An MILP-MINLP decomposition method for the global optimization of a source based model of the multiperiod blending problem

Lotero, Irene; Trespalacios, Francisco; Grossmann, Ignacio E.; Papageorgiou, Dimitri J.; Cheon, Myun-Seok

doi:10.1016/j.compchemeng.2015.12.017

Cited by 51 publications

(32 citation statements)

References 36 publications

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“…Below we use a variant of the standard P-formulation. One could also use a Q-, PQ-, or source-based formulation [21]. We assume that player j's feasible region X j is given by the following set of constraints (where the subscript j is omitted for readability):…”

Section: Contributionsmentioning

confidence: 99%

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Pooling problems under perfect and imperfect competition

Papageorgiou¹,

Harwood²,

Trespalacios³

2021

Preprint

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We investigate pooling problems in which multiple players vie with one another to maximize individual profit in a non-cooperative competitive market. This competitive setting is interesting and worthy of study because the majority of prevailing process systems engineering models largely overlook the noncooperative strategies that exist in real-world markets. In this work, each player controls a processing network involving intermediate tanks (or pools) where raw materials are blended together before being further combined into final products. Each player then solves a pure or mixed-integer bilinear optimization problem whose profit is influenced by other players. We present several bilevel formulations and numerical results of a novel decomposition algorithm. We demonstrate that our provably optimal decomposition algorithm can handle some of the largest bilevel optimization problems with nonconvex lower-level problems ever considered in the literature.

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Section: Contributionsmentioning

confidence: 99%

“…The following proposition shows that if one were to choose any feasible solution xj to player j's binary knapsack problem (20) (or (21)) and concatenate all such solutions into a single decision vector x = (x 1 , . .…”

Section: Example With Binary Playersmentioning

confidence: 99%

Pooling problems under perfect and imperfect competition

Papageorgiou¹,

Harwood²,

Trespalacios³

2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, MILP models are frequently found in the literature when start-up or fixed transportation costs are considered. NLP models are also defined in the presence of material blending, such as oil blending problems (Lotero et al, 2016). To handle uncertainty, the inventory optimization problem that considers safety stocks has been examined .…”

Section: Supply Chain Planningmentioning

confidence: 99%

Perspectives in multilevel decision-making in the process industry

Brunaud¹,

Grossmann²

2017

Front. Eng

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Decisions in supply chains are hierarchically organized. Strategic decisions involve the long-term planning of the structure of the supply chain network. Tactical decisions are mid-term plans to allocate the production and distribution of materials, while operational decisions are related to the daily planning of the execution of manufacturing operations. These planning processes are conducted independently with minimal exchange of information between them. Achieving a better coordination between these processes allows companies to capture benefits that are currently out of their reach and improve the communication among their functional areas. We propose a network representation for the multilevel decision structure and analyze the components that are involved in finding integrated solutions that maximize the sum of the benefits of all nodes of the decision network. Although such task is very challenging, significant research progress has been made in each component of this structure. An overview of strategic models, mid-term planning models, and scheduling models is presented to address the solution of each node in the decision network. Coordination mechanisms for converging the integrated solutions are also analyzed, including solving large-scale models, multiobjective optimization, bi-level programming, and decomposition. We conclude by summarizing the challenges that hinder the full integration of multilevel decision making in supply chain management.

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“…Several papers have already studied the multiperiod mixing of crude oil or refined petroleum products in blending tanks and considering the issue of (non-)simultaneous input and output flows. [3][4][5][6] Other important contributions assumed that the blending process is carried out in tankless inline blenders, and focused on the simultaneous optimization of gasoline recipes and the scheduling of blending and distribution operations 7 developed a multi-level integrated approach to coordinate the short-term scheduling of blending operations with nonlinear recipe optimization. At the upper level, a nonlinear problem is solved to determine blending recipes and product volumes for the scheduling level.…”

Section: Previous Contributionsmentioning

confidence: 99%

A cost‐effective model for the gasoline blend optimization problem

Cerdá¹,

Pautasso²,

Cafaro³

2016

AIChE Journal

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Gasoline blending is a critical process with a significant impact on the total revenues of oil refineries. It consists of mixing several feedstocks coming from various upstream processes and small amounts of additives to make different blends with some specified quality properties. The major goal is to minimize operating costs by optimizing blend recipes, while meeting product demands on time and quality specifications. This work introduces a novel continuous-time mixed-integer linear programming (MILP) formulation based on floating time slots to simultaneously optimize blend recipes and the scheduling of blending and distribution operations. The model can handle non-identical blenders, multipurpose product tanks, sequence-dependent changeover costs, limited amounts of gasoline components, and multi-period scenarios. Because it features an integrality gap close to zero, the proposed MILP approach is able to find optimal solutions at much lower computational cost than previous contributions when applied to large gasoline blend problems.

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An MILP-MINLP decomposition method for the global optimization of a source based model of the multiperiod blending problem

Cited by 51 publications

References 36 publications

Pooling problems under perfect and imperfect competition

Pooling problems under perfect and imperfect competition

Perspectives in multilevel decision-making in the process industry

A cost‐effective model for the gasoline blend optimization problem

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