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Linearization of Euclidean norm dependent inequalities applied to multibeam satellites design

COMPUTATIONAL OPTIMIZATION AND APPLICATIONS 2019年第2期73卷 679-705页

作者： Camino, Jean-Thomas Artigues, Christian Houssin, Laurent Mourgues, Stephane Univ Toulouse CNRS UPS LAAS Toulouse France Airbus Def & Space Space Syst Telecommun Syst Dept Toulouse France

Euclidean norm computations over continuous variables appear naturally in the constraints or in the objective of many problems in the optimization literature, possibly defining non-convex feasible regions or cost functions. When some other variables have discrete domains, it positions the problem in the challenging mixed integer nonlinear programming (MINLP) class. For any MINLP where the nonlinearity is only present in the form of inequality constraints involving the Euclidean norm, we propose in this article an efficient methodology for linearizing the optimization problem at the cost of entirely controllable approximations even for non convex constraints. They make it possible to rely fully on mixed integer Linear programming and all its strengths. We first empirically compare this linearization approach with a previously proposed linearization approach of the literature on the continuous k-center problem. This methodology is then successfully applied to a critical problem in the telecommunication satellite industry: the optimization of the beam layouts in multibeam satellite systems. We provide a proof of the NP-hardness of this very problem along with experiments on a realistic reference scenario.

关键词： mixed integer Linear programming mixed integer nonlinear programming Euclidean norm linearization k-center problem Multibeam satellites

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Optimal exact design of double acceptance sampling plans by attributes

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JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION 2019年第17期89卷 3313-3329页

作者： Duarte, Belmiro P. M. Granjo, Jose F. O. Coimbra Polytech ISEC Dept Chem & Biol Engn R Pedro Nunes P-3030199 Coimbra Portugal Univ Coimbra Dept Chem Engn CIEPQPF Coimbra Portugal

The design of double acceptance sampling (AS) plans for attributes based on the operating characteristic curve paradigm is usually addressed by enumeration algorithms. These AS plans may be non optimal regarding the sample size to inspect as they were obtained without the requirement that the constraints at the OC curve controlled points are not violated for minimum Average Sample Number (ASN) scenarios. An approach based on mathematical programming is proposed to systematically design double AS plans for attributes, where the characteristics controlled are modelled by binomial or Poisson distributions. Specifically, mixed integer nonlinear programming (MINLP) formulations are developed and combined with an enumeration algorithm that allows finding ASN minimax optimal plans. A theoretical result is developed with the purpose of assuring the global optimum design is reached by iteration where a convenient solver is used to find local optima. To validate the algorithm, we compare our results with those of tables commonly used for practical purposes, consider different rates of risk, and setups commonly used in Lot Quality Assurance Plans (LQAS) for health monitoring programmes. Finally, we compare AS plans determined for processes described by binomial and Poisson distributions.

关键词： Acceptance sampling plan mixed integer nonlinear programming operating characteristic curve average sample number

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Identification of groundwater contamination sources using a statistical algorithm based on an improved Kalman filter and simulation optimization

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HYDROGEOLOGY JOURNAL 2019年第8期27卷 2919-2931页

作者： Li, Jiuhui Lu, Wenxi Wang, Han Fan, Yue Jilin Univ Key Lab Groundwater Resources & Environm Minist Educ Changchun 130021 Jilin Peoples R China Jilin Univ Coll New Energy & Environm Changchun 130021 Jilin Peoples R China

