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Asia-Pacific Power and Energy Engineering Conference (APPEEC)

作者： Etemad, Reza Navabi, Reza Shayanfar, Heidar Ali Iran Univ Sci & Technol Dept Elect Engn Tehran Iran Iran Univ Sci & Technol Ctr Excellence Power Syst Automat & Operat Dept Elect Engn Tehran Iran

ISBN: (纸本)9781424448135

In this paper a novel approach to determine optimal location, number and initial compensation level of the SVC in a power system is presented. mixed integer nonlinear programming (MINLP) is used for this purpose as a useful technique for combinatorial optimization over integers and variables. This technique can provide a fast approach as well as high computational efficiency even in solving complex optimization problems. Previous works used DC power flow that has well known limitations such as reactive power balance and reactive power loss ignorance. This paper uses novel Line Flow Based (LFB) equation that employs line power flow (instead of line current) and square of the bus voltages as variables. Since control variables such as line active and reactive power flow figure directly in formulation, handling the control action of the Flexible Alternating Current Transmission Systems (FACTS) devices is directly and easily implemented. The objectives of this paper are increasing system loadability and decreasing power losses and VAr generation cost by finding the optimal location, number and initial setting of the SVCs.

关键词： FACTS allocation SVC mixed integer nonlinear programming Line Flow Based Equations

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The Multiplicative Weights Update Algorithm for mixed integer nonlinear programming: Theory, Applications, and Limitations

The Multiplicative Weights Update Algorithm for Mixed Intege...

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作者： Luca Mencarelli Ecole Polytechnique

学位级别：博士

This thesis presents a new algorithm for mixed integer nonlinear programming, inspi- red by the Multiplicative Weights Update frame- work and relying on a new class of reformulations, called the pointwise reformulations. mixed integer nonlinear programming is a hard and fascinating topic in Mathematical Optimiza- tion both from a theoretical and a computational viewpoint. Many real-world problems can be cast this general scheme and, usually, are quite challen- ging in terms of efficiency and solution accuracy with respect to the solving procedures. The thesis is divided in three main parts: a fore- word consisting in Chapter 1, a theoretical founda- tion of the new algorithm in Chapter 2, and the ap- plication of this new methodology to two real-world optimization problems, namely the Mean-Variance Portfolio Selection in Chapter 3, and the Multiple nonlinear Separable Knapsack Problem in Chap- ter 4. Conclusions and open questions are drawn in Chapter 5.

关键词： mixed integer nonlinear programming Multiplicative Weights Update Algorithm Mean-Variance Portfolio Multiple nonlinear Knapsack Problem

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A mixed Coding Scheme of a Particle Swarm Optimization and a Hybrid Genetic Algorithm with Sequential Quadratic programming for mixed integer nonlinear programming in Common Chemical Engineering Practice

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CHEMICAL ENGINEERING COMMUNICATIONS 2017年第8期204卷 840-851页

作者： Chanthasuwannasin, Manatsanan Kottititum, Bundit Srinophakun, Thongchai Kasetsart Univ Fac Engn Dept Chem Engn Bangkok Thailand Kasetsart Univ Natl Ctr Excellence Petr Petrochem & Adv Mat Fac Engn Dept Chem Engn Bangkok Thailand Kasetsart Univ Ctr Adv Studies Ind Technol Bangkok Thailand

In this paper, mixed integer nonlinear programming (MINLP) is optimized by PSO_GA-SQP, the mixed coding of a particle swarm optimization (PSO), and a hybrid genetic algorithm and sequential quadratic programming (GA-SQP). The population is separated into two groups: discrete and continuous variables. The discrete variables are optimized by the adapted PSO, while the continuous variables are optimized by the GA-SQP using the discrete variable information from the adapted PSO. Therefore, the population can be set to a smaller size than usual to obtain a global solution. The proposed PSO_GA-SQP algorithm is verified using various MINLP problems including the designing of retrofit heat exchanger networks. The fitness values of the tested problems are able to reach the global optimum.

关键词： Genetic algorithm Heat exchanger network mixed integer nonlinear programming mixed-coding Optimization Particle swarm Sequential quadratic programming

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Optimising intra-seasonal irrigation water allocation: Comparison between mixed integer nonlinear programming and differential evolution

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WATER SA 2019年第1期45卷 48-54页

作者： Grove, B. Du Plessis, M. C. Univ Free State Dept Agr Econ POB 339 ZA-9300 Bloemfontein South Africa Nelson Mandela Univ Dept Comp Sci Port Elizabeth South Africa

Optimising crop planning in conjunction with intra-seasonal water allocation necessitates the use of daily water budget calculations to determine the timing and amount of irrigation events, which complicates the solution of the problem to global optimality. The main objective of this research was to compare the intra-seasonal water allocation of a mixed integer nonlinear programming (MINLP) model with that of differential evolution (DE), to allocate a limited amount of water while considering irrigable area and the irrigation schedule that will maximise the total gross margin. Results show that both solution procedures adhere to economic theory of water allocation under limited water supply. The conclusion is that the MINLP model most likely achieved very near global optimality as the solutions of the two models were very close to each other. DE holds promise to solve more complex models involving risk and multiple crops.

