A simulation-optimization approach to integrate process design and planning decisions under technical and market uncertainties: A case from the chemical-pharmaceutical industry

Marques, Catarina M.; Moniz, Samuel; Sousa, Jorge Pinho de; Barbosa‐Póvoa, Ana Paula

doi:10.1016/j.compchemeng.2017.04.008

Cited by 23 publications

(19 citation statements)

References 58 publications

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“…The results show that with increasing demand, system costs also increase sharply. Then, a simulation–optimization model for strategic and operational decisions in the chemical-pharmaceutical industry is presented by Marques et al (2017) . For supply chain network simulation, Monte Carlo two-step simulation based on Bernoulli and Normal distributions were used.…”

Section: Literature Reviewmentioning

confidence: 99%

An integrated sustainable medical supply chain network during COVID-19

Goodarzian

Taleizadeh

Ghasemi

et al. 2021

Engineering Applications of Artificial Intelligence

189

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Nowadays, in the pharmaceutical industry, a growing concern with sustainability has become a strict consideration during the COVID-19 pandemic. There is a lack of good mathematical models in the field. In this research, a production–distribution–inventory–allocation–location problem in the sustainable medical supply chain network is designed to fill this gap. Also, the distribution of medicines related to COVID-19 patients and the periods of production and delivery of medicine according to the perishability of some medicines are considered. In the model, a multi-objective, multi-level, multi-product, and multi-period problem for a sustainable medical supply chain network is designed. Three hybrid meta-heuristic algorithms, namely, ant colony optimization, fish swarm algorithm, and firefly algorithm are suggested, hybridized with variable neighborhood search to solve the sustainable medical supply chain network model. Response surface method is used to tune the parameters since meta-heuristic algorithms are sensitive to input parameters. Six assessment metrics were used to assess the quality of the obtained Pareto frontier by the meta-heuristic algorithms on the considered problems. A real case study is used and empirical results indicate the superiority of the hybrid fish swarm algorithm with variable neighborhood search.

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Section: Literature Reviewmentioning

confidence: 99%

An integrated sustainable medical supply chain network during COVID-19

Goodarzian

Taleizadeh

Ghasemi

et al. 2021

Engineering Applications of Artificial Intelligence

189

View full text Add to dashboard Cite

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“…Here, Monte Carlo methods, as introduced in section A.2.1, represent a straightforward approach to include the optimization under uncertainty in SBO and allow a simple statistical quantification of the objective and the constraint values. This has been performed successfully for similar process design tasks [27,63]. This procedure is integrated with the presented solver in section 2.3 and will thus be used as such in the scope of this study.…”

Section: Optimization Under Uncertaintymentioning

confidence: 99%

Synergistic optimization framework for the process synthesis and design of biorefineries

Vollmer

Gernaey

et al. 2021

Front. Chem. Sci. Eng.

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“…Also, Gatica et al (2003) and Levis and Papageorgiou (2004) present a mathematical programming approach for the capacity planning problem with a focus on longterm planning and capacity investment decisions under uncertainty of clinical trials rather than scheduling. Marques et al (2017) present a simulation optimisation approach combining a MILP model and Monte Carlo simulation procedure to integrate process design and planning decisions under clinical trial and demand uncertainty for the pharmaceutical industry. Finally, Pollock et al (2013) developed a model focused on investigating the economic benefits of continuous perfusion culture and single-use technology for a monoclonal antibody (mAb).…”

Section: Literature Surveymentioning

confidence: 99%

Dynamic scheduling of multi-product continuous biopharmaceutical facilities: A hyper-heuristic framework

Oyebolu

Allmendinger

Farid

et al. 2019

Computers & Chemical Engineering

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The biopharmaceutical industry is increasingly interested in moving from batch to semi-continuous manufacturing processes. These continuous bioprocesses are more failure-prone and process failure is more consequential. In addition, the probability of failure is dependent on process run time which generally is determined independent of scheduling considerations. This work presents a discrete-event simulation of continuous bioprocesses in a scheduling environment. Dynamic scheduling policies are investigated to make operational decisions in a multiproduct manufacturing facility and react to process failure events and uncertain demand. First, different scheduling policies are adapted from the stochastic lot sizing literature and a novel look-ahead scheduling policy is proposed. Then, policy parameters (including process run time) are tuned using evolutionary algorithms. Our results demonstrate that the tuned policies perform much better than a policy that estimates policy parameters based on service level considerations and a policy based on a fixed cyclical sequence.

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A simulation-optimization approach to integrate process design and planning decisions under technical and market uncertainties: A case from the chemical-pharmaceutical industry

Abstract: A simulation-optimization approach to integrate process design and planning decisions under technical and market uncertainties: a case from the chemical-pharmaceutical industry.Computers and Chemical Engineering

Cited by 23 publications

References 58 publications

An integrated sustainable medical supply chain network during COVID-19

An integrated sustainable medical supply chain network during COVID-19

Synergistic optimization framework for the process synthesis and design of biorefineries

Dynamic scheduling of multi-product continuous biopharmaceutical facilities: A hyper-heuristic framework

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