Identification of groundwater contamination sources, as a solution to an inverse problem, is mainly based on simulation optimization methods. However, the establishment and solution of an optimization model are extremely complicated for the inverse problem, where many decision variables need to be identified. Combining simulation optimization methods with other mathematical methods can effectively avoid this shortcoming. In order to identify features of contamination sources, the Kalman filter is combined with a mixed-integer nonlinear programming optimization model (MINLP). Firstly, the Kalman filter is improved by U-D decomposition (a matrix decomposition method) to identify the number and approximate locations of real contamination sources among potential contamination sources. A 0-1 MINLP solution is then employed to identify the accurate location and release history of the contamination source. Kriging is used to produce a surrogate model for the numerical simulation model. The surrogate model approximates the numerical simulation model, which can be embedded in the 0-1 MINLP instead of the numerical simulation model and it can be called directly during the iteration of the optimization model, thereby avoiding manually coupling the numerical simulation model with the optimization model during the solution process. Results demonstrate that the improved Kalman filter exhibits better numerical stability, where it can effectively avoid the filter divergence problem as well as solve the number and approximate locations of contamination sources in the inverse identification problem. The accurate location and release history of the contamination source can be effectively identified by using the 0-1 MINLP.

关键词： Contamination Improved Kalmer filter mixed integer nonlinear programming Numerical modeling Surrogate model

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Reverse engineering genetic networks using nonlinear saturation kinetics

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BIOSYSTEMS 2019年 182卷 30-41页

作者： Youseph, Ahammed Sherief Kizhakkethil Chetty, Madhu Karmakar, Gour Monash Univ Fac Informat Technol Clayton Vic 3800 Australia Federat Univ Australia Sch Sci Engn & Informat Technol Gippsland 3842 Australia

A gene regulatory network (GRN) represents a set of genes along with their regulatory interactions. Cellular behavior is driven by genetic level interactions. Dynamics of such systems show nonlinear saturation kinetics which can be best modeled by Michaelis-Menten (MM) and Hill equations. Although MM equation is being widely used for modeling biochemical processes, it has been applied rarely. for reverse engineering GRNs. In this paper, we develop a complete framework for a novel model for GRN inference using MM kinetics. A set of coupled equations is first proposed for modeling GRNs. In the coupled model, Michaelis-Menten constant associated with regulation by a gene is made invariant irrespective of the gene being regulated. The parameter estimation of the proposed model is carried out using an evolutionary optimization method, namely, trigonometric differential evolution (TDE). Subsequently, the model is further improved and the regulations of different genes by a given gene are made distinct by allowing varying values of Michaelis-Menten constants for each regulation. Apart from making the model more relevant biologically, the improvement results in a decoupled GRN model with fast estimation of model parameters. Further, to enhance exploitation of the search, we propose a local search algorithm based on hill climbing heuristics. A novel mutation operation is also proposed to avoid population stagnation and premature convergence. Real life benchmark data sets generated in vivo are used for validating the proposed model. Further, we also analyze realistic in silico datasets generated using GeneNetweaver. The comparison of the performance of proposed model with other existing methods shows the potential of the proposed model.

关键词： Gene regulatory network Michaelis-Menten kinetics Reverse engineering mixed integer nonlinear programming Trigonometric differential evolution Local search

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Superstructure design and optimization on supercritical carbon dioxide cycle for application in concentrated solar power plant

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ENERGY CONVERSION AND MANAGEMENT 2020年第0期206卷 112290-000页

作者： Ma, Yuegeng Morosuk, Tatiana Luo, Jing Liu, Ming Liu, Jiping Xi An Jiao Tong Univ MOE Key Lab Thermal Fluid Sci & Engn Xianning West Road 28 Xian 710049 Peoples R China Tech Univ Berlin Inst Energy Engn Marchstr 18 D-10587 Berlin Germany Xi An Jiao Tong Univ State Key Lab Multiphase Flow Power Engn Xianning West Road 28 Xian 710049 Peoples R China

