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multi-objective supply planning for two-level assembly systems with stochastic lead times

COMPUTERS & OPERATIONS RESEARCH 2012年第7期39卷 1325-1332页

作者： Sakiani, R. Ghomi, S. M. T. Fatemi Zandieh, M. Amirkabir Univ Technol Dept Ind Engn Tehran Iran Shahid Beheshti Univ Management & Accounting Fac Dept Ind Management GC Tehran Iran

This paper considers two-level assembly systems whose lead times of components are stochastic with known discrete random distributions. In such a system, supply planning requires determination of release dates of components at level 2 in order to minimize expected holding cost and to maximize customer service. Hnaien et al. [Hnaien F, Delorme X, Dolgui A. multi-objective optimization for inventory control in two-level assembly systems under uncertainty of lead times. Computers and Operations Research 2010: 37:1835-43] have recently examined this problem, trying to solve it through multi-objective genetic algorithms. However, some reconsideration in their paper is unavoidable. The main problem with Hnaien et al. proposal is their wrong mathematical model. In addition, the proposed algorithms do not work properly in large-scale instances. In the current paper, this model is corrected and solved via a new approach based on NSGA-II that is called Guided NSGA-II. This approach tries to guide search toward preferable regions in the solution space. According to the statistical analyses, the guided NSGA-II has the higher performance in comparison with the basic NSGA-II used by Hnaien et al. Moreover, the wrongly reported characteristics of the Pareto front shape provided by Hnaien et al. are modified. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Assembly system Supply planning Stochastic lead time Customer service multi-objective genetic algorithm

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Optimum machine capabilities for reconfigurable manufacturing systems

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2018年第9-12期95卷 4397-4417页

作者： Asghar, Eram Zaman, Uzair Khaleeq Uz Baqai, Aamer Ahmed Homri, Lazhar Ghulam Ishaq Khan Inst Engn Sci & Technol Topi Pakistan Ecole Natl Super Arts & Metiers Lab Concept Fabricat Commande Metz France Natl Univ Sci & Technol Islamabad Pakistan

Reconfigurable manufacturing systems constitute a new manufacturing paradigm and are considered as the future of manufacturing because of their changeable and flexible nature. In a reconfigurable manufacturing environment, basic modules can be rearranged, interchanged, or modified, to adjust the production capacity according to production requirements. Reconfigurable machine tools have modular structure comprising of basic and auxiliary modules that aid in modifying the functionality of a manufacturing system. As the product's design and its manufacturing capabilities are closely related, the manufacturing system is desired to be customizable to cater for all the design changes. Moreover, the performance of a manufacturing system lies in a set of planning and scheduling data incorporated with the machining capabilities keeping in view the market demands. This research work is based on the co-evolution of process planning and machine configurations in which optimal machine capabilities are generated through the application of multi-objective genetic algorithms. Furthermore, based on these capabilities, the system is tested for reconfiguration in case of production changeovers. Since, in a reconfigurable environment, the same machine can be used to perform different tasks depending on the required configuration, the subject research work assigns optimum number of machines by minimizing the machining capabilities to carry out different operations in order to streamline production responses. An algorithm has also been developed and verified on a part family. As a result of the proposed methodology, an optimized reconfigurable framework can be achieved to realize optimal production of a part family. Finally, the proposed methodology was applied on a case study and respective conclusions were drawn.

关键词： Alternative process plans multi-objective genetic algorithm Reconfigurable manufacturing systems Reconfigurable process plans

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multi-objective sensor placement optimization for structural response estimation under spatially varying dynamic loading of bridges

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ADVANCES IN STRUCTURAL ENGINEERING 2021年第10期24卷 2255-2266页

作者： Saleem, Muhammad Mazhar Jo, Hongki Univ Arizona Dept Civil & Architectural Engn & Mech 1209 E 2nd St Tucson AZ 85719 USA Univ Engn & Technol Lahore Dept Civil Engn Lahore Pakistan

