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Simultaneous Improvement of Energy Efficiency and Product Quality in PCB Lamination Process

INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING-GREEN TECHNOLOGY 2014年第3期1卷 247-256页

作者： Park, You-Jin Pan, Rong Borror, Connie M. Montgomery, Douglas C. Lee, Gyu-Bong Chung Ang Univ Coll Business & Econ Sch Business Adm Seoul 156756 South Korea Arizona State Univ Sch Comp Informat Decis Syst Engn Ira Fulton Sch Engn Tempe AZ 85281 USA Arizona State Univ W New Coll Interdisciplinary Arts & Sci Sch Math & Nat Sci Phoenix AZ 85069 USA Mfg Serv Technol Ctr Korea Inst Ind Technol Cheonan Si 330825 Chungcheongnam South Korea

Recently, efficient use of energy resources has become a very critical issue in most industries due to various reasons such as the high price of energy resources and environmental problems. The printed circuit board (PCB) industry is known as one of the major manufacturing industries that consumes a moderately large amount of electricity. Of all PCB manufacturing processes, the photolithography process is the most complicated. The photolithography process consists of: 1) a lamination process;2) an exposure process;and, 3) a development process. Particularly, the lamination process appears to consume the most energy among the entire PCB manufacturing processes. This is due to the use of high temperatures and high pressures in that process which are required to employ photo sensitive dry film resist-coating on the panel. In addition, the PCB panel quality after the lamination process is highly dependent on conditions of three main operating parameters, temperature, pressure, and conveyor belt speed. In this research, we employ designed experiments and model-building techniques to obtain optimal settings of the three main operating parameters which will simultaneously minimize energy consumption while maximizing the probability of creating a non-defective PCB panel during the lamination process.

关键词： Lamination process PCB manufacturing Energy consumption Non-defective probability multi-objective optimization problem Genetic algorithm

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multi-objective optimization ON SUPERCRITICAL CO₂ RECOMPRESSION BRAYTON CYCLE USING KRIGING SURROGATE MODEL

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THERMAL SCIENCE 2017年第Sup1期21卷 S309-S316页

作者： Sun, Lei Wang, Chongyu Zhang, Di Xi An Jiao Tong Univ Sch Energy & Power Engn MOE Key Lab Thermofluid Sci & Engn Xian Shaanxi Peoples R China

Supercritical CO2 cycle has become one of the most popular research fields of thermal science. The selection of operation parameters on thermodynamic cycle process is an important task. The computational model of supercritical CO2 recompression cycle is built to solve the multi-objective problem in this paper. Then, the optimization of parameters is performed based on genetic algorithm. Several Kriging models are also used to reduce the quantity of samples. According to the calculation, the influence of sample quantity on the result and the time cost is obtained The results show that it is required to improve the heat transfer when improvement of the cycle efficiency is desired.

关键词： supercritical CO2 Brayton cycle recompression multi-objective optimization problem Kriging surrogate model

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Adaptive multiple selection strategy for multi-objective particle swarm optimization

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INFORMATION SCIENCES 2023年第1期624卷 235-251页

作者： Han, Honggui Zhang, Linlin Yinga, A. Qiao, Junfei Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beijing Univ Technol Beijing Key Lab Computat Intelligence & Intelligen Beijing 100124 Peoples R China Beijing Univ Technol Engn Res Ctr Digital Community Minist Educ Beijing 100124 Peoples R China China Natl Heavy Duty Truck Grp Co LTD Automot Res Inst Jinan 250102 Peoples R China Minist Publ Secur Res Inst 1 Beijing 100048 Peoples R China

