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Multi-objective mixed integer strategy for the optimisation of biological networks

IET SYSTEMS BIOLOGY 2010年第3期4卷 236-248页

作者： Sendin, J. O. H. Exler, O. Banga, J. R. IIM CSIC Spanish Natl Res Council Proc Engn Grp Vigo 36208 Spain Univ Bayreuth Dept Comp Sci D-95440 Bayreuth Germany

In this contribution, the authors consider multi-criteria optimisation problems arising from the field of systems biology when both continuous and integer decision variables are involved. Mathematically, they are formulated as mixed-integer non-linear programming problems. The authors present a novel solution strategy based on a global optimisation approach for dealing with this class of problems. Its usefulness and capabilities are illustrated with two metabolic engineering case studies. For these problems, the authors show how the set of optimal solutions (the so-called Pareto front) is successfully and efficiently obtained, providing further insight into the systems under consideration regarding their optimal manipulation.

关键词： complex networks biology computing integer decision variables Pareto front global optimisation approach mixed-integer nonlinear programming Pareto optimisation Systems theory applications in biology and medicine multiobjective mixed integer strategy General, theoretical, and mathematical biophysics multicriteria optimisation problems continuous decision variables Optimisation techniques systems biology nonlinear programming metabolic engineering biological network optimisation

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Coordinated operation of electricity and gas-hydrogen systems with transient gas flow and hydrogen concentration tracking

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ELECTRIC POWER SYSTEMS RESEARCH 2022年第0期211卷

作者： Mhanna, Sleiman Saedi, Isam Mancarella, Pierluigi Zhang, Zihang Univ Melbourne Dept Elect & Elect Engn Melbourne 3010 Australia Xi An Jiao Tong Univ State Key Lab Elect Insulat & Power Equipment Xian 710049 Peoples R China Univ Manchester Dept Elect & Elect Engn Manchester M13 9PL England

This paper addresses the problem of coordinating the operation of electricity and natural gas (NG) transmission systems with green hydrogen (H2) production and injection into existing NG networks. In particular, the operation of the two systems consists of a two-stage optimization framework that solves a network-constrained unit commitment (UC) problem with transmission power losses to obtain the profiles of gas energy demands from gas-powered generators and the maximum allowable H2 injection flow rates from power-to-gas (PtG), which are then used as inputs to an optimal transient NG-H2 flow problem with H2 concentration tracking. The nonlinearities introduced by the discretization of the H2 concentration tracking equations are particularly challenging to solve using second-order nonlinear programming (NLP) methods. Moreover, the nonlinear constraints capturing transmission line losses make the electricity operational problem intractable if solved with mixed-integer NLP methods. Therefore, this work leverages the reliability and scalability of linear programming (LP) by designing two novel and distinct sequential LP (SLP) methods that exploit the particular structures of the two problems to find feasible, possibly optimal solutions, using only first-order information. The algorithmic framework is demonstrated on the IEEE 24-bus RTS connected to the 22-node Belgian gas network. This paper is the first to demonstrate H2 concentration tracking under transient gas flow in a multi-energy optimization framework on a realistic gas transmission network with multiple H2 injection locations.

关键词： Transient gas flow Concentration tracking Green hydrogen Sequential linear programming Unit commitment Integrated modeling mixed-integer nonlinear programming

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A trust region framework for heat exchanger network synthesis with detailed individual heat exchanger designs

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COMPUTERS & CHEMICAL ENGINEERING 2021年 153卷 107447-107447页

作者： Kazi, Saif R. Short, Michael Biegler, Lorenz T. Carnegie Mellon Univ Dept Chem Engn Pittsburgh PA 15213 USA Univ Surrey Dept Chem & Proc Engn Guildford Surrey England