Although a lot of different types of sensors are available in the market only a limited number of sensors can be installed on a structure. Proper placement of these sensors plays a vital role in effectively achieving the objectives of a monitoring system. Sensor placement becomes especially critical in the case of bridges where the applied loading keeps on changing its location. A sensor layout that provides good quality structural response estimates for a given applied loading may not yield acceptable results for a different loading arrangement. Further, usually different types of sensors are installed on a structure e.g. strain gauges and accelerometers. These sensors measure different physical quantities having different units and orders of magnitude thus cannot be easily incorporated in a sensor placement optimization (SPO) process. So, this research work proposes a multi-objective sensor placement optimization approach that can effectively deal with different types of sensor measurements and spatially varying loading such as in the case of bridges. The proposed method employs an augmented Kalman filter (AKF) for structural response estimation and a multi-objective genetic algorithm for SPO. The AKF can effectively estimate structural response using a few heterogeneous noisy measurements while incorporating the modeling error. The effectiveness of the proposed method is demonstrated using a numerical example of a 3D truss bridge structure. The results show that the proposed multi-objective optimization method yields a sensor arrangement that remains effective against spatially varying dynamic loading.

关键词： multi-objective genetic algorithm sensor placement optimization structural response estimation

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A data-driven multi-objective optimization approach for enhanced methanol yield and exergy loss minimization in direct hydrogenation of CO2

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APPLIED THERMAL ENGINEERING 2024年 251卷

作者： Samad, Abdul Saghir, Husnain Musawwir, Abdul Ahmad, Iftikhar Caliskan, Hakan Natl Univ Sci & Technol Sch Chem & Mat Engn Islamabad 44000 Pakistan Friedrich Alexander Univ Erlangen Nurnberg Dept Chem & Biol Engn Erlangen Germany Usak Univ Fac Engn & Nat Sci Dept Mech Engn TR-64200 Usak Turkiye

The stability and control of process industries have historically faced obstacles due to inherent uncertainties in their operations. This work focuses on leveraging machine learning models as a surrogate to facilitate real-time optimization of operating conditions of the direct hydrogenation of CO 2 to enhance methanol production while minimizing exergy losses. Numerous studies have explored steady-state exergy analysis and the impact of various process conditions on methanol production. However, there is a notable absence of research investigating the influence of exergy destruction on methanol production under dynamic conditions. Initially, a commercial software Aspen HYSYS was utilized to design, model, and simulate the methanol synthesis plant. Exergy analysis was conducted to quantify the process exergy losses, exergy efficiency, and potential areas for improvement. The process model transitioned to a dynamic mode by incorporating +/- 5% uncertainty into critical operating conditions, i.e. , temperature, pressure, and molar flow rate, simulating realworld variability and resulting in a dataset of 370 samples. Two machine learning models;the Gaussian process regression (GPR) and artificial neural network (ANN) model were developed using the data samples to predict the process exergy losses and molar flow rate of methanol produced. To enhance the predictive capabilities of these deployed models, Bayesian optimization was employed for hyperparameter tuning. The developed models were employed as surrogates in multi -objective genetic algorithm (MOGA) environments with the aim of maximizing the production of methanol with minimum exergy losses under uncertainty. The optimized process conditions, derived from MOGA-based methods, underwent cross -validation using the Aspen model. This analysis revealed that the methanol synthesis plant exhibited an exergy loss of 2425 kW, an exergy efficiency of 97.78%, and an improvement potential of 53.74 kW. The GPR and ANN models

关键词： multi-objective genetic algorithm Exergy losses Carbon capture and conversion Energy recovery Machine learning Renewable methanol

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Onboard autonomous mission re-planning for multi-satellite system

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ACTA ASTRONAUTICA 2018年第Apr.期145卷 28-43页

作者： Zheng, Zixuan Guo, Jian Gill, Eberhard Delft Univ Technol Delft Netherlands

This paper presents an onboard autonomous mission re-planning system for multi-Satellites System (MSS) to perform onboard re-planing in disruptive situations. The proposed re-planning system can deal with different potential emergency situations. This paper uses multi-objective Hybrid Dynamic Mutation genetic algorithm (MO-HDM GA) combined with re-planning techniques as the core algorithm. The Cyclically Re-planning Method (CRM) and the Near Real-time Re-planning Method (NRRM) are developed to meet different mission requirements. Simulations results show that both methods can provide feasible re-planning sequences under unforeseen situations. The comparisons illustrate that using the CRM is average 20% faster than the NRRM on computation time. However, by using the NRRM more raw data can be observed and transmitted than using the CRM within the same period. The usability of this onboard re-planning system is not limited to multi-satellite system. Other mission planning and re-planning problems related to autonomous multiple vehicles with similar demands are also applicable.