A superstructure-based method is applied to optimize the design of supercritical carbon dioxide cycle for concentrated solar power systems. A superstructure is designed for the cycle featuring various options in cold-end and hot-end configurations. Process integration and cycle variables are simultaneously optimized for the superstructure based on the integrated thermoeconomic model of the overall system. Optimizations are performed for eight cases with different designs of maximum and minimum temperatures. Sensitivity analyses are conducted on the design parameters and related capital costs of subsystems of the overall system. 1.5-13.5% reduction in levelized cost of electricity is obtained through the superstructure-based optimization relative to the corresponding base cases. The cost of thermal storage system at optimal condition is significantly surged by 36.3-111.6% when the maximum temperature surpasses 600 degrees C, which consequently leads to the variation in configuration for the optimal cycle. The minimum levelized cost of electricity is obtained at 600 degrees C with a reduction of up to 4.1% compared to that at 550 degrees C. The further increase in the maximum temperature beyond 600 degrees C will be followed by 8.6%-0.2% rise in the levelized cost of electricity. The improvements in maximum pressure and turbomachinery performance can lead to significant reduction in levelized cost of electricity, especially at high maximum and minimum temperatures. However, these improvements have relatively uneventful effects on the configuration of the optimal cycle. The capital cost of thermal storage system has significant effects on the design of the optimal cycle configuration whereas the variation in the capital cost of cycle system has less eventful effects. The effect of capital cost of cycle system is more dominant than that of the thermal storage system at 550 degrees C and 600 degrees C maximum temperatures in terms of levelized cost of electricity, while

关键词： Concentrated solar power plant Supercritical carbon dioxide cycle Superstructure design mixed integer nonlinear programming Optimization

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A Hybrid Optimization Approach for Residential Energy Management

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IEEE ACCESS 2020年 8卷 225201-225209页

作者： Huang, Yantai Zhang, Jinjiang Mo, Yujie Lu, Shuijin Ma, Junchao Zhejiang Univ Sci & Technol Sch Automat & Elect Engn Hangzhou 310023 Peoples R China Tsinghua Univ Yangtze Delta Reg Inst Hangzhou 314006 Peoples R China State Grid Zhejiang Elect Power Res Inst Hangzhou 310014 Peoples R China

In the past few years, the development of demand response (DR) programs for smart grid systems has provided residential customers with a real opportunity to participate in DR-driven projects. One typical DR-related application now available is automated energy scheduling for residential building, which can be used to help reduce energy costs. Residential energy scheduling focuses on saving cost by managing the operation time and energy consumption level of different appliances. Demand Response programs present as NP-hard problems, the equations are non-convex mixed integer non-linear problems (MINLP), for which it is difficult to obtain an optimal solution. In relation to residential DR, we propose a hybrid approach here that is able to solve an MINLP. The problem is decomposed into discrete and continuous variables. The discrete variables are optimized by using a particle swarm optimization (PSO) algorithm, whilst the continuous variables are determined by using a gradient-based deterministic algorithm. The superiority of the proposed algorithm was demonstrated by compared with commercial optimization software and heuristic based algorithm. Furthermore, the effectiveness of the proposed method in residential DR was tested and the results were presented.

关键词： Batteries Fuel cells Buildings Optimization Cogeneration Home appliances Stochastic processes mixed integer nonlinear programming residential energy management particle swarm optimization

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Sustainable reverse logistics optimization with triple bottom line approach: An integration of disassembly line balancing

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JOURNAL OF CLEANER PRODUCTION 2020年 270卷 122475-122475页

作者： Budak, Aysenur Gebze Tech Univ Ind Engn Dept Gebze Turkey

The objective of this study is to develop the sustainable reverse logistics network with integration of disassembly line balancing to examine decisions in view of Triple Bottom Line. The Triple Bottom Line approach is a framework that considers three strategies which are economic, environmental, and social. Disassembly has great importance in reverse logistics operations. Therefore, disassembly line balancing approach has been integrated into sustainable reverse logistics network problem for the first time. A Multi period Multi-Objective mixed integer nonlinear programming model is proposed and solved with Improved Augmented Epsilon Constraint (AUGMECON-2) technique. Pareto optimal solution set is obtained. In order to demonstrate the applicability of the suggested model, developed solution procedure has been applied on end of life mobile phone recycling network. Objectives are indicated as social impact maximization, economic and environmental effect minimization. This study is applied for a three-year time period. Disassembly processes are also taken into account. The model respectively determines: (i) optimal quantity of WEEE between the facilities and inventory amounts, (ii) the number of workstations in disassembly centers (iii) the number of facilities that will be opened (iv) optimal task assignments to workstations in disassembly centers. Sensitivity analysis is proposed to examine the impact of incremental changes in WEEE amounts on the sustainable reverse logistics network. According to each scenario the number of changes in facilities, objective function values and tasks assigned to workstations are specified. This developed model could offer managers to take strategic decisions in recycling processes with regard to environmental and social impact and help to gain managerial insights as well. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Sustainable reverse logistics mixed integer nonlinear programming Multi objective optimization Triple bottom line Disassembly line balancing

