Production and maintenance planning optimisation in biopharmaceutical processes under performance decay using a continuous-time formulation: A multi-objective approach

Vieira, Miguel; Pinto‐Varela, Tânia; Barbosa‐Póvoa, Ana Paula

doi:10.1016/j.compchemeng.2017.03.008

Cited by 26 publications

(26 citation statements)

References 39 publications

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“…AUGMECON has been employed in various applications and systems, including supply chain management (Torabi et al 2013;Bootaki et al 2014Bootaki et al , 2016Canales-Bustos et al 2017;Musavi and Bozorgi-Amiri 2017;Ravat et al 2017;Vieira et al 2017;Sazvar et al 2018;Ehrenstein et al 2019;Oiu et al 2019;Shekarjan et al 2019;Xin et al 2019), energy planning (Hombach and Walther 2015;Tartibu et al 2015;Arancibia et al 2016;Mohammadkhani et al 2018;Rabbani et al 2018;Sedighizadeh et al 2018;Razm et al 2019), waste management Mavrotas et al 2015a, b;Inghels et al 2016), portfolio analysis (Xidonas et al 2010(Xidonas et al , 2011 Mavrotas and Florios (2013) further extended this algorithm in AUGMECON 2, by introducing a bypass coefficient as well as a type of lexicographic optimisation to all objective functions, the order of which was insignificant in AUGMECON:…”

Section: A Brief Overview Of the Augmented ε-Constraint Methodsmentioning

confidence: 99%

A robust augmented ε-constraint method (AUGMECON-R) for finding exact solutions of multi-objective linear programming problems

et al. 2020

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Systems can be unstructured, uncertain and complex, and their optimisation often requires operational research techniques. In this study, we introduce AUGMECON-R, a robust variant of the augmented ε-constraint algorithm, for solving multiobjective linear programming problems, by drawing from the weaknesses of AUG-MECON 2, one of the most widely used improvements of the ε-constraint method. These weaknesses can be summarised in the ineffective handling of the true nadir points of the objective functions and, most notably, in the significant amount of time required to apply it as more objective functions are added to a problem. We subsequently apply AUGMECON-R in comparison with its predecessor, in both a set of reference problems from the literature and a series of significantly more complex problems of four to six objective functions. Our findings suggest that the proposed method greatly outperforms its predecessor, by solving significantly less models in emphatically less time and allowing easy and timely solution of hard or practically impossible, in terms of time and processing requirements, problems of numerous objective functions. AUGMECON-R, furthermore, solves the limitation of unknown nadir points, by using very low or zero-value lower bounds without surging the time and resources required.

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Section: A Brief Overview Of the Augmented ε-Constraint Methodsmentioning

confidence: 99%

A robust augmented ε-constraint method (AUGMECON-R) for finding exact solutions of multi-objective linear programming problems

et al. 2020

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“…The chosen framework to model the problem is the Resource-Task Network (RTN) framework, which was developed by Ref. 23. Its capability to handle large problem sizes as they are present in industrial-sized problem was proven by Ref.…”

Section: Mathematical Modelingmentioning

confidence: 99%

Optimal Maintenance for Degrading Assets in the Context of Asset Fleets-A Case Study

Spüntrup

Ave

Imsland

et al. 2020

Front. Appl. Math. Stat.

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Decision-making for maintenance of engineering assets is a common challenge in the process industry due to ongoing degradation. With an increasing company-size, this problem becomes more complex from an operational and computational point of view. This paper introduces a case study to the academic community that represents the problem of optimal decision-making in the context of large asset fleets. The case study poses a large fleet of offshore compressors for gas production with a specific network structure. Two exemplary discrete-time mixed integer linear programming models following the Resource Task Network framework are presented. They address asset deterioration due to effects such as fouling by suggesting specific maintenance actions as a set of different countermeasures. Novel enumerator formulations are a computationally efficient and extendable way to model the various degradation types. Results show the benefit of optimal maintenance in the application to asset fleets. The decision-support that is delivered by the scheduling and planning approach helps to determine which maintenance type should be conducted and at what time. The paper demonstrates the benefits of optimal (long-term) schedules for maintenance, but indicate at the same time the need for efficient algorithms in the context of large asset fleets, in contrast to common industrial case studies that are rather small-scale.

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“…While the condition of the unit depends on the cumulative amount processed, it does not seem to affect the processing rate. Vieira et al 28 studied the optimal planning of a continuous biopharmaceutical process considering decaying production yield.…”

Section: Introductionmentioning

confidence: 99%

Industrial Demand Side Management of a Steel Plant Considering Alternative Power Modes and Electrode Replacement

Castro

Ave

Engell

et al. 2020

Ind. Eng. Chem. Res.

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As a major energy consumer, steel plants can help stabilize the power grid by shifting production from periods with high demand. Electric arc furnaces can be operated at different power levels, affecting the energy efficiency, the duration of melting tasks, and the rate of electrode degradation, which has previously been neglected. We thus propose a new mixed-integer linear programming (MILP) formulation for optimal scheduling under time-of-use electricity pricing that captures the tradeoffs involved. It relies on the resource-task network (RTN) for modeling processing tasks with variable electrode mass depletion and replacement tasks that regenerate the mass. Results for an industrial case study show that the high-power mode, which allows for faster execution and to fit more tasks in low-price periods but is the least energy-efficient and consumes the largest mass of electrode, is mostly avoided. It indicates that electrode replacement plays an important role in total cost minimization.

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Production and maintenance planning optimisation in biopharmaceutical processes under performance decay using a continuous-time formulation: A multi-objective approach

Cited by 26 publications

References 39 publications

A robust augmented ε-constraint method (AUGMECON-R) for finding exact solutions of multi-objective linear programming problems

A robust augmented ε-constraint method (AUGMECON-R) for finding exact solutions of multi-objective linear programming problems

Optimal Maintenance for Degrading Assets in the Context of Asset Fleets-A Case Study

Industrial Demand Side Management of a Steel Plant Considering Alternative Power Modes and Electrode Replacement

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