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Optimal design activated sludge process by means of multi-objective optimization: case study in Benchmark Simulation Model 1 (BSM1)

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WATER SCIENCE AND TECHNOLOGY 2014年第10期69卷 2052-2058页

作者： Chen, Wenliang Yao, Chonghua Lu, Xiwu E China Univ Sci & Technol Sch Resources & Environm Engn Shanghai 200237 Peoples R China Southeast Univ Sch Energy & Environm Nanjing 210096 Jiangsu Peoples R China

Optimal design of activated sludge process (ASP) using multi-objective optimization was studied, and a benchmark process in Benchmark Simulation Model 1 (BSM1) was taken as a target process. The objectives of the study were to achieve four indexes of percentage of effluent violation (PEV), overall cost index (OCI), total volume and total suspended solids, making up four cases for comparative analysis. Models were solved by the non-dominated sorting genetic algorithm in MATLAB. Results show that: ineffective solutions can be rejected by adding constraints, and newly added objectives can affect the relationship between the existing objectives;taking Pareto solutions as process parameters, the performance indexes of PEV and OCI can be improved more than with the default process parameters of BSM1, especially for N removal and resistance against dynamic NH4+-N in influent. The results indicate that multi-objective optimization is a useful method for optimal design ASP.

关键词： activated sludge process BSM1 multi-objective optimization non-dominated sorting genetic algorithm-ii optimal design trade-offs

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Improved NSGA-ii to solve a novel multi-objective task allocation problem with collaborative tasks

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2022年第14期236卷 3106-3123页

作者： Xu, Weiye Pi, Dawei Wang, Hongliang Xie, Boyuan Nanjing Univ Sci & Technol Sch Mech Engn 200 Xiaolingwei St Nanjing 210094 Peoples R China

From the perspective of practical application, a novel task allocation problem for multi-vehicle systems is proposed. The goal is to allocate an optimal route for each vehicle to execute tasks. The planning result is a comprehensive decision considering the influence of time windows, collaborative tasks, and recharging. This problem is represented as a new extension of the classical vehicle routing problem and a multi-objective integer programming mathematical model is established. The objective functions are the total completion time and total penalty costs. A solution strategy hybridizing non-dominated sorting genetic algorithm-ii and variable neighborhood search is proposed, and a feasibility recovery strategy and the concept of the immigrant population are introduced. Finally, the simulation results show that the proposed algorithm can solve the problem effectively and is robust to different complexity scenarios. To illustrate concretely the optimization process, an instance is given in the last.

关键词： Optimization vehicle routing problem non-dominated sorting genetic algorithm-ii variable neighborhood search multi-vehicle system

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A practical multi-objective optimization sectorization method for water distribution network

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SCIENCE OF THE TOTAL ENVIRONMENT 2019年 656卷 1401-1412页

作者： Zhang, Kui Yan, Hexiang Zeng, Han Xin, Kunlun Tao, Tao Tongji Univ Coll Environm Sci & Engn Shanghai Inst Pollut Control & Ecol Secur Siping Rd Shanghai 200092 Peoples R China UN Environm Tongji Inst Environm Sustainable Dev Siping Rd Shanghai 200092 Peoples R China

In recent years, water leakage problems in water distribution networks (WDN) have been attracting more attention, with an emphasis on energy and water resources. As one of the measures used for flow monitoring and leakage control, water network sectorization is a research hotspot in China. This paper, which begins with an introduction of present sectorization methods, proposes a multi-objective optimization sectorization method based on a comprehensive consideration of the hydraulics, water quality and economy. This method is based on the non-dominated sorting genetic algorithm (NSGA-ii) which is a heuristic algorithm formulti-objective optimization to obtain the optimal schemes. In addition, human experience is also considered in the optimization process. In a case study, this method proves to be efficient in producing good results with little impact on the hydraulics andwater quality of theWDN, and the results obtained are acceptable for multiple objectives. Therefore, this method provides references for the transformation of future water distribution network sectorization. (C) 2018 Elsevier B. V. All rights reserved.