关键词： multi-satellite system Onboard autonomy Mission re-planning multi-objective genetic algorithm

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A two-stage optimal planning and design method for combined cooling, heat and power microgrid system

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ENERGY CONVERSION AND MANAGEMENT 2013年 74卷 433-445页

作者： Guo, Li Liu, Wenjian Cai, Jiejin Hong, Bowen Wang, Chengshan Tianjin Univ Minist Educ Key Lab Smart Grid Tianjin 300072 Peoples R China Sun Yat Sen Univ Sch Phys & Engn Guangzhou 510275 Guangdong Peoples R China Sun Yat Sen Univ Sino French Inst Nucl Engn & Technol Guangzhou 510275 Guangdong Peoples R China

In this paper, a two-stage optimal planning and design method for combined cooling, heat and power (CCHP) microgrid system was presented. The optimal objective was to simultaneously minimize the total net present cost and carbon dioxide emission in life circle. On the first stage, multi-objective genetic algorithm based on non-dominated sorting genetic algorithm-II (NSGA-II) was applied to solve the optimal design problem including the optimization of equipment type and capacity. On the second stage, mixed-integer linear programming (MILP) algorithm was used to solve the optimal dispatch problem. The approach was applied to a typical CCHP microgrid system in a hospital as a case study, and the effectiveness of the proposed method was verified. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Combined cooling Heat and power microgrid system Two-stage optimal design method multi-objective genetic algorithm Mixed integer linear programming

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Modelling and application of joint maintenance grouping and workload smoothing for an automotive plant

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2024年第8期62卷 2832-2852页

作者： Truong, Minh-Tuan Vu, Hai-Canh Do, Phuc Iung, Benoit Voisin, Alexandre Univ Lorraine CRAN UMR 7039 CNRS Vandoeuvre Les Nancy France Roberval Univ Technol Compiegne Compiegne France

In the maintenance optimisation framework, grouping maintenance is a promising solution for maintenance planning of multi-component systems, in which maintenance activities are performed together to reduce maintenance costs. One of the most widely identified challenges in real applications of grouping maintenance is that it may disturb the maintenance workload balance (smoothness), causing many difficulties in production and/or labour scheduling and inventory management. In this study, we propose a joint optimisation approach for maintenance grouping and workload balancing to address the above challenge. First, a mathematical model of the joint optimisation problem was derived. A multi-objective grouping optimisation approach based on the Weighted Sum model and genetic algorithm was implemented to determine the Pareto-optimal grouping solution. The proposed approach was applied to a real case study of an automotive plant comprising 40 production lines with 1090 components. The results highlighted the advantages, effectiveness, and flexibility of the proposed maintenance approach in real-world applications.

关键词： Maintenance grouping workload smoothing automotive maintenance multi-objective genetic algorithm multi-component system

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Numerical Investigation and Optimization on Shell Side Performance of A Shell and Tube Heat Exchanger with Inclined Trefoil-Hole Baffles

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ENERGIES 2019年第21期12卷 4138-4138页

作者： Sun, Yue Wang, Xinting Long, Rui Yuan, Fang Yang, Kun Huazhong Univ Sci & Technol Sch Energy & Power Engn Wuhan 430074 Peoples R China

In this work, a shell and tube heat exchanger with inclined trefoil-hole baffles (STHX-IT) is proposed, and the numerical simulation is conducted to investigate the flow and heat transfer characteristics. A shell and tube heat exchanger with segmental baffles (STHX-SG) is also studied for the performance comparison. The results show that the heat transfer coefficient and pressure drop of the STHX-IT is averagely lower by 23.89% and 44.19% than those of the STHX-SG, but the heat transfer coefficient per pressure drop is higher by 36.38% on average. Further, the parametric studies of the inclination angle theta, trefoil-hole number n, and baffle cut delta are carried out for the STHX-IT. According to the numerical results, n and delta have more notable influence on shell side performance than theta. In detail, the heat transfer coefficient and pressure drop decrease slightly with theta increasing, and the overall performance is approximately equal;both the heat transfer coefficient and pressure drop decrease with the respective rising of n and delta, but the comprehensive performance shows a growing trend. Considering the synthetic effects of structural parameters, the multi-objective structure optimization using the genetic algorithm combined with the artificial neural networks is fulfilled. As a result, the Pareto front is obtained to characterize the behaviors of the maximum heat transfer rate and minimum pressure drop. The STHX-IT with the theta = 5.38 degrees, n = 6, and delta = 43% is decided as the optimal solution by the TOPSIS method, whose Q/Delta p is 2.34 times as much as that of the original STHX-SG.

