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Generalized Benders Decomposition to Reoptimize Water Production and Distribution Operations in a Real Water Supply Network

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JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT 2016年第2期142卷 4015059-1-4015059-11页

作者： Verleye, Derek Aghezzaf, El-Houssaine Univ Ghent Dept Ind Syst Engn & Prod Design Fac Engn & Architecture Technol Pk 903 B-9052 Zwijnaarde Belgium

Water supply networks are designed to effectively and efficiently supply water to some originally targeted agglomerations. However, the dynamics over time of water-demand patterns and electricity cost schemes may render these designs inefficient and expensive to operate. This paper considers the challenging problem of optimizing water production and distribution operations in one of the largest water supply networks operating in Flanders (Belgium) while accounting for the water-demand and electricity cost dynamics. Optimizing operations in a real water supply network is a difficult task as it involves many constraints. In addition to the complex hydraulic nonlinear equality constraints stemming from friction losses and pump curves, specific constraints for accurate modeling of storage buffers must be taken into account. These constraints require additional binary variables to model free inflow or possible reinjection of water to the network. The resulting optimization problem is thus a nonconvex mixed-integer and nonlinear mathematical problem that is computationally expensive to solve. A particularly appealing method for solving such optimization problems is Generalized Benders Decomposition (GBD). This paper extends this method to the water supply network optimization problem mentioned previously. It is experimentally shown that the approach is successful through careful selection of the complicating variables and values for the penalty term. Results of the experiments carried out on two network instances show that the carefully fine-tuned model allows convergence to near-optimal solutions. Compared to state-of-the-art solvers, the proposed approach proved to be competitive on the tested networks in terms of solution quality and computational time. (C) 2015 American Society of Civil Engineers.

关键词： Large water supply networks mixed-integer nonlinear programming Benders decomposition

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Additive manufacturing capacity allocation problem over a network

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IISE TRANSACTIONS 2023年第8期55卷 807-820页

作者： Cokyasar, Taner Jin, Mingzhou Univ Tennessee Dept Ind & Syst Engn Knoxville TN 37996 USA Argonne Natl Lab Transportat & Power Syst Div Lemont IL USA Tarsus Univ Dept Ind Engn Takbas Mersin Turkey

The use of Additive Manufacturing (AM) for low demand volumes, such as spare parts, has recently attracted considerable attention from researchers and practitioners. This study defines the AM Capacity Allocation Problem (AMCAP) to design an AM supply network and choose between printing upon demand and sourcing through an alternative option for each part in a given set. A mixed-integer nonlinear program was developed to minimize the production, transportation, alternative sourcing, and lead time costs. We developed a cut generation algorithm to find optimal solutions in finite iterations by exploring the convexity of the nonlinear waiting time for AM products at each AM facility. Numerical experiments show the effectiveness of the proposed algorithm for the AMCAP. A case study was conducted to demonstrate that the optimal AM deployment can save almost 20% of costs over situations that do not use any AM. The case also shows that AM can realize its maximum benefits when it works in conjunction with an alternative option, e.g., inventory holding, and its capacity is strategically deployed. Since AM is a new technology and is rapidly evolving, this study includes a sensitivity analysis to see the effects of improved AM technology features, such as machine cost and build speed. When the build speed increases, the total cost decreases quickly, but the number of AM machines will increase first then decrease later when more parts are assigned to the AM option.

关键词： Additive manufacturing capacity planning network optimization mixed-integer nonlinear programming

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Adjustable Robust Optimization for the Multi-period Planning Operations of an Integrated Refinery-Petrochemical Site under Uncertainty

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COMPUTERS & CHEMICAL ENGINEERING 2022年 160卷 107703-107703页

作者： Zhang, Lifeng Yuan, Zhihong Chen, Bingzhen Tsinghua Univ Dept Chem Engn State Key Lab Chem Engn Beijing 100084 Peoples R China Tsinghua Univ Dept Chem Engn Beijing 100084 Peoples R China

This paper concentrates on the formulation of a large-scale nonconvex mixed-integer nonlinear programming model and the application of robust optimization for the multi-period operational planning of realworld integrated refinery-petrochemical site in China under uncertain product demands and crude oil price. To avoid excessive conservativeness resulting from classical static robust optimization, an adjustable robust counterpart incorporating resource decisions via an affinely adjustable linear decision rule is first derived. On the basis of a proposed polyhedral dynamic uncertainty set that mimics the dynamic behavior of the product demand over time, an adjustable robust counterpart with a dynamic uncertainty set is further formulated. Classical static robust optimization, adjustable robust optimization, and adjustable robust optimization with dynamic uncertainty sets are systematically compared for case studies. The results clearly illustrate the advantages of the affinely adjustable robust optimization with a dynamic uncertainty set over the classic robust optimization in decision making.