关键词： Water distribution network sectorization District metering area Multi-objective optimization method non-dominated sorting genetic algorithm-ii

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Multidisciplinary design optimization of stiffened panels using collaborative optimization and artificial neural network

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2018年第20期232卷 3595-3611页

作者： Chagraoui, Hamda Soula, Mohamed ENSIT Tunis Univ Dept Mech Engn Tunis Tunisia

A new method for solving the multidisciplinary design optimization problems with a minimal computational effort is presented. The proposed methodology is based on the combination of artificial neural network model and Improved Multi-Objective Collaborative Optimization. In the artificial neural network-Improved Multi-Objective Collaborative Optimization scheme, the back-propagation algorithm is used for training the artificial neural network metamodel and the non-dominated sorting genetic algorithm-ii is used to search a Pareto optimality set for the objective functions of stiffened panels. The artificial neural network-Improved Multi-Objective Collaborative Optimization algorithm aims firstly to decompose the global optimization problem hierarchically into optimization design problem at system level and several sub-problems at sub-system level and secondly to replace each optimization problem at the system and subsystem levels by artificial neural network model to limit the computational cost. To highlight the efficiency and effectiveness of the proposed artificial neural network-Improved Multi-Objective Collaborative Optimization method, mathematical and engineering examples are presented. Results obtained from the application of the artificial neural network-Improved Multi-Objective Collaborative Optimization approach to an optimization problem of a stiffened panel are compared with those obtained by traditional optimization without using prediction tools. The new method (artificial neural network-Improved Multi-Objective Collaborative Optimization) was proven to be superior to traditional optimization. These results have confirmed the efficiency and effectiveness of the artificial neural network-Improved Multi-Objective Collaborative Optimization method. In addition, it converges at faster rate than traditional optimization. The traditional optimization method converges within 7918 s, while artificial neural network-Improved Multi-Objective Collaborative Optimiz

关键词： Multidisciplinary design optimization artificial neural network Improved Multi-Objective Collaborative Optimization non-dominated sorting genetic algorithm-ii artificial neural network-Improved Multi-Objective Collaborative Optimization

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A bi-objective optimisation of post-disaster relief distribution and short-term network restoration using hybrid NSGA-ii algorithm

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2022年第19期60卷 5769-5793页

作者： Ransikarbum, Kasin Mason, Scott J. Ubonratchathani Univ Dept Ind Engn Ubon Ratchathani 34190 Thailand Clemson Univ Dept Ind Engn Clemson SC USA

Humanitarian logistics research has recently received tremendous interest from researchers and practitioners due to its importance in assisting relief operations. While there is an increasing trend for mathematical models related to preparedness and response phases for disaster operations management, recovery-phase models are not as emphasised as other phases due to scarce data and model complication from NP-hard nature of the models. One particular approach that can provide a sufficiently good solution for the NP-hard problems is the metaheuristic approach. In this research, we explore the bi-criteria integrated response and recovery model for making strategic post-disaster decisions in the relief distribution and short-term network restoration. Next, with a focus on considering conflicting objectives between fairness and cost of this problem, we propose a hybrid approach with its evolutionary component based on the non-dominated sorting genetic algorithm-ii (NSGA-ii) called HNSGA-ii. The proposed HNSGA-ii is compared against the exact method using the approximate Pareto-front analysis. The proposed algorithm is verified using a case study from a risk assessment tool called Hazus to illustrate how to cope with the aftermath of an earthquake. Finally, results are evaluated using a Hypervolume-based technique and computation time to illustrate the efficiency of the proposed algorithm.

