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Reward Factor-Based Multiple Agile Satellites Scheduling With Energy and Memory Constraints

IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2022年第4期58卷 3090-3103页

作者： Chatterjee, Abhijit Tharmarasa, Ratnasingham McMaster Univ Dept Elect & Comp Engn Hamilton ON L8S 4L8 Canada

Earth observing satellites (EOS) orbit around the earth to perform observation tasks specified by users. The additional maneuverability resulting from higher degrees of freedom than nonagile EOS (N-AEOS) provides agile EOS (AEOS) a significantly larger visible time window to complete the tasks. As a consequence, the task scheduling for AEOS is much more computationally complex than N-AEOS. In this article, a mixed-integer nonlinear optimization problem is formulated to find a near-optimal task allocation for a realistic AEOS scheduling problem. The satellite resources, such as energy and memory constraints, are considered in this problem. A reward factor is used to address the requirement of multiple scans in order to complete a task. A probability factor is also taken into consideration to incorporate the uncertainty of successful scans due to external factors, such as cloud coverage. An elitist mixed coded genetic algorithm-based satellite scheduling (EMCGA-SS) algorithm is proposed to solve the formulated problem. EMCGA-SS is extended to elitist mixed coded hybrid genetic algorithm-based satellite scheduling by combining a hill-climber mechanism in order to have better initialization. Experimental results to illustrate the performance of the algorithms and a comparison with some widely used methodologies are also presented.

关键词： Task analysis Satellites Genetic algorithms Earth Processor scheduling Earth Observing System Scheduling Agile satellite scheduling energy and memory constraints genetic algorithm mixed-integer nonlinear programming reward factor

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Hybrid Machine Learning-Mathematical programming Approach for Optimizing Gas Production and Water Management in Shale Gas Fields

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ACS SUSTAINABLE CHEMISTRY & ENGINEERING 2023年第15期11卷 6043-6056页

作者： Lopez-Flores, Francisco Javier Lira-Barragan, Luis Fernando Rubio-Castro, Eusiel El-Halwagi, Mahmoud M. Ponce-Ortega, Jose Maria Univ Michoacana Chem Engn Dept Morelia 58060 Michoacan Mexico Univ Autonoma Sinaloa Chem & Biol Sci Dept Culiacan 80010 Sinaloa Mexico Texas A&M Univ Chem Engn Dept College Stn TX 77843 USA

This paper presents a novel mathematical program-ming approach that simultaneously incorporates a mixed-integer nonlinear programming formulation with machine learning models to determine the operating conditions, gas production, and optimal water management for the completion phase in shale gas fields. The dataset for the development of an artificial neural network model has been collected from the Eagle Ford Texas formation. The total cumulative gas production and flowback water generated in shale gas wells are selected as output variables. The mathematical optimization model considers machine learning models for each well, mass balances, treatment, storage, reuse, and disposal options as well as well location selection and associated costs and revenues from the sale of the shale gas produced. A case study has been used to illustrate the benefits of the proposed approach. The compromise solution offers a water consumption per produced energy of 6.71 L/GJ in addition to the fact that 27% of the total fracture water required can be obtained by reusing the flowback water with attractive economic indicators.

关键词： machine learning flowback water shale gas production hydraulic fracturing artificial neural network mixed-integer nonlinear programming

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An efficient strategy for the activation of MIP relaxations in a multicore global MINLP solver

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JOURNAL OF GLOBAL OPTIMIZATION 2018年第3期70卷 497-516页

作者： Zhou, Kai Kilinc, Mustafa R. Chen, Xi Sahinidis, Nikolaos V. Zhejiang Univ Coll Control Sci & Engn State Key Lab Ind Control Technol Hangzhou 310027 Zhejiang Peoples R China Carnegie Mellon Univ Dept Chem Engn 5000 Forbes Ave Pittsburgh PA 15213 USA

Solving mixed-integer nonlinear programming (MINLP) problems to optimality is a NP-hard problem, for which many deterministic global optimization algorithms and solvers have been recently developed. MINLPs can be relaxed in various ways, including via mixed-integer linear programming (MIP), nonlinear programming, and linear programming. There is a tradeoff between the quality of the bounds and CPU time requirements of these relaxations. Unfortunately, these tradeoffs are problem-dependent and cannot be predicted beforehand. This paper proposes a new dynamic strategy for activating and deactivating MIP relaxations in various stages of a branch-and-bound algorithm. The primary contribution of the proposed strategy is that it does not use meta-parameters, thus avoiding parameter tuning. Additionally, this paper proposes a strategy that capitalizes on the availability of parallel MIP solver technology to exploit multicore computing hardware while solving MINLPs. Computational tests for various benchmark libraries reveal that our MIP activation strategy works efficiently in single-core and multicore environments.