We develop a trust region filter strategy for simultaneous optimal design of heat exchanger networks that includes detailed design of shell-and-tube heat exchangers. The strategy first solves a mixed-integer nonlinear programming (MINLP) formulation with shortcut models to generate candidate network topologies, which are then used in a non-isothermal mixing nonlinear programming (NLP) suboptimization with detailed optimal exchanger design models embedded using a modified trust region filter (TRF) algorithm. An integer cut based strategy is used to bound the solutions from MINLP and the NLP which aids in convergence to the solution of the overall simultaneous design problem. Under assumptions, the TRF based strategy can guarantee convergence to near optimal solutions of the overall design problem. The presented solution strategy is thus able to find optimal heat exchanger network designs based on the simultaneous optimization of the network topology and mass and energy balances, together with detailed shell-and-tube heat exchanger optimization, including the number of shell and tube passes, pressure drops, and tubes, tube lengths, etc. The proposed strategy is tested on three literature based case studies and their results are compared with previous studies to showcase its performance. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Heat exchanger network synthesis nonlinear programming mixed-integer nonlinear programming Trust region filter optimization Optimization Process system engineering

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The MINLP optimization approach to structural synthesis - Part I: A general view on simultaneous topology and parameter optimization

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INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING 1998年第2期43卷 263-292页

作者： Kravanja, S Kravanja, Z Bedenik, BS Univ Maribor Fac Civil Engn SI-2000 Maribor Slovenia Univ Maribor Fac Chem & Chem Engn SI-2000 Maribor Slovenia

This paper presents the mixed-integer Non-linear programming (MINLP) optimization approach to structural synthesis. Non-linear continuous/discrete non-convex problems of structural synthesis are proposed to be solved by means of simultaneous topology, parameter and standard dimension optimization. Part I of this three-part series of papers contains a general view of the MINLP approach to simultaneous topology and continuous parameter optimization. The MINLP optimization approach is performed through three steps. The first one includes the generation of a mechanical superstructure of different topology alternatives, the second one involves the development of an MINLP model formulation and the last one consists of a solution for the formulated MINLP problem. Some MINLP methods are also presented. A Modified OA/ER algorithm is applied to solve the MINLP problem and a simple example of a multiple cantilever beam is given to demonstrate the steps of the proposed MINLP optimization approach. As simultaneous optimization, extended to include also standard dimensions, requires additional effort, the development of suitable strategies to carry out the optimization is further discussed in Part II. The modelling of MINLP superstructures and the topology and parameter optimization of roller and sliding hydraulic steel gate structures are shown in Part III of the paper. An example of the synthesis of an already erected roller gate, i.e. the Intake Gate of Aswan II in Egypt, is presented as a comparative design research work. (C) 1998 John Wiley & Sons, Ltd.

关键词： structural synthesis optimization topology optimization mixed-integer nonlinear programming MINLP modified OA/ER algorithm

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Optimal Plug-In Hybrid Electric Vehicle Design and Allocation for Minimum Life Cycle Cost, Petroleum Consumption, and Greenhouse Gas Emissions

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JOURNAL OF MECHANICAL DESIGN 2010年第9期132卷 091013-091013页

作者： Shiau, Ching-Shin Norman Kaushal, Nikhil Hendrickson, Chris T. Peterson, Scott B. Whitacre, Jay F. Michalek, Jeremy J. Carnegie Mellon Univ Pittsburgh PA 15213 USA

Plug-in hybrid electric vehicle (PHEV) technology has the potential to reduce operating cost, greenhouse gas (GHG) emissions, and petroleum consumption in the transportation sector. However, the net effects of PHEVs depend critically on vehicle design, battery technology, and charging frequency. To examine these implications, we develop an optimization model integrating vehicle physics simulation, battery degradation data, and U. S. driving data. The model identifies optimal vehicle designs and allocation of vehicles to drivers for minimum net life cycle cost, GHG emissions, and petroleum consumption under a range of scenarios. We compare conventional and hybrid electric vehicles (HEVs) to PHEVs with equivalent size and performance (similar to a Toyota Prius) under urban driving conditions. We find that while PHEVs with large battery packs minimize petroleum consumption, a mix of PHEVs with packs sized for similar to 25-50 miles of electric travel under the average U. S. grid mix (or similar to 35-60 miles under decarbonized grid scenarios) produces the greatest reduction in life cycle GHG emissions. Life cycle cost and GHG emissions are minimized using high battery swing and replacing batteries as needed, rather than designing underutilized capacity into the vehicle with corresponding production, weight, and cost implications. At 2008 average U. S. energy prices, Li-ion battery pack costs must fall below $590/kW h at a 5% discount rate or below $410/kW h at a 10% rate for PHEVs to be cost competitive with HEVs. Carbon allowance prices offer little leverage for improving cost competitiveness of PHEVs. PHEV life cycle costs must fall to within a few percent of HEVs in order to offer a cost-effective approach to GHG reduction. [DOI: 10.1115/1.4002194]

