gdansk Univ Technol Fac Elect Telecommun & Informat PL-80233 Gdansk Poland

Design automation, including reliable optimization of engineering systems, is of paramount importance for both academia and industry. This includes the design of high-frequency structures (antennas, microwave circuits, integrated photonic components), where the appropriate adjustment of geometry and material parameters is crucial to meet stringent performance requirements dictated by practical applications. Realistic design has to account for multiple objectives, which are often conflicting. Identification of available trade-offs (e.g., electrical/field properties vs. physical size and cost), otherwise essential from industry standpoint, requires multi-objective optimization. It is a computationally expensive endeavor as in most cases - for the sake of accuracy - the system evaluation has to be carried out using full-wave electromagnetic (EM) analysis. Attempting to solve EM-driven multi-objective (MO) tasks directly using population-based nature-inspired techniques may be prohibitive in terms of cost. Employing surrogate modeling techniques can lead to mitigation of the cost issue;however, construction of fast replacement models over broad ranges of the system parameters is expensive by itself, especially in higher-dimensional spaces. Recently, several approaches involving knowledge-based surrogate modeling approach have been proposed with the metamodels constructed over small regions of the parameter space containing the Pareto front. The latter are approximated using the sets of preoptimized reference designs and permit a dramatic reduction of the number of training points required to set up a reliable surrogate, thus reducing the overall cost of the MO process. This paper reviews the recent advancements in these methodologies, and demonstrates the benefits of domain confinement using the various techniques such as reference design triangulation, nested kriging, and modeling with explicit dimensionality reduction using spectral analysis of the reference set. Demo

关键词： High-frequency design Design automation Data-driven optimization knowledge-based optimization Surrogate modeling Domain confinement

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Surrogate-based optimization for Reduction of Contagion Susceptibility in Financial Systems

Surrogate-based Optimization for Reduction of Contagion Susc...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Michalak, Krzysztof Wroclaw Univ Econ Inst Business Informat Dept Informat Technol Wroclaw Poland

ISBN: (纸本)9781450361118

This paper studies a bi-objective optimization problem in which the goal is to optimize connections between entities in the market in order to make the entire system resilient to shocks of varying magnitudes. As observed in the literature concerning contagion on the inter-bank market, no system structure maximizes resilience to shocks of all sizes. For larger shocks more connections between entities make the crisis worse, and for smaller shocks more connected systems turn out to be more resilient than less connected ones. In order to benefit from the information about the system connectivity, a machine learning model is used to estimate the values of the objectives attained by a given solution. The paper presents a comparison of a multiobjective optimization algorithm using simulations for evaluating solutions with a surrogate-based algorithm using a machine learning model. In the experiments the surrogate-based method outperformed the simulation-based one. This observation along with the analysis of the dependence between system connectivity and the resilience to shocks of different magnitudes presented in the paper allow to conclude that the information on system connectivity can be used for improving the working of optimization methods aimed at making the system less susceptible to financial contagion.

关键词： knowledge-based optimization financial contagion REDS graphs

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Informed Mutation Operator using Machine Learning for optimization in Epidemics Prevention 18

Informed Mutation Operator using Machine Learning for Optimi...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Michalak, Krzysztof Wroclaw Univ Econ Inst Business Informat Dept Informat Technol Wroclaw Poland

ISBN: (纸本)9781450356183

This paper concerns using evolutionary algorithms for optimization in epidemics prevention. Spreading of the epidemic is simulated on a graph and the goal of optimization is to determine which vertices in the graph to vaccinate in order to minimize the number of vertices affected by the epidemic. Decisions whether to vaccinate a vertex or not are represented using a binary genotype. In this paper an informed mutation operator is introduced. The presented operator uses machine learning approach for determining which positions in the genotype to flip. The learning model used in the paper is a neural network trained using graph-based features of the vertices (such as a vertex degree) and the information how often on average a given vertex is infected. Once trained, the learning model helps determine which positions in the current solution to mutate. Results presented in the paper suggest that the proposed informed operator improves the ability of the evolutionary algorithm to produce good solutions to the tackled problem. Interestingly, a very good generalization was achieved. The model built using problem instances with 1000 vertices in the graph improved solutions for problem instances with up to 20000 vertices.

关键词： knowledge-based optimization Graph problems REDS graphs Epidemics Prevention

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What happens in a catalytic fixed-bed reactor for n-butane oxidation to maleic anhydride? Insights from spatial profile measurements and particle resolved CFD simulations

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CHEMICAL ENGINEERING JOURNAL 2018年 350卷 799-811页

作者： Dong, Ying Geske, Michael Korup, Oliver Ellenfeld, Nils Rosowski, Frank Dobner, Cornelia Horn, Raimund Hamburg Univ Technol Inst Chem React Engn Eissendorfer Str 38 D-21073 Hamburg Germany BasCat UniCat BASF JointLab Hardenbergstr 36 D-10623 Berlin Germany BASF SE Proc Res & Chem Engn Carl Bosch Str 38 D-67056 Ludwigshafen Germany

Industrial catalytic reactors are made of steel, operate at high temperatures and pressures and contain hazardous chemicals. What happens inside remains hidden. Reactor optimization requires costly trial and error or is based on simplified mathematical models employing more or less accurate transport correlations and reaction kinetics. In the present work a pilot-scale fixed-bed reactor was developed for measuring concentration and temperature profiles for n-butane oxidation to maleic anhydride on vanadyl pyrophosphate catalyst pellets under industrially-relevant conditions. The reactor was equipped with five heating zones. The reactor was modeled by particle-resolved computational fluid dynamics. The catalyst bed was created by discrete element simulation and the result was validated by comparison with experimental radial porosity profiles. Catalytic chemistry was included by a kinetic model of intrinsic reaction rates. Transport resistances and packing deviations were lumped in reaction rate multipliers determined by fitting the model to profiles measured at a uniform reactor wall temperature. Simulation results reveal strong inhomogeneities inside the bed. A hot-spot develops at uniform wall temperature. At this hot-spot temperature differences of 40 K exist on one and the same pellet with negative impact on maleic anhydride selectivity and catalyst lifetime. An optimized wall temperature profile was derived by combining knowledge from the experimental profiles at uniform wall temperature and the CFD results. A gradual increasing temperature was predicted by the model to eliminate the hot-spot and increase integral maleic anhydride selectivity at constant n-butane conversion. This prediction was confirmed by experiment. At 80% n-butane conversion the maleic anhydride selectivity could be improved by 2%. Facing the scale of the process, this improvement translates into significant n-butane savings, reduced COx emissions and increased revenue.

关键词： n-Butane oxidation Maleic anhydride Profile measurements Computational fluid dynamics Discrete element method knowledge-based optimization

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