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Bi-objective optimization of a three-echelon multi-server supply-chain problem in congested systems: Modeling and solution

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COMPUTERS & INDUSTRIAL ENGINEERING 2016年第0期99卷 41-62页

作者： Maghsoudlou, Hamidreza Kahag, Mahdi Rashidi Niaki, Seyed Taghi Akhavan Pourvaziri, Hani Islamic Azad Univ Fac Ind & Mech Engn Dept Ind Engn Qazvin Branch Qazvin Iran Sharif Univ Technol Dept Ind Engn POB 11155-9414 Azadi Ave Tehran *** Iran

A novel bi-objective three-echelon supply chain problem is formulated in this paper in which cross-dock facilities to transport the products are modeled as an M/M/m queuing system. The proposed model is validated using the epsilon constraint method when applied to solve some small-size problems. Since the problem belongs to the class of NP-hard and that it is of a bi-objective type, a multi-objective particle swarm optimization (MOPSO) algorithm with a new solution structure that satisfies all of the constraints is developed to find Pareto solutions. As there is no benchmark available in literature, three other multi objective meta-heuristic algorithms called non-dominated ranking genetic algorithm (NRGA), non dominated sorting genetic algorithm (NSGA-II), and multi-objective imperialist competitive algorithm (MOICA) are utilized as well to validate the solutions obtained for large-scale problems. The parameters of the solution algorithms are calibrated using the Taguchi method. The comparison results based on five multi-objective performance metrics used in the AHP-TOPSIS method show that the parameter-tuned MOPSO acts better than the other parameter-tuned algorithms to solve, small, medium, and large-size problems. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： multi-echelon supply chains Queuing systems Disruption cost multi-objective particle swarm optimization AHP-TOPSIS method

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multi-objective optimization of hybrid backorder inventory model

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EXPERT SYSTEMS WITH APPLICATIONS 2016年 51卷 76-84页

作者： Srivastav, Achin Agrawal, Sunil Pandit Dwarka Prasad Mishra Indian Inst Informat Jabalpur MP India Pandit Dwarka Prasad Mishra Indian Inst Informat Dept Mech Engn Jabalpur Dumna Airport RdPO Khamaria Jabalpur MP India Flat E-801 Jaipur 302001 Rajasthan India

This paper addresses inventory problem for the products that are sold in monopolistic and captive markets experiencing hybrid backorder (i.e., fixed backorder and time-weighted backorder). The problem with stochastic demand is studied first by developing single objective (cost) inventory model. Computational results of a numerical problem show the effectiveness of hybrid backorder inventory model over fixed backorder inventory model. The model is later extended to multi-objective inventory model. Three objectives of multi-objective inventory model are the minimization of total cost, minimization of stockout units and minimization of the frequency of stockout. A multi-objective particle swarm optimization (MOPSO) algorithm is used to solve the inventory model and generate Pareto curves. The Pareto curves obtained for hybrid backorder inventory model are compared with the existing Pareto curves that are based on fixed backorder. The results show a substantial reduction in stockout units and frequency of stockout with a marginal rise in cost with proposed hybrid backorder inventory system in comparison to existing fixed backorder inventory system. Sensitivity analysis is done to study the robustness of total cost, order quantity, and safety stock factor with the change in holding cost. In the end, the performance of the MOPSO algorithm is compared with the multi-objective genetic algorithm (MOGA). The metrics that are used for the performance measurement of the algorithms are error ratio, spacing and maximum spread. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Backorder Inventory multi-objective particle swarm optimization Pareto multi-objective genetic algorithm

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Design and Implementation of Fuzzy-PI Controllers for PMSM Based on multi-objective optimization Algorithms

Design and Implementation of Fuzzy-PI Controllers for PMSM B...