multiple-swarm approach is a quite successful evolutionary computation framework for multi-objective particle swarm optimization algorithm (MOPSO) to solve multi-objective optimization problems (MOPs). However, the main challenge of using this framework lies in the lack of leader selection, resulting in the optimal solutions being distributed loosely, as well as far away from the true Pareto-optimal front. To overcome this problem, a multi-swarm MOPSO with an adaptive multiple selection strategy (MOPSO-AMS) is investigated in this paper. This proposed MOPSO-AMS is able to guide each swarm with a suitable lea-der to improve the evolutionary performance. The novelties and advantages of MOPSO-AMS include the following three aspects. First, a hierarchical evolutionary state detection mechanism, based on the distribution and dominance information of non-dominated solu-tions, is designed to obtain the evolutionary state of current iteration. Then, the require-ments of evolutionary process can be detected. Second, an adaptive multiple selection strategy, using the evolutionary state information and spatial features of candidate solu-tions, is developed to select leaders of sub-swarms in multiple evolutionary states. Then, suitable leaders can be selected to keep the balance between convergence and diversity. Third, an adaptive parameter adjustment mechanism, based on the dominance relationship of each particle, is introduced to further improve the evolutionary performance of MOPSO-AMS. Finally, numerical simulations and a practical application are used to validate the analytical results and demonstrate the significant improvement of MOPSO-AMS.(c) 2022 Elsevier Inc. All rights reserved.

关键词： multi-objective particle swarm optimization multi-objective optimization problem Adaptive multiple selection strategy Evolutionary state detection

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Interior ballistic charge design based on a modified particle swarm optimizer

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STRUCTURAL AND multiDISCIPLINARY optimization 2012年第2期46卷 303-310页

作者： Cheng, Cheng Zhang, Xiao-Bing Nanjing Univ Sci & Technol Sch Energy & Power Engn Nanjing 210094 Jiangsu Peoples R China

According to the limitation of the interior ballistic charge design with genetic algorithms and some other direct optimization methods, which has complex evolution operators such as crossover and mutation or has poor perferance in solution accuracy and speed, a modified particle swarm optimizer is proposed which is based on a geometrical way and a fuzzy multi-objective optimization. The modified particle swarm optimizer is used to both single-objective and multi-objective optimization problems of interior ballistic charge design for a guided projectile. The solution results show that the modified particle swarm optimizer has a better convergence rate and accuracy than the original particle swarm optimizer and other ever used optimization methods. Combined with deterred propellant technique, the interior ballistic charge design for a guided projectile is optimized by the modified particle swarm optimizer. The optimization results improve the interior ballistic performance and launch safety and provide theoretical direction for the interior ballistic charge design of guided projectile.

关键词： Particle swarm optimization Interior ballistic Deterred propellant multi-objective optimization problem Guided projectile Charge structure design

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A multi-objective energy scheduling of the reconfigurable off-grid microgrid with electric vehicles using demand response program

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COMPUTERS & ELECTRICAL ENGINEERING 2024年 117卷

作者： Harsh, Pratik Das, Debapriya Indian Inst Technol Elect Engn Dept Kharagpur 721302 West Bengal India

Microgrids are essential in ensuring responsible energy consumption and production patterns to support and advance the United Nations' sustainable development goals. However, due to the limited supply in the off-grid microgrids, an increase in the integration of electric vehicles (EVs) into electrical networks for their charging operation is expected to create a void between the energy supply and demand. Demand response (DR) programs provide a better solution to the energy management problem of microgrids. However, the difference in the objectives of the stakeholders of the off-grid microgrid can create an issue in providing optimal solutions to its energy scheduling problem. This paper focuses on the multi -objective energy scheduling strategy of the off-grid microgrid to manage its limited energy supply optimally using an incentivebased DR program. A fast heuristic approach is also presented in this work to reconfigure off-grid microgrid optimally during its hourly operations. Furthermore, Hong's (2m+1) point estimation method is used in this work to consider the uncertainties in the EVs and other DR participants. The proposed work is analyzed on the 33 -bus and 69 -bus off-grid microgrid consisting of droop-controlled distributed generations, EV charging stations, and consumers with curtailable loads. For the day-ahead operation of the off-grid microgrids, the analysis shows that the proposed work reduces the operational cost of the two off-grid microgrids by 2.3% and 4.56%, respectively.