关键词： Humanitarian logistics short-term restoration bi-objective programming metaheuristic non-dominated sorting genetic algorithm-ii hypervolume

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Multiobjective robust PID controller design for various industrial processes

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AI COMMUNICATIONS 2015年第3期28卷 567-578页

作者： Kumar, C. Agees Nair, N. Kesavan Noorul Islam Coll Engn Dept Elect & Instrumentat Engn Kumaracoil Tamil Nadu India CSI Inst Technol Dept Elect & Elect Engn Thovalai Tamil Nadu India

In this paper, non-dominated sorting Particle Swarm Optimization (NSPSO) is applied to design multiobjective robust Proportional-Integral-Derivative (PID) controllers for various industrial processes such as distillation column plant, liquid level control system and DC servo motor control system. Multiobjective robust PID controller design problem is formulated by minimizing Integral Squared Error (ISE) and balanced robust performance criteria. The performance of various optimal PID controllers is compared in terms of ISE and robust criteria. For the purpose of comparison, NSPSO and non-dominated sorting genetic algorithm-ii (NSGA-ii) methods are considered. The performance of PID controllers obtained using NSPSO is better than that of NSGA-ii. The results reveal that NSPSO yields better robustness and consistency in terms of integral squared error (ISE) and balanced robust performance criteria than NSGA-ii for various industrial processes.

关键词： non-dominated sorting Particle Swarm Optimization Proportional-Integral-Derivative Integral Squared Error non-dominated sorting genetic algorithm-ii robust multiobjective

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Multi-Objective Optimization for Multi-Line and U-Shape Layout of Sewing Assembly Line in Apparel Industry

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IEEE ACCESS 2019年 7卷 107188-107200页

作者： Zhang, Xujing Wang, Lichuan Chen, Yan Soochow Univ Coll Text & Clothing Engn Suzhou 215021 Peoples R China

The design of assembly line layout is one of the most important influential factors for the company's performance. The inappropriate arrangement of the order of workstations in the assembly line could cause the excessive movement of the material. In order to increase productivity and reduce production cost, a multi-objective optimization model with minimum the transfer distance of semi-finished products and the total area of assembly line was established in this paper. The non-dominated sorting genetic algorithm-ii was applied to obtain the results of this model. The coding method, genetic operation and fitness function for three different kinds of sewing assembly line layouts (multi-line, U-shape two-line and U-shape three-line) were studied, while the workstations were organized in three ways: the order of processes, the type of machines, and the components of garment. The efficiency of the model was verified by the practice of sewing assembly lines for men's shirt. The layout schemes are compared with other algorithms. The results illustrated that NSGA-ii algorithm is an effective tool to solve the sewing assembly line layout problem. The transfer distances of semi-finished products in the multi-line and U-shape (three-line) layouts arranged by garment components workstation layout were short. The multi-line layout arranged by the type of machines workstation layout occupied the smallest area. The model is suitable for solving the layout problem of the garment sewing workshop in practice, shortening the production cycle, and reducing the production cost. A variety of assembly line layout schemes are provided for apparel manufacturers in this paper. Apparel manufacturers can select the appropriate assembly line layout based on the actual conditions of the workshop and product.

关键词： Sewing assembly line layout workstation layout multi-objective optimization non-dominated sorting genetic algorithm-ii

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Toward sustainable joint optimisation for product family and supply chain configuration with smart contracting consideration

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JOURNAL OF ENGINEERING DESIGN 2023年第12期34卷 1013-1045页

作者： Tian, Yongting Song, Shouxu Zhou, Dan Yang, Ruirui Wei, Chen Hefei Univ Technol Sch Mech Engn Hefei Peoples R China Mie Univ Grad Sch Engn Div Elect & Elect Engn Tsu Japan

