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Day surgery appointment scheduling with patient preferences and stochastic operation duration

TECHNOLOGY AND HEALTH CARE 2021年第4期29卷 697-708页

作者： Wu, Qianyun Xie, Naiming Shao, Yuting Nanjing Univ Aeronaut & Astronaut Coll Econ & Management 29 Jiangjun Ave Nanjing 211106 Jiangsu Peoples R China

BACKGROUND: Due to its fast service and high utilization, day surgery is becoming more and more important in the medical system. As a result, an effective day surgery scheduling can reasonably release the supply and demand pressure. objective: This paper aims to investigate the day surgery scheduling problem with patient preferences and limited operation room for the sake of increasing operation efficiency and further decreasing surgery costs. METHODS: A multiple objective stochastic programming model is constructed to seek a satisfactory surgical scheduling for both patients and hospitals under different scenarios. multi-objective genetic algorithm is designed to solve the model and different scales of scenarios are utilized to test the effectiveness of the algorithm and modeling process. RESULTS: Results show that the proposed model and algorithm can provide a feasible solution for maximizing individual preference of surgeons with surgery date and operation room utilization as well. CONCLUSIONS: Patient preference is proposed to be incorporated into day surgery scheduling, and the variability of surgery duration considered to seek a satisfactory surgery scheduling scheme for both patients and hospitals is more in line with the actual hospital situation.

关键词： Patient preferences stochastic operation duration surgery scheduling stochastic programming multi-objective genetic algorithm

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Optimal Allocation Model of Water Resources in Tidal Flat Development of Coastal Areas

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JOURNAL OF COASTAL RESEARCH 2018年第Spe84期34卷 89-93页

作者： Huang, Xianfeng Chen, Yingqin Fang, Guohua Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing Jiangsu Peoples R China Hohai Univ Wentian Coll Dept Civil Engn Maanshan Peoples R China

As a result of economic and population development in coastal areas, the conflict of water utilization is becoming increasingly serious. To ensure rational utilization, the optimal allocation model of water resources was constructed for the development of coastal tidal flats based on the analysis of the coastal area development model. The goal of optimal allocation was to achieve optimal economic, social, and ecological benefits under the conditions of coordinated use of water and soil resources. Due to the complexity of the water resources system, the model used various land areas, in addition to economic, social, and ecological data, to establish target measures, and a multi-objective genetic algorithm was used to solve the problem. Following each iteration, individuals' fitness was ranked and constantly updated according to population evolution, since individuals learn from the fittest and genes are improved accordingly. The coastal area of Dongtai in Jiangsu province, China, was considered a case study, in which the scheme of the city's water resources was optimized. The optimal allocation results show that by 2020, the agricultural and construction land will have increased, and the unused land will decrease, with the construction land area being 36185 hm2;thus, conforming to the future trend of Dongtai land utilization. The results verify the rationality and feasibility of the model and the algorithm, which provides ideas and experience for the optimal allocation of water resources during beach development in coastal areas.

关键词： Beach development the optimization of water resources multi-objective genetic algorithm coastal areas

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A HYBRIDIZED NEURO-genetic SOLUTION FOR CONTROLLING INDUSTRIAL R³ WORKSPACE

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NEURAL NETWORK WORLD 2010年第7期20卷 811-824页

作者： Irigoyen, E. Larrea, M. Valera, J. Gomez, V. Artaza, F. Univ Basque Country UPV EHU Dept Syst Engn & Automat Control Computat Intelligence Grp ETSI Bilbao 48013 Spain

This work presents a hybridized neuro-genetic control solution for R-3 workspace application. The solution is based on a multi-objective genetic algorithm reference generator and an adaptive predictive neural network strategy. The trajectory calculation between two points in an R-3 workspace is a complex optimization problem considering the fact that there are multiple objectives, restrictions and constraint functions which can play an important role in the problem and be in competition. We solve this problem using genetic algorithms, in a multi objective optimization strategy. Subsequently, we enhance a training algorithm in order to achieve the best adaptation of the neural network parameters in the controller which is responsible for generating the control action for a nonlinear system. As an application of the proposed hybridized control scheme, a crane tracking control is presented.