关键词： plug-in hybrid electric vehicle greenhouse gas emissions environmental policy design optimization mixed-integer nonlinear programming battery degradation vehicle design

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Freeway network design with exclusive lanes for automated vehicles under endogenous mobility demand

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2021年 133卷 103440-103440页

作者： Chakraborty, Shantanu Rey, David Levin, Michael W. Waller, S. Travis UNSW Sydney Sch Civil & Environm Engn Sydney NSW 2052 Australia Univ Minnesota Dept Civil Environm & Geoengn Minneapolis MN 55455 USA

Automated vehicles (AV) have the potential to provide cost-effective mobility options along with overall system-level benefits in terms of congestion and vehicular emissions. Additional resource allocation at the network level, such as AV-exclusive lanes, can further foster the usage of AVs rendering this mode of travel more attractive than legacy vehicles (LV). However, it is necessary to find the crucial locations in the network where providing these dedicated lanes would reap the maximum benefits. In this study, we propose an integrated mixed-integer programming framework for optimal AV-exclusive lane design on freeway networks which accounts for commuters' demand split among AVs and LVs via a logit model incorporating class-based utilities. We incorporate the link transmission model (LTM) as the underlying traffic flow model due to its computational efficiency for system optimum dynamic traffic assignment. The LTM is modified to integrate two vehicle classes namely, LVs and AVs with a lane-based approach. The presence of binary variables to represent lane design and the logit model for endogenous demand estimation results in a nonconvex mixed-integer nonlinear program formulation. We propose a Benders' decomposition approach to tackle this challenging optimization problem. Our approach iteratively explores possible lane designs in the Benders' master problem and, at each iteration, solves a sequence of system-optimum dynamic traffic assignment problems in an attempt to find fixed-points representative of logit-compatible demand splits. The proposed approach is implemented on three hypothetical freeway networks with single and multiple origins and destinations. Our numerical results reveal that the optimal lane design of freeway network is non-trivial while accounting for endogenous demand of each mode.

关键词： Benders decomposition mixed-integer nonlinear programming Freeway network design problem Endogenous demand Automated vehicles Exclusive lanes

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A discrete competitive facility location model with variable attractiveness

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2011年第9期62卷 1726-1741页

作者： Kucukaydin, H. Aras, N. Altinel, I. K. Bogazici Univ Dept Ind Engn TR-34342 Istanbul Turkey

We consider the discrete version of the competitive facility location problem in which new facilities have to be located by a new market entrant firm to compete against already existing facilities that may belong to one or more competitors. The demand is assumed to be aggregated at certain points in the plane and the new facilities can be located at predetermined candidate sites. We employ Huff's gravity-based rule in modelling the behaviour of the customers where the probability that customers at a demand point patronize a certain facility is proportional to the facility attractiveness and inversely proportional to the distance between the facility site and demand point. The objective of the firm is to determine the locations of the new facilities and their attractiveness levels so as to maximize the profit, which is calculated as the revenue from the customers less the fixed cost of opening the facilities and variable cost of setting their attractiveness levels. We formulate a mixed-integer nonlinear programming model for this problem and propose three methods for its solution: a Lagrangean heuristic, a branch-and-bound method with Lagrangean relaxation, and another branch-and-bound method with nonlinear programming relaxation. Computational results obtained on a set of randomly generated instances show that the last method outperforms the others in terms of accuracy and efficiency and can provide an optimal solution in a reasonable amount of time. Journal of the Operational Research Society (2011) 62, 1726-1741 doi:10.1057/jors.2010.136 Published online 8 September 2010

关键词： competitive facility location variable facility attractiveness mixed-integer nonlinear programming Lagrangean heuristic branch-and-bound

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Practical approximations and heuristic approaches for managing shiftable loads in the multi-period optimal power flow framework