This article underscores the necessity for sustainable and environmentally friendly manufacturing practices in product family configuration (PFC) projects, which are paramount to the global economy. Nevertheless, conventional approaches often fixate solely on design aspects, overlooking downstream supply chain configuration (SCC) considerations and the corresponding environmental benefits. Consequently, there is an escalating demand for an integrated optimisation approach that encompasses both PFC and SCC to realise economic and environmental advantages. This study delves into a methodology that integrates blockchain smart contracts as binary 0-1 variables with waste recycling and utilisation, yielding a comprehensive multi-objective model. The proposed methodology seamlessly incorporates considerations for both PFC and SCC. Furthermore, a nested leader-follower optimisation algorithm, based on the non-dominated sorting genetic algorithm-ii (NSGA-ii), has been devised with the objective of achieving triple benefits: augmented profits, maintenance revenue, and diminished environmental emissions. In conclusion, this research contributes to the advancement of sustainable collaborative optimisation through the innovative utilisation of blockchain smart contracts and multi-level modelling. To demonstrate the effectiveness of the proposed methodology, it is applied to a 60 KW DC electric vehicle (EV) charging piles, accompanied by a sensitivity analysis to assess its management implications.

关键词： Product family configuration supply chain configuration leader-follower optimisation non-dominated sorting genetic algorithm-ii DC charging piles

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Simultaneous maximization ofGenCo's revenue and minimization ofISO's purchase cost in adjacent power markets usingMOPSOandNSGA-iioptimizers

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2020年第11期30卷 1-20页

作者： Parmar, Ashokkumar Darji, Pranav SVNIT Dept Elect Engn Surat India

This paper presents a problem of simultaneous maximization of GenCo's (Generation Company) revenue and minimization of ISO's (Independent System Operator) purchase cost in interconnected power markets having different designs, namely capacity-plus-energy market and energy-only market. Both these problems have solved by multi-objective optimizers such as multi-objective particle swarm optimization (MOPSO) and non-dominated sorting genetic algorithm-ii (NSGA-ii) using market data available in the literature. MOPSO and NSGA-ii are meta-heuristic optimization algorithms, which are used for several engineering applications to optimize multiple objectives simultaneously. The paper illustrates a comparative analysis of results with optimum allocation and pricing under different market conditions such as cost of recall, probability of recall and load forecasting (L. F.) error etc. Furthermore, main objective to emphasize on GenCo's total revenue maximization as well as ISO's purchase cost minimization instead of capacity cost only. Multi-objective optimization techniques have used to optimize both objectives simultaneously. Existing literature does not discuss the simultaneous solution of these problems by multi-objective optimization techniques, which makes this work novel. It has been observed that the capacity price, the maximum value of GenCo's revenue and the minimum value of ISO's purchase increases at a different rate, with increasing the probability of recall, cost of recall and L. F. error. GenCo's revenue and ISO's purchase cost have profoundly affected by the L. F. error, moderately affected by the probability of recall and very lowly affected by the cost of recall.

关键词： capacity market generation company independent system operator load serving entity multi-objective particle swarm optimization non-dominated sorting genetic algorithm-ii

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A utility-aware multi-task scheduling method in cloud manufacturing using extended NSGA-ii embedded with game theory

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INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING 2021年第2期34卷 175-194页

作者： Zhang, Wenyu Xiao, Jiuhong Zhang, Shuai Lin, Jian Feng, Ruijun Zhejiang Univ Finance & Econ Sch Informat Management & Artificial Intelligence Hangzhou 310018 Peoples R China

As an emerging sharing and collaborative paradigm, the cloud manufacturing system should maximize the satisfaction of stakeholders to promote the long-term development of the system. This article proposes a new utility-aware cloud manufacturing multi-task scheduling model, which considers the utilities of both customers and manufacturers. To solve the proposed model, an extended non-dominated sorting genetic algorithm-ii with three improvements is presented to find the approximate optimal Pareto solution set. Then, these non-dominated solutions are ranked by means of game theory, and the resulting optimal solution is recommended to the cloud manufacturing system. Simulation experiments are conducted to verify the effectiveness of the proposed algorithm by comparing it with three baseline multi-objective evolutionary algorithms.

关键词： cloud manufacturing multi-task scheduling utility-aware game theory non-dominated sorting genetic algorithm-ii

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