关键词： intra-seasonal water allocation mixed integer nonlinear programming differential evolution

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Optimal Configuration of Gas Solid Separation Equipment Using mixed integer nonlinear programming

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ATMOSPHERE 2022年第11期13卷

作者： Failaka, Muhamad Fariz Madhuranthakam, Chandra Mouli R. Yetilmezsoy, Kaan Almansoori, Ali Elkamel, Ali Univ Waterloo Dept Chem Engn Waterloo ON N2L 3G1 Canada Abu Dhabi Univ Dept Chem Engn POB 59911 Abu Dhabi U Arab Emirates Yildiz Tech Univ Fac Civil Engn Dept Environm Engn TR-34220 Istanbul Turkey Khalifa Univ Dept Chem Engn POB 59911 Abu Dhabi U Arab Emirates

The separation of solid particles from gas-solid process streams is an important unit operation in many chemical processes. Out of the many different types of separation equipment that are used for gas-solid separation, cyclone separators are widely used for their operational flexibility, efficiency and capital cost. This study focused on the design of an optimal configuration for several cyclones used in a fertilizer plant. The granulation step in the fertilizer plant leads to using different size cyclones and a different number of cyclones in series or parallel or a mix of both arrangements. A mixed integer nonlinear programming (MINLP) model is formulated to find the best cyclone arrangement with the optimal number of cyclones and dimensions from several combinations of 1D3D and 2D2D cyclones arranged in parallel-series for a high volume and heavy loading of solid particles. The objective function was to minimize the total cost, including the operating cost and the capital cost. The results indicated that a maximum of 90% efficiency is achieved with a parallel-series arrangement of 1D3D and 2D2D cyclones to be an optimal configuration for the maximum reduction in pollution level.

关键词： cyclone separators general algebraic modeling system mixed integer nonlinear programming modeling optimization

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Bilevel mixed-integer nonlinear programming for integrated scheduling in a supply chain network

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第6期22卷 15517-15532页

作者： Yang, Jianchao Guo, Feng Luo, Li Ye, Xiaoming Sichuan Univ Business Sch Chengdu Sichuan Peoples R China Sichuan Univ Coll Comp Sci Chengdu Sichuan Peoples R China Chengdu Univ Informat Technol Sch Cybersecur Chengdu Sichuan Peoples R China

An integrated scheduling problem under a make-to-order supply chain network is addressed. This problem considers integrated production and transportation scheduling with realistic supply chain features such as unrelated parallel shop and product batch-based transportation. The mathematical model for this problem is presented, which is formulated as a bilevel mixed-integer nonlinear program. A novel bilevel evolutionary optimization model based on memetic algorithm is proposed to resolve this problem because the problem is hard-to-tackle for mathematical programming techniques and traditional intelligent techniques. The effectiveness of the proposed optimization model is validated through a series of numerical experiments. The experimental results also confirmed that the proposed optimization model is superior to other three intelligent optimization models.

关键词： Make-to-order supply chain scheduling mixed integer nonlinear programming Bilevel programming Memetic algorithm Bilevel evolutionary algorithms

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Solving mixed-integer nonlinear programming problems using improved genetic algorithms

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KOREAN JOURNAL OF CHEMICAL ENGINEERING 2011年第1期28卷 32-40页

作者： Wasanapradit, Tawan Mukdasanit, Nalinee Chaiyaratana, Nachol Srinophakun, Thongchai Kasetsart Univ Dept Chem Engn Fac Engn Bangkok 10900 Thailand King Mongkuts Univ Technol Fac Engn Dept Chem Engn Bangkok 10140 Thailand King Mongkuts Univ Technol Fac Engn Dept Chem Engn Bangkok 10800 Thailand Natl Ctr Excellence Petr Petrochem & Adv Mat Bangkok 10330 Thailand

This paper proposes a method for solving mixed-integer nonlinear programming problems to achieve or approach the optimal solution by using modified genetic algorithms. The representation scheme covers both integer and real variables for solving mixed-integer nonlinear programming, nonlinear programming, and nonlinear integer programming. The repairing strategy, a secant method incorporated with a bisection method, plays an important role in converting infeasible chromosomes to feasible chromosomes at the constraint boundary. To prevent premature convergence, the appropriate diversity of the structures in the population must be controlled. A cross-generational probabilistic survival selection method (CPSS) is modified for real number representation corresponding to the representation scheme. The efficiency of the proposed method was validated with several numerical test problems and showed good agreement.

