Computational strategies for improved MINLP algorithms

Su, Lijie; Tang, Lixin; Grossmann, Ignacio E.

doi:10.1016/j.compchemeng.2015.01.015

Cited by 28 publications

(15 citation statements)

References 33 publications

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“…In what could be applicable to the problem discussed here, recent publications have showcased advancements in MINLP techniques which, they say, can facilitate convergence in the optimisation of production planning and scheduling for large scale problems. Su et al (2015) have presented strategies such as multiple-generation cuts, hybrid methods and partial surrogate cuts for improving the e ciencies of the Outer Approximation and Generalized Benders Decomposition methods and Su et al (2016) have applied one of these techniques in a cracking production process. Other developments such as cutting plane methods (Eronen et al, 2015) and supporting hyperplane techniques (Westerlund et al, 2018) claim to produce easier convergence in nonsmooth, generalised convex formulations and demonstrate applicability to production and scheduling problems.…”

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confidence: 99%

An optimal control approach to scheduling and production in a process using decaying catalysts

Adloor

Pons

Vassiliadis

2020

Computers & Chemical Engineering

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This article presents a novel approach to optimise scheduling and production planning to meet seasonal demand in an industrial process using decaying catalysts, based on its formulation as a multistage mixed-integer optimal control problem (MSMIOCP). Unlike existing methodologies, the MSMIOCP formulation allows to solve this problem as a standard nonlinear optimisation problem without combinatorial optimisation methods, which can be advantageous in providing reliable, robust and e cient solutions. Using this formulation, four case studies of this problem, di↵ering in reaction or deactivation kinetics, are investigated. Two di↵erent solution implementations are used, each having their own relative advantages. The first implementation demonstrates a bang-bang behaviour for the linear scheduling controls, consistent with a theoretical analysis, but faces integration problems and does not always produce high quality solutions. The second implementation, while not demonstrating the bang-bang property, always produces high quality solutions and shows the advantages of the MSMIOCP formulation over existing methodologies.

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confidence: 99%

An optimal control approach to scheduling and production in a process using decaying catalysts

Adloor

Pons

Vassiliadis

2020

Computers & Chemical Engineering

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“…The maximal number of cutting planes or supporting hyperplanes added per iteration is controlled by the option Dual.HyperplaneCuts.MaxPerIteration. Utilizing the solution pool for generating multiple constraints has also been used in [67] for an OA-type algorithm and in [38] for the ESH algorithm.…”

Section: Generating Multiple Hyperplanes Per Iterationmentioning

confidence: 99%

The supporting hyperplane optimization toolkit for convex MINLP

2022

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In this paper, an open-source solver for mixed-integer nonlinear programming (MINLP) problems is presented. The Supporting Hyperplane Optimization Toolkit (SHOT) combines a dual strategy based on polyhedral outer approximations (POA) with primal heuristics. The POA is achieved by expressing the nonlinear feasible set of the MINLP problem with linearizations obtained with the extended supporting hyperplane (ESH) and extended cutting plane (ECP) algorithms. The dual strategy can be tightly integrated with the mixed-integer programming (MIP) subsolver in a so-called single-tree manner, i.e., only a single MIP optimization problem is solved, where the polyhedral linearizations are added as lazy constraints through callbacks in the MIP solver. This enables the MIP solver to reuse the branching tree in each iteration, in contrast to most other POA-based methods. SHOT is available as a COIN-OR open-source project, and it utilizes a flexible task-based structure making it easy to extend and modify. It is currently available in GAMS, and can be utilized in AMPL, Pyomo and JuMP as well through its ASL interface. The main functionality and solution strategies implemented in SHOT are described in this paper, and their impact on the performance are illustrated through numerical benchmarks on 406 convex MINLP problems from the MINLPLib problem library. Many of the features introduced in SHOT can be utilized in other POA-based solvers as well. To show the overall effectiveness of SHOT, it is also compared to other state-of-the-art solvers on the same benchmark set.

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“…Some examples of techniques capable of solving MINLP problems include generalised benders decomposition, branch and cut, outer approximation, and extended cutting plane. It should however be noted that these are limited to convex problems [19,20]. Some algorithms capable of solving these problems directly include the genetic algorithm (GA) and particle swarm optimisation which are stochastic in nature.…”

Section: Model Predictive Control (Mpc) Is An Advanced Process Contromentioning

confidence: 99%

Economic hybrid non-linear model predictive control of a dual circuit induced draft cooling water system

Muller

Craig

2017

Journal of Process Control

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Computational strategies for improved MINLP algorithms

Cited by 28 publications

References 33 publications

An optimal control approach to scheduling and production in a process using decaying catalysts

An optimal control approach to scheduling and production in a process using decaying catalysts

The supporting hyperplane optimization toolkit for convex MINLP

Economic hybrid non-linear model predictive control of a dual circuit induced draft cooling water system

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