关键词： Hybrid neuro-genetic solution optimal trajectory generation multi-objective genetic algorithm nonlinear neural control adaptive predictive control

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Characterising the dynamics of EEG waveforms as the path through parameter space of a neural mass model: Application to epilepsy seizure evolution

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NEUROIMAGE 2012年第3期59卷 2374-2392页

作者： Nevado-Holgado, Alejo J. Marten, Frank Richardson, Mark P. Terry, John R. Univ Sheffield Dept Automat Control & Syst Engn Sheffield S10 2TN S Yorkshire England Univ Bristol Fac Engn Bristol BS8 1TR Avon England Kings Coll London Inst Psychiat London SE5 8AF England Univ Sheffield Sheffield Inst Translat Neurosci Sheffield S10 2HQ S Yorkshire England

In this paper we propose that the dynamic evolution of EEG activity during epileptic seizures may be characterised as a path through parameter space of a neural mass model, reflecting gradual changes in underlying physiological mechanisms. Previous theoretical studies have shown how boundaries in parameter space of the model (so-called bifurcations) correspond to transitions in EEG waveforms between apparently normal, spike and wave and subsequently poly-spike and wave activity. In the present manuscript, we develop a multi-objective genetic algorithm that can estimate parameters of an underlying model from clinical data recordings. A standard approach to this problem is to transform both clinical data and model output into the frequency domain and then choose parameters that minimise the difference in their respective power spectra. Instead in the present manuscript, we estimate parameters in the time domain, their choice being determined according to the best fit obtained between the model output and specific features of the observed EEG waveform. This results in an approximate path through the bifurcation plane of the model obtained from clinical data. We present comparisons of such paths through parameter space from separate seizures from an individual subject, as well as between different subjects. Differences in the path reflect subtleties of variation in the dynamics of EEG, which at present appear indistinguishable using standard clinical techniques. (C) 2011 Elsevier Inc. All rights reserved.

关键词： Neural mass model Nonlinear parameter estimation multi-objective genetic algorithm Time-domain estimation Bifurcation analysis Nonlinear dynamics

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Structural multi-objective Probabilistic Design for Six Sigma

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Journal of Shanghai Jiaotong university(Science) 2007年第1期12卷 100-105页

作者：李玉强崔振山陈军张冬娟阮雪榆 National Die and Mold CAD Eng.Research Center Shanghai Jiaotong Univ. Dept. of Die Design Shanghai Sekely Die Technology Co. Ltd. Shanghai 201209 China

Uncertainties in engineering design may lead to low reliable solutions that also exhibit high sensitivity to uncontrollable variations. In addition, there often exist several conflicting objectives and constraints in various design environments. In order to obtain solutions that are not only "multi-objectively" optimal, but also reliable and robust, a probabilistic optimization method was presented by integrating six sigma philosophy and multi-objective genetic algorithm. With this method, multi-objective genetic algorithm was adopted to obtain the global Pareto solutions, and six sigma method was used to improve the reliability and robustness of those optimal solutions. Two engineering design problems were provided as examples to illustrate the proposed method.

关键词： multi-objective genetic algorithm six sigma reliability-based design optimization robust design

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Robustness of Time-multiplexed Hyperthermia to Temperature Dependent Thermal Tissue Properties

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IEEE JOURNAL OF ELECTROMAGNETICS RF AND MICROWAVES IN MEDICINE AND BIOLOGY 2020年第2期4卷 126-132页