关键词： mixed-integer nonlinear programming Adjustable robust optimization Dynamic uncertainty set Planning operations Refinery-petrochemical

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Optimal voyage scheduling of all-electric ships considering underwater radiated noise

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2023年第1期148卷

作者： Khatami, Roohallah Chen, Bo Chen, Yu Christine Univ British Columbia Dept Elect & Comp Engn Vancouver BC Canada

Underwater radiated noise (URN) emanating from ships can adversely impact the life functions of certain marine mammals that rely on sound to navigate, communicate, and locate prey. This paper formulates an optimal voyage scheduling problem to mitigate the impact of URN on sensitive marine species by choosing amongst different possible paths and specifying the cruising speed along the selected path. We focus on all-electric ships (AESs) owing to their greater flexibility for speed regulation by coordinating an integrated power system. The proposed optimization model schedules generators and energy storage devices toward minimizing the operation cost while satisfying constraints pertinent to URN levels and atmospheric greenhouse gas (GHG) emissions, the electric power network and operational limits, and expected voyage timelines, culminating in a mixed-integer nonlinear programming problem. To promote com-putational tractability, we approximate the nonlinear relationships for URN, propulsion load, and fuel consumption with piecewise linear functions. This leads to a mixed-integer second -order cone programming problem, which enables convergence to the global optimum and computationally efficient solutions. We illustrate the effectiveness of the proposed model in curbing URN levels and GHG emissions with numerical case studies involving an 18-node ship test system.

关键词： All-electric ship mixed-integer nonlinear programming Optimal voyage scheduling Underwater radiated noise

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Integrated supply chain scheduling of procurement, production, and distribution under spillover effects

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COMPUTERS & OPERATIONS RESEARCH 2021年 126卷 105105-105105页

作者： Wang, Gang Univ Massachusetts Charlton Coll Business Dept Decis & Informat Sci 285 Old Westport Rd N Dartmouth MA 02747 USA

This paper considers an integrated scheduling problem in an e-commerce supply chain. The supply chain consists of heterogeneous suppliers, consumer-goods manufacturers that offer online channels, and a network of retailers. Suppliers provide raw materials or semi-finished products to manufacturers. Then, manufacturers produce and deliver end items to retailers. The retailers require that the manufac-turers maintain a given fill rate, and, otherwise, a penalty will apply. Missing the fill rate can not only result in penalties, but also retailers' switching, operational issues, and loss of image, and market value. Therefore, the total penalty costs are nonlinear with retailers' unsatisfied quantities and the fill rate, which exhibits spillover effects. The problem is to select a subset of suppliers, assign retailers' orders, and identify a production schedule with the manufacturers while minimizing the total costs that includes shipping and penalty. In this paper, we formulate a mixed-integer nonlinear program and develop a hybrid particle swarm optimization algorithm that can find a suitable solution within a reasonable time. The algorithm incorporates augmented Lagrangian relaxation and particle swarm optimization. We then perform computational testing on randomly generated cases and evaluate the performance of the proposed algorithm. Our numerical experiments show that the proposed algorithm is robust and efficient. Moreover, we provide a real case study, which demonstrates that the proposed model can lead to a substantial reduction of both the total and penalty costs. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Integrated scheduling Spillover effect mixed-integer nonlinear programming Augmented Lagrangian relaxation Particle swarm optimization

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Comprehensive energy footprint benchmarking of commercial electrified powertrains

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APPLIED ENERGY 2023年第1期345卷

作者： Anwar, Hamza Vishwanath, Aashrith Ahmed, Qadeer Chunodkar, Apurva Ohio State Univ Ctr Automot Res 930 Kinnear Rd Columbus OH 43212 USA Cummins Tech Ctr 1900 McKinley Ave Columbus IN 47201 USA

This work presents a comprehensive energy management framework applied to electrified commercial powertrains with in-depth energy footprint analysis. We use our recently proposed algorithm, validated real -world powertrain system models, and Pareto-optimal analysis to optimize fuel consumption and harmful pollutant emissions. The approach involves dynamic optimization of 13 states and 4 control levers capturing complex subsystem interactions in parallel P2 and series range-extender commercial medium-duty electrified trucks. These subsystems exhibit vehicular (eco-driving), thermal, electrical, and mechanical dynamics at different time scales, and contain kinematic and combinatorial constraints, integer-and real-valued variables, interpolated look-up tables, and data maps. A Pareto-optimal solution is found by carefully optimizing fuel and NOx emissions to understand the energy footprint. Presented results exhibit rich powertrain behavior to unearth up to 6% improvement in energy demand, 2% in fuel economy and 18% in pollutant NOx reduction when compared to Dynamic programming baseline from a coarsely modeled powertrain system. Furthermore, internal energy flow in the powertrains is analyzed to benchmark the optimal energy consumption and realize the multi-objective trade-off. Finally, a transient study problem is presented comparing cold versus warm after-treatment start conditions. It optimizes engine switching, eco-driving, and powersplit controls revealing adequate dynamic response and complex system interactions.

