An approximate method for the optimization of long-Horizon tank blending and scheduling operations

Beach, Benjamin; Hildebrand, Robert; Ellis, Kimberly P.; Lebreton, Baptiste

doi:10.1016/j.compchemeng.2020.106839

Cited by 7 publications

(3 citation statements)

References 58 publications

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“…The authors used the clustering solution as a pre-step to simplify operations and reduce the total number of vessel routes. Beach et al [260] proposed a discretization-based algorithm which can approximate non-convex mixed-integer quadratically constrained programming (MIQCP) as a MILP. To solve the tank blending and scheduling problem, the authors combined the algorithm with a rolling horizon approach, which is evaluated to be supportive of using industrial datasets.…”

Section: Refinery Schedulingmentioning

confidence: 99%

A Review: Machine Learning for Combinatorial Optimization Problems in Energy Areas

et al. 2022

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Combinatorial optimization problems (COPs) are a class of NP-hard problems with great practical significance. Traditional approaches for COPs suffer from high computational time and reliance on expert knowledge, and machine learning (ML) methods, as powerful tools have been used to overcome these problems. In this review, the COPs in energy areas with a series of modern ML approaches, i.e., the interdisciplinary areas of COPs, ML and energy areas, are mainly investigated. Recent works on solving COPs using ML are sorted out firstly by methods which include supervised learning (SL), deep learning (DL), reinforcement learning (RL) and recently proposed game theoretic methods, and then problems where the timeline of the improvements for some fundamental COPs is the layout. Practical applications of ML methods in the energy areas, including the petroleum supply chain, steel-making, electric power system and wind power, are summarized for the first time, and challenges in this field are analyzed.

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Section: Refinery Schedulingmentioning

confidence: 99%

A Review: Machine Learning for Combinatorial Optimization Problems in Energy Areas

et al. 2022

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“…The MBSP has a wide variety of engineering applications such as crude or refined oil scheduling, 4−6 mine planning, 7,8 wastewater treatment, 9,10 copper concentrate blending, 11,12 and specialty chemicals' manufacturing. 13 While an optimal solution of the MBSP can bring significant economic benefits, 14−16 state-of-the-art methods can only solve instances of modest size. Thus, improved techniques are sought to solve the MBSP of practical interest.…”

Section: Introductionmentioning

confidence: 99%

“…The objective of the MBSP is to find the least cost blending plan (or maximize profit), subject to various constraints such as raw material availability, operational rules, and product demand requirements. The MBSP has a wide variety of engineering applications such as crude or refined oil scheduling, − mine planning, , wastewater treatment, , copper concentrate blending, , and specialty chemicals’ manufacturing . While an optimal solution of the MBSP can bring significant economic benefits, − state-of-the-art methods can only solve instances of modest size.…”

Section: Introductionmentioning

confidence: 99%

Linear Programming-Based Preprocessing Algorithm and Tightening Constraints for Multiperiod Blend Scheduling Problems

Song,

Maravelias

2024

Ind. Eng. Chem. Res.

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The multiperiod blend scheduling problem has a wide variety of engineering applications and is typically formulated as a nonconvex mixed-integer nonlinear program (MINLP). Such an MINLP is challenging to solve due to a large number of bilinear terms and binary variables. One prevalent solution method is branch and bound, whose efficiency heavily relies on the tightness of the convex relaxation of the MINLP. In this article, we propose new constraints that can be used for tightening such convex relaxation. These constraints are derived from the physical information lost due to relaxation and require solving linear programs during preprocessing. Extensive numerical tests are executed to examine the effectiveness of the proposed methods. The results show that even though hundreds of linear programs may be solved during preprocessing, our new methods can significantly reduce the overall computational time, including both the preprocessing and MINLP solver solution time. Further implications are discussed.

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Enhancements of discretization approaches for non-convex mixed-integer quadratically constrained quadratic programming: Part I

Beach,

Burlacu,

Bärmann

et al. 2024

Comput Optim Appl

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We study mixed-integer programming (MIP) relaxation techniques for the solution of non-convex mixed-integer quadratically constrained quadratic programs (MIQCQPs). We present MIP relaxation methods for non-convex continuous variable products. In this paper, we consider MIP relaxations based on separable reformulation. The main focus is the introduction of the enhanced separable MIP relaxation for non-convex quadratic products of the form $$z=xy$$ z = x y , called hybrid separable (HybS). Additionally, we introduce a logarithmic MIP relaxation for univariate quadratic terms, called sawtooth relaxation, based on Beach (Beach in J Glob Optim 84:869–912, 2022). We combine the latter with HybS and existing separable reformulations to derive MIP relaxations of MIQCQPs. We provide a comprehensive theoretical analysis of these techniques, underlining the theoretical advantages of HybS compared to its predecessors. We perform a broad computational study to demonstrate the effectiveness of the enhanced MIP relaxation in terms of producing tight dual bounds for MIQCQPs. In Part II, we study MIP relaxations that extend the MIP relaxation normalized multiparametric disaggregation technique (NMDT) (Castro in J Glob Optim 64:765–784, 2015) and present a computational study which also includes the MIP relaxations from this work and compares them with a state-of-the-art of MIQCQP solvers.

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An approximate method for the optimization of long-Horizon tank blending and scheduling operations

Cited by 7 publications

References 58 publications

A Review: Machine Learning for Combinatorial Optimization Problems in Energy Areas

A Review: Machine Learning for Combinatorial Optimization Problems in Energy Areas

Linear Programming-Based Preprocessing Algorithm and Tightening Constraints for Multiperiod Blend Scheduling Problems

Enhancements of discretization approaches for non-convex mixed-integer quadratically constrained quadratic programming: Part I

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