作者： Cappiello, Grazia Paulides, Margarethus M. Drizdal, Tomas O'Loughlin, Declan O'Halloran, Martin Glavin, Martin van Rhoon, Gerard Jones, Edward Natl Univ Ireland Coll Engn & Informat Discipline Elect & Elect Engn Galway H91 TK33 Ireland Erasmus MC Canc Inst Dept Radiat Oncol Sect Hyperthermia NL-3015 GD Rotterdam Netherlands Eindhoven Univ Technol Fac Elect Engn NL-5612 AZ Eindhoven Netherlands Czech Tech Univ Dept Biomed Technol Fac Biomed Engn Kladno 27201 Czech Republic Natl Univ Ireland Lambe Inst Translat Res Translat Med Device Lab Galway H91 TK33 Ireland

Microwave hyperthermia is a promising cancer treatment used in combination with radio- and chemotherapy. Typically, hyperthermia systems involve several antennas that transfer electromagnetic energy into the tissue. The principal need in hyperthermia treatment is to optimally focus the heating into the target while minimising heating in the surrounding healthy tissue. Patient-specific treatment planning is done to optimize the specific absorption rate and the resulting temperature distribution. Uncertainties associated with the thermal model used for temperature simulations represent an important challenge. Our previous work has demonstrated that the occurrence of hotspots can be reduced and target heating enhanced using time-multiplexed steering procedures. In this paper, the robustness of time-multiplexed hyperthermia against temperature dependent thermal tissue properties is investigated. Temperature simulations are used to predict the time-dependent heating achieved by multiple antenna phase and amplitude configurations that are generated by a multi-objective genetic algorithm and applied sequentially. The proposed strategy is compared with the heating obtained using one single heating setting obtained by particle swarm optimization as typically used in clinical hyperthermia. Thermal performance of the static and time-multiplexed methods are assessed by applying two thermal models, one that uses constant properties of blood perfusion and thermal conductivity of tumor, muscle and fat, and a second one that uses temperature dependent perfusion values. This study shows that time-multiplexed hyperthermia enhances target heating and limits the hotspot appearance regardless of the thermal model used in thermal simulations.

关键词： Hyperthermia Hyperthermia Treatment Planning multi-objective genetic algorithm Thermal Tissue Properties

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multi-response Optimization of Thermal Cycling Process for Al6092/SiC/ZrW2O8 Composites Using RSM-MOGA

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2022年第6期47卷 7669-7682页

作者： Zhang, Shihao Hou, Qinglin Fu, Zhixiang Jiang, Haiyun Hunan Univ Technol Sch Packaging & Mat Engn Zhuzhou 412007 Peoples R China Hunan Univ Technol Natl & Local Joint Engn Res Ctr Adv Packaging Mat Zhuzhou 412007 Peoples R China

Thermal cycling can reduce or eliminate thermal stresses in aluminum matrix composites, which is essential to improve the dimensional stability of the composites. In this work, the effect of thermal cycling process on the thermophysical properties of 6092 aluminum alloy/silicon carbide/zirconium tungstate composites was investigated by combining response surface optimization and multi-objective genetic algorithm. The purpose is to elucidate the effects of the process of heating temperature, heating time and cycles on the coefficient of thermal expansion (CTE), micro-strain and thermal conductivity of the composites. A series of robust response models were proposed and verified using analysis of variance, externally studentized residuals distribution and the predicted residuals distribution. According to these models, the heating temperature, heating time and cycles interact in a nonlinear manner concerning on thermophysical properties. The experimental results confirmed that thermal cycling can effectively reduce or eliminate thermal residual stresses in the composites and induce the phase transformation process of zirconium tungstate. The heating temperature plays a dominant role in promoting the thermal mismatch stress release between the metal matrix and the reinforcing particles. The Pareto-optimal hypersurface results on the response surface were obtained according to the non-dominated sorting genetic algorithms (NSGA-II), indicating that the optimal thermal cycling processes were 200 degrees C, 2 h and 3 cycles with corresponding CTE, micro-strain and thermal conductivity of 20.55 x 10(-6)/K, 1.16 x 10(-3) and 116.68 W/(m K), respectively.