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Near-optimal time series sampling based on the reduced Hessian

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AICHE JOURNAL 2020年第7期66卷 e16248-e16248页

作者： Chen, Weifeng Biegler, Lorenz T. Zhejiang Univ Technol Coll Informat Engn Hangzhou Peoples R China Carnegie Mellon Univ Dept Chem Engn Ctr Adv Proc Decis Making 5000 Forbes Ave Pittsburgh PA 15213 USA

A model-based experimental design strategy is developed to select a minimal set of measured points from a time series of experiments. Based on reduced Hessian information embedded within a mixed integer nonlinear program, an optimal time series is determined that leads to well-fitted models, with ratios of parameter standard deviation to estimated value within acceptable bounds. Demonstrated on three examples, the proposed approach is effective and efficient especially for time-consuming and expensive measurements.

关键词： experimental design mixed integer nonlinear programming nonlinear optimization parameter estimation

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Integrated optimisation of stope boundary and access layout for underground mining operations

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MINING TECHNOLOGY-TRANSACTIONS OF THE INSTITUTIONS OF MINING AND METALLURGY 2019年第4期128卷 193-205页

作者： Hou, Jie Xu, Chaoshui Dowd, Peter Alan Li, Guoqing Univ Sci & Technol Sch Civil & Resource Engn Beijing Peoples R China Univ Adelaide Sch Civil Environm & Min Engn Adelaide SA Australia Wuhan Univ Sci & Technol Sch Resource & Environm Engn Wuhan Hubei Peoples R China

For underground mines, two major designs that need to be optimised are the stope boundary and the access layout. The optimisation of these two components cannot be done independently as ore extracted from stopes is transported to the surface via the access network. Different stope locations result in different associated access development costs. This paper describes a proposed integrated optimisation model to optimise stope and access layouts simultaneously. The interactions and interdependency between stopes and access are ensured by a dynamic access development cost analysis based on recursive computations. mixed integer non-linear programming is used to maximise the overall economic value subject to the constraints of generating optimal stopes and access layout, which is solved by a Genetic Algorithm modified for computational efficiency. A stratiform gold deposit is used as a case study to demonstrate that the new approach can provide a more profitable solution compared with traditional approaches.

关键词： Underground mine optimisation optimal stope layout and access development mixed integer nonlinear programming dynamic access development cost model

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Disruption Management in Liner Shipping: A Service-Cost Trade-off Model for Vessel Schedule Recovery Problem 5

Disruption Management in Liner Shipping: A Service-Cost Trad...

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5th International Conference on Transportation Information and Safety (ICTIS)

作者： Xing, Jiangbo Wang, Yuhong Ningbo Univ Fac Maritime & Transportat Ningbo Peoples R China

ISBN: (纸本)9781728104898

This paper focuses on the Vessel Schedule Recovery Problem (VSRP) with container flow recovery which aims to find the trade-off between service standards and recovery costs. Firstly, we divide the recovery strategies into three levels for hierarchical management. Second, a MINLP model is presented and can be solved by LINGO. At last, a real-life case study is investigated, and computational results indicate the model can be solved in a few minutes. It is verified that the proposed model is valid.

关键词： Disruption management Liner shipping Vessel schedule recovery problem mixed integer nonlinear programming

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