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2017 5th International Conference on Mechanical, Automotive and Materials Engineering（CMAME 2017）

作者： I-Hsi Kao Kuna-Chung Lu Jau-Woei Perng Department of Mechanical and Electro-Mechanical Engineering National Sun Yat-Sen University

Various intelligent algorithms have been applied to our daily lives,such as fuzzy theory,neural networks,and machine *** methods are widely used for solving many real-world problems;however,these algorithms also exhibit deficiencies and *** paper introduces the recently improved algorithm,known as multi-objective particle swarm optimization,based on decomposition and dominance（D^2 MOPSO） in order to design the permanent magnet synchronous motor（PMSM） fuzzy controller for different *** means that the user can easily change the customized controller,according to their ***,this paper compares the final decision of the controller parameter with other algorithms:the multiobjective particle swarm optimization with crowding distance（MOPSO-CD）,and nondominated sorting genetic algorithm II（NSGA-II）.The simulation results of the three algorithms indicate the optimum PMSM controller parameter in the computing software ***,we implement the fuzzy controller in an embedded system（DSP28069） to demonstrate that our design matches the reality system response and meets the user’s demands with ease.

关键词： fuzzy control multi-objective optimization algorithm multi-objective particle swarm optimization MOPSO Nondominated sorting genetic algorithm NSGA-Ⅱ

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multi-objective optimization OF PARAMETERS AND LOCATION OF PASSIVE VIBRATION ISOLATION SYSTEM EXCITED BY CLAMPED THIN PLATE FOUNDATION

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ENGINEERING REVIEW 2016年第1期36卷 19-27页

作者： Wei, H. Jian, X. Da-Yong, Z. Ying-Lei, W. Jian-Wei, L. Kun-Lin, L. Hefei Univ Technol Sch Civil & Hydraul Engn Hefei Peoples R China China Natl Machinery Ind Corp Beijing Peoples R China Anhui Prov Lab Civil Engn & Mat Hefei Peoples R China Hefei Univ Technol Sch Mech & Automot Engn Hefei Peoples R China

This paper reports a novel optimization strategy combined with artificial intelligence for parameters and location design of precision equipment and provides a broader view for traditional passive vibration isolation. It also considers "precision equipment-vibration isolators-thin plate foundation" as a composite passive vibration isolation system and the clamped thin plate. The vibration isolation system is considered as four-point support, and the displacement amplitude transmissibility from the thin plate to precision equipment is derived and based on the analysis of influencing factors of transmissibility;subsequently, multi-objective optimization of the composite system is performed. A novel swarm intelligence multi-objective optimization method-a multi-objective particle swarm optimization (MOPSO) algorithm is adopted in this paper which can achieve a global optimal solution and by selecting the desired solution from an equivalence relation, the whole Pareto set can be avoided. The maximum and variance of the four transmitted peak displacements are simultaneously considered as fitness functions, and the purpose is to reduce the amplitude of the multi-peak isolation system, and in the meantime, to allow the plate to be uniformly vibrated as far as possible. Moreover, the presented idea is validated numerically, and the location research of the precision equipment mounted on the plate is also conducted.

关键词： Precision equipment Vibration isolators Thin plate Clamped Passive vibration isolation Displacement transmissibility multi-peak system multi-objective particle swarm optimization

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multi-objective Performance optimization of Robotic Mechanism

Multi-objective Performance Optimization of Robotic Mechanis...

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IEEE Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)

作者： Ying, Mingfeng Mo, Xiaohui Jiang, Jin Jinling Inst Technol Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781467391948

Based on the kinematics of an industrial robot, the three indices, including global kinematic average value, global kinematic volatility value, global kinematic worst value are analyzed and optimized by multi-objective optimization algorithm. The link length parameters of robot are design as the variable, and the multi-objective optimization model is established by the requirements of the robot workspace and kinematics. An improved multi-objective particle swarm optimization (pso) algorithm is proposed by using the distribution entropy and its difference of an approximate Pareto front to assess the diversity and evolutionary status of the population, and getting feedback information to design evolution strategy. Finally, a group of optimum link length is calculated by using this method. The optimized link length parameters of robot can improve the performance index of the robot greatly.