关键词： Global optimization mixed-integer nonlinear programming mixed-integer linear programming Parallel computing Multicore architectures Portfolios of relaxations

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Quantifying Double McCormick

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MATHEMATICS OF OPERATIONS RESEARCH 2017年第4期42卷 1230-1253页

作者： Speakman, Emily Lee, Jon Univ Michigan Ann Arbor MI 48109 USA

When using the standard McCormick inequalities twice to convexify trilinear monomials, as is often the practice in modeling and software, there is a choice of which variables to group first. For the important case in which the domain is a nonnegative box, we calculate the volume of the resulting relaxation, as a function of the bounds defining the box. In this manner, we precisely quantify the strength of the different possible relaxations defined by all three groupings, in addition to the trilinear hull itself. As a by-product, we characterize the best double-McCormick relaxation. We wish to emphasize that, in the context of spatial branch and bound for factorable formulations, our results do not only apply to variables in the input formulation. Our results apply to monomials that involve auxiliary variables as well. So, our results apply to the product of any three (possibly complicated) expressions in a formulation.

关键词： global optimization mixed-integer nonlinear programming spatial branch and bound convexification bilinear trilinear McCormick inequalities

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Multi-objective cell formation problem considering work-in-process minimization

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2015年第9-12期76卷 1947-1955页

作者： Rafiei, Hamed Rabbani, Masoud Nazaridoust, Babak Ramiyani, Sara Saeidi Univ Tehran Coll Engn Sch Ind & Syst Engn Tehran Iran

This paper addresses designing a dynamic cellular manufacturing system which considers optimizing batch sizes for inter-cell moves in order to pursue fundamentals of Just-In-Time production philosophy. To do so, a bi-objective mixed-integer nonlinear programming model is proposed with two conflicting objective functions: minimizing sum of machine purchasing, operating, inter-cell moves, machine relocation, and machine transferring cost, and minimizing work-in-process (WIP) with regard to inter-cell batch sizes. Also, the best time to sell unused machines is obtained by calculating their salvage values. Finally, the proposed model is validated using numerical experiments and hence, the resulted WIP inventories are assessed through the conducted numerical experiments, and sensitivity analysis is provided with respect to three major parameters (machine purchasing cost, inter-cell material handling cost, and part demand) applied in the proposed model.

关键词： Multi-objective optimization Dynamic cellular manufacturing Work-in-process mixed-integer nonlinear programming

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A particle-based simplified swarm optimization algorithm for reliability redundancy allocation problems

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2015年第Oct.期142卷 221-230页

作者： Huang, Chia-Ling Kainan Univ Dept Logist & Shipping Management Taoyuan 33857 Taiwan

This paper proposes a new swarm intelligence method known as the Particle-based Simplified Swarm Optimization (PSSO) algorithm while undertaking a modification of the Updating Mechanism (UM), called N-UM and R-UM, and simultaneously applying an Orthogonal Array Test (OA) to solve reliability-redundancy allocation problems (RRAPs) successfully. One difficulty of RRAP is the need to maximize system reliability in cases where the number of redundant components and the reliability of corresponding components in each subsystem are simultaneously decided with nonlinear constraints. In this paper, four REAP benchmarks are used to display the applicability of the proposed PSSO that advances the strengths of both PSO and SSO to enable optimizing the RRAP that belongs to mixed-integer nonlinear programming. When the computational results are compared with those of previously developed algorithms in existing literature, the findings indicate that the proposed PSSO is highly competitive and performs well. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Reliability Redundancy allocation problem PSO SSO mixed-integer nonlinear programming

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A novel MINLP-based representation of the original complex model for predicting gasoline emissions

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COMPUTERS & CHEMICAL ENGINEERING 2008年第12期32卷 2857-2876页

作者： Furman, Kevin C. Androulakis, Ioannis P. ExxonMobil Res & Engn Co Corp Strateg Res Annandale NJ 08801 USA Rutgers State Univ Dept Biomed Engn Piscataway NJ 08854 USA

The Environmental Protection Agency (EPA) introduced Reformulated Gasoline (RFG) requirements as a measure to reduce emissions from gasoline-powered vehicles in certain geographic areas. As part of this effort, the EPA developed empirical models for predicting emissions as a function of gasoline properties and established statutory baseline emissions from a representative set of gasolines. All reformulated gasoline requires certification via this model, known as the Complex Model, and all refiners and importers calculate emissions performance reductions from the statutory baseline gasoline. The current representation of the Complex Model is extremely difficult to implement within refinery operations models or to use in combination with models for designer gasoline. RFG and boutique fuels are key driving forces in the North American refining industry. The RFG models introduce increasingly complex constraints with the major limitation that they are implicitly defined through a series of complicated disjunctions assembled by the EPA in the form of spreadsheets. This implicit and cumbersome representation of the emissions predictive models renders rigorous optimization and sensitivity analysis very difficult to address directly. In this paper, we discuss how the federal government requirements for reformulated gasoline can be restated as a set of mixed-integer nonlinear programming (MINLP) constraints with the aid of disjunctive programming techniques. We illustrate the use of this model with two simple example fuel blending problems. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： Reformulated gasoline mixed-integer nonlinear programming Generalized disjunctive programming