关键词： Optimal energy management Eco-driving Powertrain control mixed-integer nonlinear programming Fuel and NOx emissions minimization

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Decarbonizing existing coal-fired power stations considering endogenous technology learning: A Turkish case study

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JOURNAL OF CLEANER PRODUCTION 2020年 261卷 121100-121100页

作者： Aliabadi, Danial Esmaeili Sabanci Univ Fac Engn & Nat Sci Istanbul Turkey Sabanci Univ Syst Modelling & Decis Analyt Lab Istanbul Turkey Istanbul Int Ctr Energy & Climate IICEC Istanbul Turkey

Despite environmental threats, coal is expected to remain a remarkable energy source as the share of intermittent renewable sources in the energy mix grows;hence, managing pollutants such as carbon dioxide (CO2) is critical while pushing for clean alternatives. Carbon capture, utilization, and storage (CCUS) is a set of methods that removes CO2 from emissions and stores it safely for the long-term. As it stands now, CCUS is prohibitively expensive;however, learning-by-doing is a recognized phenomenon that assists novel technologies to become more affordable as employed continuously. Thus, for accurate long-term planning, this study investigates the impact of early adopters on CCUS market diffusion considering endogenous technology learning (ETL). A mixed-integer nonlinear programming model (MINLP) is developed which determines the optimal capacity and deployment time of post-combustion carbon capture units and the optimal sourceesink matching in the CO2 supply chain, taking into account techno-economic constraints and regulations. The MINLP model is transformed into a series of mixed-integer linear programming models using a Stackelberg game approach and solved with a diagonalization method. Equilibrium solutions of the proposed model were obtained for independent and coordinated actions of players. The proposed model is applied to the Turkish energy market with multiple CO2 sources and sinks, where a cap-and-trade program is presumed to be in place. While the study demonstrates the interplay between CCUS and ETL in Turkey, the proposed model is universal, and it can be applied to other countries if the inputs are available. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Decarbonization Post-combustion Endogenous technology learning Source-sink matching mixed-integer nonlinear programming

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Efficient iterative optimization to real-time train regulation in urban rail transit networks combined with Benders decomposition method

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COMPUTERS & OPERATIONS RESEARCH 2024年 162卷

作者： Yuan, Yin Li, Shukai Yang, Lixing Gao, Ziyou Beijing Jiaotong Univ Sch Syst Sci Beijing 100044 Peoples R China

Considering the rapid expansion of urban rail transit networks and increasing transfer demands, this paper investigates the design of real-time train regulation strategies for urban rail networks under disturbances. Specifically, a mixed-integer nonlinear programming (MINLP) model is constructed in a rolling horizon (RH) framework to reduce train deviations and passenger waiting times, which involves complex coupling relationships among network-wide non-transfer passengers, transfer passengers and train traffics of different lines. To effectively address the model complexity and adapt to the real-time nature of this issue, we proposed an efficient iterative optimization (IO) approach to split the original problem into a smaller-scale regulation issue and a computationally cheap passenger loading procedure, which can efficiently treat the introduced nonconvexity of the exact modelling scheme. Besides, tailored to the mixed-integer property of the model, the generalized Benders decomposition (GBD) technique is incorporated into our algorithm. Furthermore, to verify the effectiveness of our method, we describe the detailed implementation of a series of numerical experiments on a small-scale and a real-world case. Computational results show that our regulation method can effectively contribute to the reliability of train systems, improving operational efficiency and service level. Besides, it performs promising computational efficiency in dealing with large-scale real-world cases, applicable for real-time applications.

关键词： Timetabling Urban rail transit networks Train regulation Benders decomposition mixed-integer nonlinear programming

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Optimal computer-aided molecular design of ionic liquid mixtures for post-combustion carbon dioxide capture

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COMPUTERS & CHEMICAL ENGINEERING 2022年 157卷 107622-107622页

作者： Silva-Beard, Andrea Flores-Tlacuahuac, Antonio Rivera-Toledo, Martin Univ Iberoamer Ciudad Mexico Dept Ingn Quim Ind & Alimentos Prolongac Paseo Reforma 880 Ciudad De Mexico 01219 Mexico Tecnol Monterrey Escuela Ingn & Ciencias Campus MonterreyAve Eugenio Garza Sada 2501 Monterrey 64849 NL Mexico

This work addresses the post-combustion carbon dioxide capture capabilities of optimally designed mixtures of ionic liquids, as well as pure ionic liquids. We postulate that ionic liquid mixtures can display better absorption rates and processing features when compared with pure ionic liquids. The computer-aided molecular design of the ionic liquids mixture was posed as a mixed-integer nonlinear programming problem to determine both the chemical structure and the amount of each ionic liquid in the mixture. Also, multiobjective optimization was performed to determine optimal operating conditions, specifically pressure and ionic liquids mixture feed stream, for an absorption separation process with various optimal mixtures. Results show that the anion [Tf2N] with the chain -(CH)(2)(CH2)(5)(CH3)(3) was present both in the pure ionic liquid case and in the mixtures of these compounds. Results also indicate that, even though the amount of carbon dioxide absorbed by pure ionic liquid is slightly higher than the absorbed by ionic liquids mixture, there are important operational and environmental advantages of using such type of mixtures since they have milder physical properties than the pure ionic liquid. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： Multiobjective optimization Ionic liquid mixture Carbon dioxide capture mixed-integer nonlinear programming Computer-aided molecular design

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

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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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