关键词： robot kinematics performance index Pareto entropy multi-objective particle swarm optimization

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Antenna Placement of multistatic Radar System with Detection and Localization Performance 19

Antenna Placement of Multistatic Radar System with Detection...

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19th International Conference on Information Fusion (FUSION)

作者： Yang, Yichuan Yi, Wei Zhang, Tianxian Cui, Guolong Kong, Lingjiang Yang, Xiaobo Univ Elect Sci & Technol China Sch Elect Engn Chengdu Sichuan Peoples R China

ISBN: (纸本)9780996452748

We consider an optimization problem of antenna placement of multistatic radar system (MSRS). The goal is to improve the system performance in terms of both detection capability and localization accuracy by optimally placing the antennas of MSRS. To this end, a coverage ratio and an average geometric dilution of precision (GDOP) of surveillance region are proposed as objective functions to evaluate the two different aspects of system performance. However, the formulated problem is computationally intractable for any general application scenario due to its high dimensionality and multiple objectives. Therefore, we device an efficient Pareto-optimal solution to this multi-objective nonlinear non-convex optimization problem based on multi-objective particle swarm optimization (MOPSO). Simulation results verify the validity and efficacy of the proposed method. Our contributions include a novel optimization placement model of MSRS and a novel Pareto-optimal solution to the antenna placement of MSRS.

关键词： sensor networks multistatic radar multi-objective particle swarm optimization optimal deployment

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A multicriteria Decision-Making Application to the Conceptual Design of Mechanical Components

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JOURNAL OF multi-CRITERIA DECISION ANALYSIS 2016年第3-4期23卷 87-111页

作者： Renzi, Cristina Leali, Francesco Univ Modena & Reggio Emilia InterMech MORE Interdept Res Ctr Appl Res & Serv Adv Mech & Motor Sect Via P Vivarelli 10 I-41125 Modena Italy Univ Modena & Reggio Emilia Dept Mech & Civil Engn Modena Italy

Early design is crucial for the success of the final product. In the conceptual design phase, several constraints, criteria, objectives and disciplines have to be considered. To this aim, multidisciplinary optimization has proven effective for the solution of engineering design problems, even in the industrial every-day practice, to improve and simplify the work of designers in a successful quest of the best compromise solution. In this paper, a multicriteria decision-making (MCDM)-based design platform for early optimal design of industrial components is proposed. In a group decision-making context, the selection of the most suitable component among several possible layouts is performed by means of a group Fuzzy Technique for Order of Preference by Similarity to Ideal Solution approach. Hence, a multi-objective optimization is performed on the selected component by applying a multi-objective particle swarm optimization for finding optimal component dimensions. An industrial case study is presented for showing the efficiency of the multicriteria decision-making-based design platform, regarding an innovative and low-cost solution to increase the duration of heel tips in women's shoes. Copyright (c) 2016 John Wiley & Sons, Ltd.

关键词： design optimization multi-objective particle swarm optimization group multicriteria decision-making Fuzzy TOPSIS conceptual design

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Thermo-economic and environmental optimization of solar assisted heat pump by using multi-objective particle swam algorithm

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ENERGY 2014年 72卷 680-690页

作者： Khorasaninejad, Ehsan Hajabdollahi, Hassan Islamic Azad Univ Dept Mech Engn Behbahan Branch Behbahan Iran Vali E Asr Univ Rafsanjan Dept Mech Engn Rafsanjan Iran