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A mixed-integer nonlinear program for the optimal design and dispatch of distributed generation systems

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OPTIMIZATION AND ENGINEERING 2014年第1期15卷 167-197页

作者： Pruitt, Kristopher A. Leyffer, Sven Newman, Alexandra M. Braun, Robert J. Colorado Sch Mines Div Econ & Business Golden CO 80401 USA Argonne Natl Lab Math & Comp Sci Div Argonne IL 60439 USA Colorado Sch Mines Dept Mech Engn Golden CO 80401 USA

Maturing distributed generation (DG) technologies have promoted interest in alternative sources of energy for commercial building applications due to their potential to supply on-site heat and power at a lower cost and emissions rate compared to centralized generation. Accordingly, we present an optimization model that determines the mix, capacity, and operational schedule of DG technologies that minimize economic and environmental costs subject to the heat and power demands of a building and to the performance characteristics of the technologies. The technologies available to design the system include lead-acid batteries, photovoltaic cells, solid oxide fuel cells, heat exchangers, and a hot water storage tank. Modeling the acquisition and operation of discrete technologies requires integer restrictions, and modeling the variable electric efficiency of the fuel cells and the variable temperature of the tank water introduces nonlinear equality constraints. Thus, our optimization model is a nonconvex, mixed-integer nonlinear programming (MINLP) problem. Given the difficulties associated with solving large, nonconvex MINLPs to global optimality, we present convex underestimation and linearization techniques to bound and solve the problem. The solutions provided by our techniques are close to those provided by existing MINLP solvers for small problem instances. However, our methodology offers the possibility to solve large problem instances that exceed the capacity of existing solvers and that are critical to the real-world application of the model.

关键词： Global optimization mixed-integer nonlinear programming Distributed generation Combined heat and power

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A new approach to security-constrained generation scheduling of large-scale power systems with a piecewise linear ramping model

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2012年第1期34卷 121-131页

作者： Lyu, J. K. Kim, M. K. Yoon, Y. T. Park, J. K. Dong A Univ Dept Elect Engn Pusan 604714 South Korea Seoul Natl Univ Sch Elect Engn & Comp Sci Seoul 151744 South Korea

This paper presents a new and efficient approach to determine security-constrained generation scheduling (SCGS) in large-scale power systems, taking into account dispatch, network, and security constraints in pre and post-contingency states. A novel ramp rate limit is also modeled as a piecewise linear function in the SCGS problem to reflect more practical characteristics of the generating units. Benders decomposition is applied to this constrained solution process to obtain an optimal SCGS problem based on mixed-integer nonlinear programming (MINLP). The formulation can be embedded in two stages. First, a MIP is formulated in the master problem to solve a unit commitment (UC) problem. This stage determines appropriate on/off states of the units. The second stage, the subproblem, is formulated as a NLP to solve a security-constrained economic dispatch (SCED) problem. This stage is used to determine the feasibility of the master problem solution. It provides information to formulate the benders cuts that connect both problems. The proposed approach is tested in the IEEE 118-bus system to show its effectiveness. The simulation results are more realistic and feasible, whilst assuring an acceptable computation time. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Security-constrained generation scheduling mixed-integer nonlinear programming Piecewise linear ramp limit Benders decomposition

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Hydrogen network retrofit via flexibility analysis: The steady-state flexibility index

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CHEMICAL ENGINEERING RESEARCH & DESIGN 2017年 117卷 83-94页

作者： Birjandi, Mohammad Reza Sardashti Shahraki, Farhad Razzaghi, Kiyanoosh Univ Sistan & Baluchestan Dept Chem Engn Zaheolan 98164 Iran

A systematic approach is presented to retrofit design of a hydrogen distribution network. A methodology is based on the steady-state flexibility index (FIs) for optimizing network and enhancing the efficiency of purifiers. The main object of this technique is to investigate hydrogen network flexibility under severe operation uncertainty and to reduce streams to purifiers by consideration inlet feed purity of purifiers. The presented approach is applied to the hydrogen network of, a real installation. (C) 2016 The Author(s). Published by Elsevier B.V. on behalf of Institution of Chemical Engineers. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Hydrogen network Flexibility analysis mixed-integer nonlinear programming nonlinear programming

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