关键词： Demand response Electric vehicles Off-grid microgrid multi-objective optimization problem Reconfiguration

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Hybrid Heuristic Algorithm for Better Energy optimization and Resource Utilization in Cloud Computing

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WIRELESS PERSONAL COMMUNICATIONS 2021年第1期118卷 43-73页

作者： Al-Mahruqi, Ali Abdullah Hamed Morison, Gordon Stewart, Brian G. Athinarayanan, Vallavaraj Natl Univ Sci & Technol Dept Elect & Comp Engn Muscat Oman Glasgow Caledonian Univ Sch Engn & Built Environm Glasgow G4 0BA Lanark Scotland Univ Strathclyde Dept Elect & Elect Engn Glasgow G1 1XW Lanark Scotland

Energy-efficient execution of the scientific workflow is a challenging task in cloud computing that demands high-performance computing to process growing datasets. Due to the interdependency of tasks in the scientific workflow applications, energy-efficient resource allocation is vital for large-scale applications running on heterogeneous physical machines. Thus, this paper proposes a hybrid heuristic algorithm based energy-efficient cloud computing service (HH-ECO) that offers a significant solution for resource allocation, task scheduling, and optimization of scientific workflows. To ensure the energy-efficient execution, the HH-ECO focuses on executing non-dominant workflow tasks through adaptive mutation and energy-aware migration strategy. HH-ECO adopts the chaotic based particle swarm optimization (C-PSO) principle to optimize the resource allocation, task scheduling, and resource migration by generating the global best plans without local convergence. C-PSO with adaptive mutation avoids the deterioration of global optima while finding the best host to place the virtual machine and ensures an appropriate resource allocation plan. By considering the workflow task precedence relationships during C-PSO based task scheduling, the novel hybrid heuristic method efficiently solves the multi-objective combinatorial optimization problem without dominance among the workflow tasks. The Cloudsim based simulation study delivers superior results compared to the existing methods such as the hybrid heuristic workflow scheduling algorithm (HHWS) and distributed dynamic VM management (DDVM). The proposed approach significantly improves the optimal makespan to 38.27% and energy conservation to 38.06% compared to the existing methods.

关键词： Green cloud computing VM migration Energy-efficient resource optimization multi-objective optimization problem Heuristic algorithm Load category

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An approach for solving the three-objective arc welding robot path planning problem

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ENGINEERING optimization 2023年第4期55卷 650-667页

作者： Zhou, Xin Wang, Xuewu Gu, Xingsheng East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai Peoples R China

multi-objective welding robot path planning is becoming an important problem owing to the developing requirements of industrial production intelligence. An approach with path planning and optimization is proposed for solving the problem of arc welding. A rapidly exploring random tree* (RRT*)-based local path search algorithm is applied to generate a set of collision-free paths between any two welding seams, and the global search algorithm based on the decomposition-based multi-objective evolutionary algorithm (MOEA/D) framework is introduced to improve welding production efficiency by optimizing the requested contradictory objectives, namely, path length, trajectory smoothness and energy consumption. Both proposed algorithms are tested in different instances and on an actual welding workpiece, and the results prove that the proposed method could be useful in industrial production.

关键词： Welding robot path planning multi-objective optimization problem RRT* MOEA D

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Goal Programming Approach for Energy Management of Smart Building

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IEEE ACCESS 2022年 10卷 25341-25348页

作者： Foroozandeh, Zahra Ramos, Sergio Soares, Joao Vale, Zita Polytech Inst Porto Sch Engn ISEP Res Grp Intelligent Engn & Comp Adv Innovat & Dev P-4200072 Porto Portugal

In this paper, a collective residential building is considered in which the following points are taken into consideration: (i) a flexibility value of Contract Power (CP) is considered for each consumer;(ii) it is assumed a single CP for the entire building;(iii) an energy resource manager entity is considered to manage the energy resources in the residential building, such as Electric Vehicles (EVs), Photovoltaic (PV) generation system, and the Battery Energy Storage System (BESS). Taking into consideration the previous assumptions, the major goal of this work is to minimize the electricity consumption costs of the residential building by using a multi-objective Mixed-Binary Linear Programming (MOMBLP) formulation. The objective function of the MOMBLP model minimizes the electricity cost consumption of each apartment. Then, a Goal Programming (GP) strategy is applied to find the most appropriate solutions for the proposed MOMBLP model. Finally, the performance of the suggested model is evaluated by comparing the obtained results from a Single-objective Mixed-Binary Linear Programming (SOMBLP) approach in which the whole building consumption cost is minimized. The results show that using the GP strategy a reduction of 7.5% in the total annual energy consumption is verified in comparison with SOMBLP. Moreover, the GP approach leads to fair benefit among building consumers, by finding a solution with less distance from the desired level.

