Fast genetic algorithm approaches to solving discrete-time mixed integer linear programming problems of capacity planning and scheduling of biopharmaceutical manufacture-Reference-Cited by-同舟云学术

Fast genetic algorithm approaches to solving discrete-time mixed integer linear programming problems of capacity planning and scheduling of biopharmaceutical manufacture

Published:2019-02 Issue: Volume:121 Page:212-223
ISSN:0098-1354
Container-title:Computers & Chemical Engineering
language:en
Short-container-title:Computers & Chemical Engineering

Author:

Jankauskas Karolis^ORCID,Papageorgiou Lazaros G.^ORCID,Farid Suzanne S.

Funder

EPSRC

Eli Lilly

Publisher

Elsevier BV

Subject

Computer Science Applications,General Chemical Engineering

Reference38 articles.

1. Efficient discovery of chromatography equipment sizing strategies for antibody purification processes using evolutionary computing;Allmendinger,2012

2. Optimal mutation rates in genetic search;Back,1993

3. Genetic planning using variable length chromosomes;Brie,2005

4. An algorithmic approach to parameter selection in machine learning using meta-optimization techniques;Camilleri;WSEAS Trans. Syst.,2014

5. Neural networks training using genetic algorithms;Chen,1998

Cited by 29 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Towards 3-fold sustainability in biopharmaceutical process development and product distribution;Systems and Control Transactions;2024-07-09

2. Improved scheduling model for a multistage biopharmaceutical process in a multiproduct facility;Chemical Engineering Research and Design;2024-07

3. Business intelligence to stimulate the commercial turnaround in the microcenter of an intermediate-sized city;Región Científica;2024-01-15

4. An Evolutionary Strategy with a Tailor-Made Repair Operator for Natural-Gas Management Under Supply Shortages;2024

5. Integrated capacity and production planning in the pharmaceutical supply chain: Framework and models;Computers & Chemical Engineering;2023-03