Evidence map›Paper›PMID 34219916›Full record

ArticleIndustrial & engineering chemistry research2021

Multiobjective Optimization of Mixed-Integer Linear Programming Problems: A Multiparametric Optimization Approach.

Iosif Pappas, Styliani Avraamidou, Justin Katz, Baris Burnak, Burcu Beykal, Metin Türkay, Efstratios N Pistikopoulos

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Industrial & engineering chemistry research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Iosif PappasArtie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77843, U.S.A.
Styliani AvraamidouTexas A&M Energy Institute, Texas A&M University, College Station, TX 77843, U.S.A.
Justin KatzArtie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77843, U.S.A.
Baris BurnakArtie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77843, U.S.A.
Burcu BeykalTexas A&M Energy Institute, Texas A&M University, College Station, TX 77843, U.S.A.
Metin TürkayDepartment of Industrial Engineering, Koç University, Rumelifeneri Yolu, Sarıyer, 34450 Istanbul, Turkey.
Efstratios N PistikopoulosArtie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77843, U.S.A.

Funding

Single cell, multi-parametric high throughput platform to classify endocrine disruptor potential of mixturesP42ES027704 · NIEHS · TEXAS A&M UNIVERSITY · PI Thomas Joseph McDonald · 2017 to 2026
$21.2M
NIEHS NIH HHS P42 ES027704
6 · The paper itself

Abstract

Industrial process systems need to be optimized, simultaneously satisfying financial, quality and safety criteria. To meet all those potentially conflicting optimization objectives, multiobjective optimization formulations can be used to derive optimal trade-off solutions. In this work, we present a framework that provides the exact Pareto front of multiobjective mixed-integer linear optimization problems through multiparametric programming. The original multiobjective optimization program is reformulated through the well-established

Identifiers

PMID34219916
PMCPMC8248908

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.