In this paper, a solar assisted heat pump is modelled and optimized. Solar panel surface areas, evaporator pressure, condenser pressure, capacity of heat storage tank as well as the value of superheating/subcooling in evaporator/condenser are selected as design parameters. MOPSO (multi-objective particle swarm optimization) algorithm is used to find the optimum value of design parameters where TAC (total annual cost) and COP (coefficient of performance) taken as two objective functions. TAC is included with sum of investment, operation and environmental costs. The optimization is separately performed for five working fluids including R123, R134a, R245fa, R407C and R22. The optimization results showed that the best studied working fluid is R245fa in both thermo-economical and environmental view point with 1746.1 $/year as TAC, 3.76 for COP and annual environmental cost of 81.825. The optimum results of R245fa as working fluid, showed 15.22%, 21.28%, 22.31% and 44.66% improvement in TAC compared with R134a, R123, R22 and R407C, respectively. Furthermore, COP improvement for R245fa was obtained 26.77%, 30.92%, 34.31% and 48.12% compared with R134a, R123, R22 and R407C, respectively. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Solar assisted heat pump Refrigerant Total annual cost multi-objective particle swarm optimization

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Fuzzy control optimized by a multi-objective Differential Evolution algorithm for vibration suppression of smart structures

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COMPUTERS & STRUCTURES 2015年第Jan.期147卷 126-137页

作者： Marinaki, Magdalene Marinakis, Yannis Stavroulakis, Georgios E. Tech Univ Crete Sch Prod Engn & Management Khania Greece

Smart structures include elements of active, passive or hybrid control. The fuzzy control is considered which is a suitable tool for the systematic development of active control strategies. A new multi-objective Differential Evolution algorithm for the calculation of the free parameters in active control systems is proposed and tested. In particular, the usage of MODE with a combination of continuous and discrete variables for the optimal design of the controller is proposed. Numerical applications on smart piezoelastic beams are presented. The results obtained are compared with the ones obtained with the fuzzy controller optimized by a MOPSO algorithm. (C) 2014 Civil-Comp Ltd and Elsevier Ltd. All rights reserved.

关键词： multi-objective Differential Evolution multi-objective particle swarm optimization Active control of structures Fuzzy control Smart structures

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multi-objective efficiency enhancement using workload spreading in an operational data center

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APPLIED ENERGY 2015年第Jan.15期138卷 432-444页

作者： Khalaj, Ali Habibi Scherer, Thomas Siriwardana, Jayantha Halgamuge, Saman K. Univ Melbourne Dept Mech Engn Parkville Vic 3052 Australia IBM Res Zurich Cloud & Comp Infrastruct Dept Ruschlikon Switzerland

The cooling systems of rapidly growing Data Centers (DCs) consume a considerable amount of energy, which is one of the main concems in designing and operating DCs. The main source of thermal inefficiency in a typical air-cooled DC is hot air recirculation from outlets of servers into their inlets, causing hot spots and leading to performance reduction of the cooling system. In this study, a thermally aware workload spreading method is proposed for reducing the hot spots while the total allocated server workload is increased. The core of this methodology lies in developing an appropriate thermal DC model for the optimization process. Given the fact that utilizing a high-fidelity thermal model of a DC is highly time consuming in the optimization process, a three dimensional reduced order model of a real DC is developed in this study. This model, whose boundary conditions are determined based on measurement data of an operational DC, is developed based on the potential flow theory updated with the Rankine vortex to account for buoyancy and air recirculation effects inside the DC. Before evaluating the proposed method, this model is verified with a computational fluid dynamic (CFD) model simulated with the same boundary conditions. The efficient load spreading method is achieved by applying a multi-objective particle swarm optimization (MOPSO) algorithm whose objectives are to minimize the hot spot occurrences and to maximize the total workload allocated to servers. In this case study, by applying the proposed method, the Coefficient of Performance (COP) of the cooling system is increased by 17%, and the total allocated workload is increased by 10%. These results demonstrate the effectiveness of the proposed method for energy efficiency enhancement of DCs. Crown Copyright (C) 2014 Published by Elsevier Ltd. All rights reserved.

关键词： Data center Cooling system Workload spreading Heat flow reduced order model multi-objective particle swarm optimization CFD

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