关键词： Genetic Algorithms mixed integer nonlinear programming Repairing Strategy CPSS Modified Genetic Algorithms

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An improved Estimation of Distribution Algorithm for Solving Constrained mixed-integer nonlinear programming Problems

An improved Estimation of Distribution Algorithm for Solving...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Molina Perez, Daniel Alfredo Portilla-Flores, Edgar Mezura-Montes, Efren Vega-Alvarado, Eduardo Inst Politecn Nacl CIDETEC Ciudad De Mexico Mexico Inst Politecn Nacl UPIIT Tlaxcala Mexico Univ Veracruz Artificial Intelligence Res Ctr Veracruz Veracruz Mexico

ISBN: (数字)9781665467087

ISBN: (纸本)9781665467087

In a mixed-integer nonlinear programming problem, integer restrictions divide the feasible region into discontinuous feasible parts with different sizes. Evolutionary Algorithms (EAs) are usually vulnerable to being trapped in larger discontinuous feasible parts. In this work, an improved version of an Estimation of Distribution Algorithm (EDA) is developed, where two new operations are proposed. The first one establishes a link between the learning-based histogram model and the s-constrained method. Here, the constraint violation level of the s-constrained method is used to explore the smaller discontinuous parts and form a better statistical model. The second operation is the hybridization of the EDA with a mutation operator to generate offspring from both the global distribution information and the parent information. A benchmark is used to test the performance of the improved proposal. The results indicated that the proposed approach shows a better performance against other tested EAs. This new proposal solves to a great extent the influence of the larger discontinuous feasible parts, and improve the local refinement of the real variables.

关键词： estimation of distribution algorithm evolutionary algorithms integer restriction handling mixed integer nonlinear programming

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A hybrid algorithm framework for heat exchanger networks synthesis considering the optimal locations of multiple utilities

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CHEMICAL ENGINEERING SCIENCE 2025年 301卷

作者： Yang, Lu Yang, Zekun Akram, Naeem Chang, Chenglin Mo, Wenlong Shen, Weifeng Zhang, Nan Smith, Robin Chongqing Univ Sch Chem & Chem Engn Chongqing 400044 Peoples R China Univ Manchester Ctr Proc Integrat Dept Chem Engn Manchester England Minhaj Univ Lahore Sch Chem Engn Lahore 54000 Punjab Pakistan Xinjiang Univ Coll Chem Engn Key Lab Oil & Gas Fine Chem Minist Educ & Xinjiang Uygur Autonomous Reg Urumqi 830046 Peoples R China Xinjiang Yihua Chem Co Ltd Changji 831100 Peoples R China

This work focuses on heat exchanger networks (HENs) synthesis (HENS) considering the optimal locations of multiple utilities. Based on an extended stage-wise superstructure where available heaters and coolers are placed at all stages, HENS is modeled as a computationally-hard mixed integer nonlinear programming (MINLP) problem. To obtain high-quality solutions, we propose a new hybrid algorithm framework that combines deterministic algorithm (commercial solver) and genetic algorithm (GA) without the use of penalty functions. In the outer level of the framework, GA is employed to optimize the integer variables which represent the existences of matches between process streams as well as the available heaters and coolers at intermediate stages. In the inner level, a reduced-size MINLP model is built to minimize the total annualized costs (TACs) of HENs generated in the outer level. We also propose three new sets to exclude infeasible stream matches, thereby the HENs generated in the outer level are all feasible and our GA does not need any penalty terms. Four literature examples are tested and optimal solutions with lower TACs are obtained within acceptable computing time compared to solutions reported in literature.

关键词： Heat exchanger networks Hybrid algorithm framework Locations of utilities Superstructure mixed integer nonlinear programming

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A MINP model and hybrid heuristic algorithm for railway cold chain service network design problem: a case study of China

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2025年第1期63卷 171-192页

作者： Li, Dandan Gan, Mi Qiao, Yu Ou, Qichen Liu, Xiaobo Southwest Jiaotong Univ Sch Transportat & Logist Chengdu Peoples R China Southwest Jiaotong Univ Natl Engn Lab Integrated Transportat Big Data Appl Chengdu Peoples R China Southwest Jiaotong Univ 111 Erhuanlu Beiyiduan Chengdu 610031 Sichuan Peoples R China

Railway Cold Chain Service Network Design (RCC-SND) aims to optimise resource allocation and utilisation to ensure the efficient transportation of perishable foods. In this study, we propose a mixed integer nonlinear programming (MINP) model to solve the hub selection, service frequency determination, wagon flow organisation and routing problems in RCC-SND. The application of the model is illustrated using the real network comprising 163 cities in China. The PSO-GA algorithm is proven effective in solving the problem. Furthermore, we introduce two future scenarios to assess the carbon reduction potential and economic costs of railway cold chain operations. There are some main findings: as the hub number increases from 30 to 40, the total cost decreases, as the hub continues to rise, this reduction is offset by the increased hub operational costs. Increasing hubs can enhance direct train frequency while causing railway capacity redundancy, this redundancy can be addressed by freight volume increase, as demonstrated in this study, 4.5 times increase in freight volume can boost train utilisation by up to 12.5%. By 2030, railway cold chain carbon emissions are projected to exceed 1.1 million tons, the adoption of hydrogen as alternative energy is an economically viable solution for emission reduction.

关键词： Perishable food industry railway cold chain transportation hub-routing problem mixed integer nonlinear programming SDG 9: Industry innovation and infrastructure

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