The MINLP optimization approach to structural synthesis. Part III: synthesis of roller and sliding hydraulic steel gate structures

Kravanja, S.; Kravanja, Z.; Bedenik, S.

doi:10.1002/(sici)1097-0207(19980930)43:2<329::aid-nme414>3.3.co;2-z

Cited by 3 publications

(5 citation statements)

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“…These conditions are defined in a similar way as the standard design variables (sections). For more on the MINLP model formulation and how to handle the equations and variables, see Kravanja et al [13]- [15].…”

Section: Minlp Problem Formulationmentioning

confidence: 99%

Optimization of Steel and Timber Hall Structures

Kravanja

Žula

2022

WIT Transactions on the Built Environment

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The paper deals with the optimization of single-storey hall structures consisting of the same main frames to which steel purlins, façade rails and façade columns are connected. The frames can be steel or timber portal frames. While the steel frames are made of steel I-sections, the timber frames are made of glulam with rectangular cross-sections. The hall structure is optimized using mixed-integer nonlinear programming (MINLP), a combined continuous-discrete optimization technique. MINLP optimization is performed in three steps. It starts with defining the hall superstructure, modelling the optimization model of the structure, and solving the defined optimization problem. The superstructure includes all discrete alternatives of topologies, standard dimensions and material qualities competing for a feasible and optimal result. The optimization model includes continuous and discrete binary variables. The continuous variables represent dimensions, cross-sections, material grades, loads, etc., while the binary variables are used to optimize the topology of the structure and to select standard dimensions/profiles and material grades. The objective function of the material cost of the structure is subject to a system of (in)equality constraints of structural analysis and dimensioning. The dimensioning constraints are defined according to the Eurocode regulations. In order to solve the defined optimization problem, the modified outer-approximation/equality-relaxation (OA/ER) algorithm was used. A numerical example of MINLP optimization of a steel and timber frame hall structure is presented at the end of the article.

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Section: Minlp Problem Formulationmentioning

confidence: 99%

Optimization of Steel and Timber Hall Structures

Kravanja

Žula

2022

WIT Transactions on the Built Environment

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“…Further extension, the Modified OA/ER algorithm was afterwards introduced in order to solve non-convex problems, see Kravanja and Grossmann [12]. This algorithm was adapted and applied in structural optimization by Kravanja et al [13][14][15]. DOI: 10.22616/CE.2017.006…”

Section: Minmentioning

confidence: 99%

“…A number of problems in the area of structural optimization were solved with the presented MINLP approach: e.g. hydraulic steel gates for dams and hydro-power plants by Kravanja et al [15,16], steel and aluminum trusses by Šilih et al [18], timber truss structures by Šilih et al [19,20], multi-storey steel frame buildings by Klanšek et al [21], single-storey industrial steel buildings by Kravanja and Žula [22] and Kravanja et al [23], composite I-beam structures from concrete and steel by Kravanja and Šilih [24], Klanšek and Kravanja [25,26], Žula et al [27] and Kravanja et al [28] as well as timber-concrete composite floors by Jelušič and Kravanja [29]. A recent research work is also referred in the field of MINLP optimization of project schedules by Klanšek [30] and Cajzek and Klanšek [31].…”

Section: Minmentioning

confidence: 99%

Mixed-integer Non-linear Programming in Civil Engineering

Kravanja¹

2018

Civil Engineering

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The paper discusses the Mixed-Integer Non-linear Programming (MINLP) of problems in civil engineering. The MINLP enables the optimization of continuous parameters simultaneously with discrete alternatives. While continuous parameters are in structural optimization structural costs, masses, loads, stresses, resistances and deflections, as well as the discrete alternatives are in most cases defined as different topologies, standard sizes and materials. The continuous parameters are in the models expressed by continuous variables, whilst the discrete alternatives by discrete (0-1) variables. The MINLP optimization of a structure is usually a comprehensive and highly non-linear calculation process. The MINLP approach requires that a structure is generated as an MINLP superstructure including a number of structure alternatives. One of them is the optimal one. For each optimization problem/structure, an MINLP optimization model of the structure must be developed, where the cost or mass objective function of the structure is subjected to structural analysis and dimensioning equality/inequality constraints. The Modified Outer-Approximation/Equality-Relaxation algorithm and a threephase MINLP strategy are applied. Three numerical examples, i.e. the MINLP optimization of a cantilever beam, composite floor and high-pressure penstock are presented at the end of the paper.

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“…Due to the above reasons, the algorithms developed for the convex MINLP problems are often used to generate a local optimum for large‐scale strongly nonconvex MINLP problems in many PSE applications, especially when detailed unit operation models are employed 39 . For example, the OA algorithm 15 with some modifications 40–42 has been applied to optimization of both single distillation column 41,43,44 and distillation sequence 45 modeled by rigorous material balance, phase equilibrium, summation and enthalpy balance (MESH) equations, synthesis of heat exchanger network, 4 simultaneous process and heat exchanger network optimization, 46 and simultaneous topology and parameter optimization of a multiple cantilever beam 42 and roller and sliding hydraulic steel gate 47 . The logic‐based OA algorithm 22 has also been applied to solve the synthesis of distillation sequence and optimize the whole plant using MESH model for distillation 48–50 .…”

Section: Introductionmentioning

confidence: 99%

“…39 For example, the OA algorithm 15 with some modifications [40][41][42] has been applied to optimization of both single distillation column 41,43,44 and distillation sequence 45 modeled by rigorous material balance, phase equilibrium, summation and enthalpy balance (MESH) equations, synthesis of heat exchanger network, 4 simultaneous process and heat exchanger network optimization, 46 and simultaneous topology and parameter optimization of a multiple cantilever beam 42 and roller and sliding hydraulic steel gate. 47 The logic-based OA algorithm 22 has also been applied to solve the synthesis of distillation sequence and optimize the whole plant using MESH model for distillation. [48][49][50] The nonlinear B&B has been applied to integrated design and control, 51,52 simultaneous scheduling and control, 53 process synthesis incorporating MESH models for distillation columns.…”

mentioning

confidence: 99%

Homotopy continuation enhanced branch and bound algorithms for strongly nonconvex mixed‐integer nonlinear optimization

2022

AIChE Journal

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Large‐scale strongly nonlinear and nonconvex mixed‐integer nonlinear programming (MINLP) models frequently appear in optimization‐based process synthesis, integration, intensification, and process control. However, they are usually difficult to solve by existing algorithms within acceptable time. In this study, we propose two robust homotopy continuation enhanced branch and bound (HCBB) algorithms (denoted as HCBB‐FP and HCBB‐RB) where the homotopy continuation method is employed to gradually approach the optimum of the NLP subproblem at a node from the solution at its parent node. A variable step length is adapted to effectively balance feasibility and computational efficiency. The computational results from solving four existing process synthesis problems demonstrate that the proposed HCBB algorithms can find the same optimal solution from different initial points, while the existing MINLP algorithms fail or find much worse solutions. In addition, HCBB‐RB is superior to HCBB‐FP due to much lower computational effort required for the same locally optimal solution.

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The MINLP optimization approach to structural synthesis. Part III: synthesis of roller and sliding hydraulic steel gate structures

Cited by 3 publications

References 0 publications

Optimization of Steel and Timber Hall Structures

Optimization of Steel and Timber Hall Structures

Mixed-integer Non-linear Programming in Civil Engineering

Homotopy continuation enhanced branch and bound algorithms for strongly nonconvex mixed‐integer nonlinear optimization

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