关键词： Thermal cycling Response surface methodology multi-objective genetic algorithm Thermophysical

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A Novel Slotted Cylinder Spring Geometry with an Improved Energy Storing Capacity

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2022年第12期47卷 15539-15549页

作者： Warzecha, Mariusz Michalczyk, Krzysztof Machniewicz, Tomasz AGH Univ Sci & Technol Al Mickiewicza 30 PL-30059 Krakow Poland

Slotted cylinder springs combine some advantages of helical and disc springs. They do not twist when they are compressed and, at the same time, can carry axial and transverse loads. Their wider use is hampered by the uneven distribution of stresses during operation resulting from their geometry, which makes their load capacity relatively low. The paper presents an attempt to modify the geometry of a conventional slotted spring using the multi-objective genetic algorithm optimization. It exemplifies holistic application of various software tools aiding design process. The analyses confirmed a significant reduction of maximum stress at a given load when compared to the conventional design. The optimization allowed the energy storage capacity to be increased by more than two and a half times compared to the reference spring. Analyses showed that a 39% reduction in maximum equivalent stress was achieved for the same axial load. The experimental tests allowed the results obtained by numerical means to be verified.

关键词： Slotted spring Optimization multi-objective genetic algorithm Elastic element Spring design

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Stochastic multi-objective production-distribution network design using simulation-based optimization

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2009年第2期47卷 479-505页

作者： Ding, Hongwei Benyoucef, Lyes Xie, Xiaolan ISGMP COSTEAM Project INRIA Metz France IBM China Res Lab Beijing Peoples R China Ctr Hlth Sci & Engn St Etienne France

This paper addresses the design of production-distribution networks including both supply chain configuration and related operational decisions such as order splitting, transportation allocation and inventory control. The goal is to achieve the best compromise between cost and customer service level. An optimization methodology that combines a multi-objective genetic algorithm (MOGA) and simulation is proposed to optimize not only the structure of the production-distribution network but also its operation strategies and related control parameters. A flexible simulation framework is developed to enable the automatic simulation of the production-distribution network with all possible configurations and all possible control strategies. To illustrate its effectiveness, the proposed method is applied to a real life case study from automotive industry.

关键词： Production-distribution network design Supply chains multi-objective genetic algorithm Simulation-based optimization

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multi-point and multi-objective optimization design method for industrial axial compressor cascades

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2011年第C6期225卷 1481-1493页

作者： Ju, Y. P. Zhang, C. H. Xi An Jiao Tong Univ Dept Fluid Machinery & Engn Sch Energy & Power Engn Xian 710049 Shaanxi Peoples R China

Modern aerodynamic optimization design methods for the industrial axial compressor cascade mainly aim at improving both design point and off-design point performance. In this study, a multi-point and multi-objective optimization design method is established for the cascade, particularly aiming at widening the operating range while maintaining good performance at the acceptable expense of computational load. The design objectives are to maximize the static pressure ratio and minimize the total pressure loss coefficient at the design point, and to maximize the operating range for the positive and negative incidences. To alleviate the computational load, a design of experiment (DOE)-based GA-BP-ANN model is constructed to rapidly approximate the cascade aerodynamic performance in the optimization process. The artificial neural network (ANN) is trained by the genetic algorithm (GA) technique and back propagation (BP) algorithm, where the training cascades are sampled by the DOE method and analysed by the computational fluid dynamics method. The multi-objective genetic algorithm is used to search for a series of Pareto-optimum solutions, from which an optimal cascade is found out whose objectives are all better than (ABT) those of the original design. The ABT cascade is characterized by the lower camber and higher turning angle, leading to better aerodynamic performance in a widened operating range. Compared with the original design, the ABT cascade decreases the total pressure loss coefficient by 1.54 per cent, 23.4 per cent, and 7.87 per cent at the incidences of 5 degrees, -9 degrees, and 13 degrees, respectively. The established optimization design method can be extended to the three-dimensional aerodynamic design of axial compressor blade.

关键词： axial compressor cascade optimization design multi-point multi-objective genetic algorithm design of experiment artificial neural network Pareto optimum

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