关键词： Shell and tube heat exchanger numerical simulation heat transfer enhancement artificial neural network multi-objective genetic algorithm

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Optimization of a boron nitride nanotubes nanofluid-cooled microchannel heat sink at different concentrations

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JOURNAL OF THERMAL ANALYSIS AND CALORIMETRY 2023年第8期148卷 3117-3127页

作者： Yusof, Nur Liyana Nabihah Shamsuddin, Hielfarith Suffri Estelle, Patrice Mohd-Ghazali, Normah Univ Teknol Malaysia Fac Engn Sch Mech Engn Utm Johor Bahru 81310 Johor Malaysia Univ Rennes LGCGM F-35000 Rennes France

The microchannel heat sink (MCHS) has become the most relevant micro-heat exchanger for a small area in need of an effective high heat removal system. However, heat dissipation from the microchips where the MCHS is utilized-the microprocessor and microcontroller-is getting higher with the sizes getting smaller. A more effective coolant is needed to address the increasing heat load from the microchip and nanofluid, nanosized particles dispersed in a base fluid, is among those explored. This paper reports a new potential use of non-metallic nanofluid, boron nitride nanotube (BNN), that is capable of improving the overall performance of a rectangular MCHS. A heuristic method, multi-objective genetic algorithm (MOGA), is employed to simultaneously minimize the thermal resistance and pressured drop of a boron nitride nanotubes (BNN) nanofluid-cooled MCHS to obtain optimized dimensions at various mass concentrations. The method is capable in achieving conflicting objectives, minimization of the thermal resistance and the pressure drop;an increase in the former decreases the latter and vice versa. In addition, experimental thermophysical properties of the BNN nanofluid are used to provide reliability to the optimization outcomes in identifying the best BNN concentration for cooling of a MCHS at 50 degrees C. The optimization results showed that as the thermal resistance decreases, the pressure drop decreases. For mass concentrations of 0.001, 0.003, 0.005, 0.01 and 0.03% the thermal resistance is the lowest, 0.0711 K/W at 0.01 mass.%. at 0.0115 W. The thermal resistance is lowered by 5.34% compared to water for the same operating conditions. These results indicate the great potential of BNN nanofluid as a coolant in the electronics cooling system. Optimization with the MOGA provides a fast analysis into the potentials of any coolants for MCHS applications as shown here, reliability being provided by experimentally obtained thermophysical properties.

关键词： Microchannel heat sink multi-objective genetic algorithm Optimization Boron nitride nanotubes nanofluid

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Smart outrigger damper system for response reduction of tall buildings subjected to wind and seismic excitations

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INTERNATIONAL JOURNAL OF STEEL STRUCTURES 2017年第4期17卷 1263-1272页

作者： Kim, Hyun-Su Kang, Joo-Won Sunmoon Univ Div Architecture Architectural & Civil Engrgineer Asan South Korea Yeungnam Univ Sch Architecture Gyongsan South Korea

The outrigger damper system has recently been proposed to reduce the dynamic response of tall buildings subjected to lateral loads. Previous studies have shown that the outrigger damper system could effectively increase the response reduction capacity of tall buildings. The outrigger damper system was used not only for a response reduction of tall buildings, but also for adjusting the differential column shortening. When an outrigger damper system is designed optimally for wind or earthquake loads, it shows good control performance against each target excitation. On the other hand, the outrigger damper system designed for the wind load cannot effectively control the seismic responses and vice versa. This study examined the control performance of a smart outrigger damper system for reducing both the wind and seismic responses. The smart outrigger damper system was comprised of magnetorheological dampers. A fuzzy logic control algorithm, which was optimized by a multi-objective genetic algorithm, was used to control the smart outrigger damper system. Numerical analysis showed that the smart outrigger damper system could provide superior control performance for the reduction of both wind and earthquake responses compared to the general outrigger system and passive outrigger damper system.

关键词： smart outrigger damper system wind-induced response reduction seismic response control MR damper fuzzy logic controller multi-objective genetic algorithm tall buildings

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