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ELECTRIC POWER SYSTEMS RESEARCH 2021年 190卷 106864-106864页

作者： Avramidis, Iason-Iraklis Capitanescu, Florin Deconinck, Geert Luxembourg Inst Sci & Technol LIST Environm Res & Innovat ERIN Dept 41 Rue Brill L-4422 Belvalux Luxembourg Katholieke Univ Leuven ESAT Electa Kasteelpk Arenberg 10 B-3001 Leuven Belgium

The presence of smart buildings (SBs) is expected to grow manifold in the next decade, leading to both challenges and opportunities in managing increasingly "active" distribution systems. SBs are host to a number of devices and technologies capable of providing residential flexibility in order to maintain acceptable operating conditions within the distribution system. A prevalent class of devices with significant potential for flexibility provision are shiftable loads (SLs), which represent a large amount of potential SB-provided flexibility. However, the accurate modelling of SLs within the multi-period optimal power flow (MP-OPF) framework results in complex non-convex mixed-integer nonlinear programming (MINLP) problems. For realistic feeders, MINLP problems are intractable and hence require some heuristic form of circumvention. This work evaluates the limitations of the MINLP formulation and proposes new practical alternatives, namely a flexible NLP approximation and a multi-faceted heuristic algorithm. The proposed approaches are generic and applicable for any problem size or type of SL, while they are shown to work efficiently, both speed-wise and solution-quality-wise, outperforming state-of-the-art MINLP solvers. A simple scoring and ranking scheme is also proposed in order to make a direct comparison between the different approaches.

关键词： mixed-integer nonlinear programming multi-period optimal power flow residential flexibility shiftable loads smart buildings

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A green cogeneration microgrid composed of water-source heat pumps, a gravity energy storage, and a bio-fueled gas turbine: Design and techno-economic optimization

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SUSTAINABLE CITIES AND SOCIETY 2023年 95卷

作者： Taheri, Bahman Jabari, Farkhondeh Foroud, Asghar Akbari Semnan Univ Elect & Comp Engn Fac Semnan Iran Niroo Res Inst NRI Power Syst Operat & Planning Res Dept Tehran Iran

Rapid population and economic growth, democratization, decarbonization, deregulation, decentralization, and digitization (5D) of energy sector, transformation in lifestyle, as well as subsidized supply of petroleum products contribute to peak electricity utilization. In summer, limited power generation of thermal units have caused an urgent need to storages and renewable energy sources (RES). To mitigate pollutants emitted from generation facilities, nearly zero-energy buildings are created using RES and energy storage technologies. Hence, this paper presents a green combined cooling and power (CCP) generation system for industrial applications. A bio-fueled gas turbine cycle is selected to produce power for supplying both electrical demand and energy requirement of cooling loads. Some water-source heat pumps (WSHPs) are economically dispatched to procure cooling loads. A gravity energy storage (GES) is optimally scheduled to charge surplus electricity by pumping water and lifting a piston within a container and discharge it during peak periods by falling the piston and returning the high-pressure water into a hydro-turbine. A mixed-integer nonlinear programming problem is modeled to find the optimum operating point of the proposed plant minimizing the total energy cost. Simulations are conducted on a real industrial end-user with four WSHPs under two cases without and with cooperation of GES.

关键词： Water-source heat pumps (WSHPs) Gravity energy storage (GES) Bio-fueled Brayton cycle Economic dispatch mixed-integer nonlinear programming (MINLP)

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Combining Acquisition Planning with Inventory Management Under Uncertain Demand

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INFOR 2008年第2期46卷 129-135页

作者： Zhang, Guoqing Univ Windsor Dept Ind & Mfg Syst Engn Windsor ON N9B 3P4 Canada

We develop a new mixed integer nonlinear model to maximize a manufacturer's expected profit by combining strategic acquisition decisions with inventory management, where the manufacturer produces multiple products but faces uncertain demand for each product. This model also considers that suppliers provide competitive discount schemes. An iterative algorithm is developed to solve the problem. The preliminary computational results for a numerical example are reported.

关键词： Acquisition decision inventory management mixed-integer nonlinear programming

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