关键词： Costs Buildings Linear programming Energy management Indexes Energy management systems Batteries Energy management goal-programming methodology multi-objective optimization problem renewable energy smart building

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multi-objective optimization Approaches for Physical Layer Secure Communications Based on Collaborative Beamforming in UAV Networks

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IEEE-ACM TRANSACTIONS ON NETWORKING 2023年第4期31卷 1902-1917页

作者： Li, Jiahui Sun, Geng Kang, Hui Wang, Aimin Liang, Shuang Liu, Yanheng Zhang, Ying Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Singapore Univ Technol & Design Pillar Engn Syst & Design Singapore 487372 Singapore Northeast Normal Univ Sch Informat Sci & Technol Changchun 130024 Peoples R China Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA

Unmanned aerial vehicle (UAV) communications and networks are promising technologies in the forthcoming 5G/6G wireless communications. However, they have challenges for realizing secure communications. In this paper, we consider to construct a virtual antenna array consists UAV elements and use collaborative beamforming (CB) to achieve the UAV secure communications with different base stations (BSs), subject to the known and unknown eavesdroppers on the ground. To achieve a better secure performance, the UAV elements can fly to optimal positions with optimal excitation current weights for performing CB transmissions. However, this leads to extra motion energy consumption. We formulate a physical layer secure communication multi-objective optimization problem (MOP) of UAV networks to simultaneously improve the total secrecy rates, total maximum sidelobe level (SLL) and total motion energy consumption of UAVs by jointly optimizing the positions and excitation current weights of UAVs, and the order of communicating with different BSs. Due to the complexity and NP-hardness of the formulated MOP, we propose an improved multi-objective dragonfly algorithm with chaotic solution initialization and hybrid solution update operators (IMODACH) and a parallel-IMODACH (P-IMODACH) to solve the problem. Simulation results verify that the proposed approaches can effectively solve the formulated MOP and it has better performance than some other benchmark algorithms and approaches. Moreover, some unexpected circumstances are considered and discussed.

关键词： Trajectory Jamming Autonomous aerial vehicles Transmitters Receivers optimization Energy consumption UAV communications collaborative beamforming physical layer security multi-objective optimization problem evolutionary computation

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multi-objective Energy Efficient Resource Allocation in Massive multiple Input multiple Output-Aided Heterogeneous Cloud Radio Access Networks

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IEEE ACCESS 2023年 11卷 33480-33497页

作者： Amani, Nahid Parsaeefard, Saeedeh Yanikomeroglu, Halim ICT Res Inst Dept Commun Technol Tehran *** Iran Univ Toronto Dept Elect & Comp Engn M5S Toronto ON Canada Carleton Univ Dept Syst & Comp Engn Ottawa ON K1S 5B6 Canada

In this work, a novel energy efficient multi-objective resource allocation algorithm for heterogeneous cloud radio access networks (H-CRANs) is proposed where the trade-off between increasing throughput and decreasing operation cost is considered. H-CRANs serve groups of users through femto-cell access points (FAPs) and remote radio heads (RRHs) equipped with massive multiple input multiple output (MIMO) connected to the base-band unit (BBU) pool via front-haul links with limited capacity. We formulate an energy-efficient multi-objective optimization (MOO) problem with a novel utility function. Our proposed utility function simultaneously improves two conflicting goals as total system throughput and operation cost. With this MOO, we jointly assign the sub-carrier, transmit power, access point (AP)(RRH/FAP), RRH, front-haul link, and BBU. To address the conflicting objectives, we convert the MOO problem into a single-object optimization problem using an elastic-constraint scalarization method. With this approach, we flexibly adjust trade-off parameters to choose between two objective functions. To propose an efficient algorithm, we deploy successive convex approximation (SCA) and complementary geometric programming (CGP) approaches. Finally, via simulation results we discuss how to select the values of trade-off parameters, and we study their effects on conflicting objective functions (i.e., throughput and operation cost in MOO problem). Simulation results also show that our proposed approach can offload traffic from C-RANs to FAPs with low transmit power and thereby reduce operation costs by switching off the under-utilized RRHs and BBUs. It can be observed from the simulation results that the proposed approach outperforms the traditional approach in which each user is associated to the AP (RRHs/FAPs) with the largest average value of signal strength. The proposed approach reduces operation costs by 30 % and increases throughput index by 25 % which in turn leads to

关键词： 5G multi-objective optimization problem elastic-constraints method

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