Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
On Restricting Real-Valued Genotypes in Evolutionary Algorithms Kapitel lesen Erstes Kapitel lesen

2021 | OriginalPaper | Buchkapitel

A Multi-objective Multi-type Facility Location Problem for the Delivery of Personalised Medicine

verfasst von : Andreea Avramescu, Richard Allmendinger, Manuel López-Ibáñez

Erschienen in: Applications of Evolutionary Computation

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Advances in personalised medicine targeting specific sub-populations and individuals pose a challenge to the traditional pharmaceutical industry. With a higher level of personalisation, an already critical supply chain is facing additional demands added by the very sensitive nature of its products. Nevertheless, studies concerned with the efficient development and delivery of these products are scarce. Thus, this paper presents the case of personalised medicine and the challenges imposed by its mass delivery. We propose a multi-objective mathematical model for the location-allocation problem with two interdependent facility types in the case of personalised medicine products. We show its practical application through a cell and gene therapy case study. A multi-objective genetic algorithm with a novel population initialisation procedure is used as solution method.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Bayesian Networks for Mood Prediction Using Unobtrusive Ecological Momentary Assessments
Nächstes Kapitel RDE-OP: A Region-Based Differential Evolution Algorithm Incorporation Opposition-Based Learning for Optimising the Learning Process of Multi-layer Neural Networks
Fußnoten
1
A temperature-controlled supply chain that maintains a product viable through decreases in temperature.
 
2
Code for both MOO algorithms is available at doi:​ 10.​5281/​zenodo.​4495163.
 
Literatur
1.
Abou-El-Enein, M., Elsanhoury, A., Reinke, P.: Overcoming challenges facing advanced therapies in the EU market. Cell Stem Cell 19(3), 293–297 (2016)CrossRef
2.
Ahmadi-Javid, A., Seyedi, P., Syam, S.S.: A survey of healthcare facility location. Comput. Oper. Res. 79, 223–263 (2017)MathSciNetCrossRef
3.
Ardjmand, E., Weckman, G.R., Park, N., Taherkhani, P., Manjeet, S.: Applying genetic algorithm to a new location and routing model of hazardous materials. Int. J. Prod. Res. 53, 916–928 (2015)CrossRef
4.
5.
Bersenev, A., Kili, S.: Management of ‘out of specification’ commercial autologous CAR-T cell products. Cell Gene Therapy Insights 4(11), 1051–1058 (2018)CrossRef
6.
Bhattacharya, R., Bandyopadhyay, S.: Solving conflicting bi-objective facility location problem by NSGA II evolutionary algorithm. Int. J. Adv. Manuf. Technol. 51(1–4), 397–414 (2010)CrossRef
7.
Burkholder, T.W., Hill, K., Hynes, E.J.C.: Developing emergency care systems: a human rights-based approach. Bull. World Health Organ. 97(9), 612–619 (2019)CrossRef
8.
9.
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)CrossRef
10.
Doerner, K.F., Gutjahr, W.J., Nolz, P.C.: Multi-criteria location planning for public facilities in tsunami-prone coastal areas. OR Spectrum 31(3), 651–678 (2008)MathSciNetCrossRef
11.
Eder, C., Wild, C.: Technology forecast: advanced therapies in late clinical research, EMA approval or clinical application via hospital exemption. J. Market Access Health Policy 7(1), 1600939 (2019)CrossRef
12.
13.
Ermak, G.: Emerging medical technologies. World Scientific (2016)
14.
Farahani, R.Z., SteadieSeifi, M., Asgari, N.: Multiple criteria facility location problems: a survey. Appl. Math. Model. 34, 1689–1709 (2010)MathSciNetCrossRef
15.
16.
Hanley, P.J.: Fresh versus frozen: effects of Cryopreservation on CAR T Cells. Mol. Therapy 27(7), 1213–1214 (2019), ISSN 15250024
17.
Hiassat, A., Diabat, A., Rahwan, I.: A genetic algorithm approach for location-inventory-routing problem with perishable products. J. Manuf. Syst. 42, 93–103 (2017)CrossRef
18.
Jarosławski, S., Toumi, M.: Sipuleucel-t (Provenge) - Autopsy of an Innovative Paradigm Change in Cancer Treatment: Why a Single-Product Biotech Company Failed to Capitalize on its Breakthrough Invention. BioDrugs 29(5), 301–307 (2015)CrossRef
19.
Lander, E.S., Linton, L.M., Birren, B.: Initial sequencing and analysis of the human genome. Nature 409(6822), 860–921 (2001)CrossRef
20.
Lopes, A.G., Noel, R., Sinclair, A.: Cost analysis of vein-to-vein CAR T-cell therapy: automated manufacturing and supply chain. Cell Gene Therapy Insights 6(3), 487–510 (2020)CrossRef
21.
Medaglia, A.L., Villegas, J.G., Rodríguez-Coca, D.M.: Hybrid biobjective evolutionary algorithms for the design of a hospital waste management network. Journal of Heuristics 15(2), 153–176 (2009)CrossRef
22.
Nematollahi, M., Hosseni-Motlagh, S.M., Heydari, J.: Economic and social collaborative decision-making on visit interval and service level in a two-echelon pharmaceutical supply chain. J. Clean. Prod. 142, 3956–3969 (2017)CrossRef
23.
Panch, S.R., et al.: Effect of cryopreservation on autologous chimeric antigen receptor T-Cell characteristics. Mol. Therapy 27(7), 1275–1285 (2019), ISSN 15250024
24.
Papathanasiou, M.M., Stamatis, C., Lakelin, M., Farid, S., Titchener-Hooker, N., Shah, N.: Autologous CAR T-cell therapies supply chain: challenges and opportunities? Cancer Gene Ther. 27(10–11), 799–809 (2020)CrossRef
25.
Pirabán, A., Guerrero, W.J., Labadie, N.: Survey on blood supply chain management: models and methods. Comput. Oper. Res. 112, 104756 (2019)
26.
Quinn, C., Young, C., Thomas, J., Trusheim, M.: Estimating the Clinical Pipeline of Cell and Gene Therapies and Their Potential Economic Impact on the US Healthcare System. Value Health 22(6), 621–626 (2019)CrossRef
27.
Rahmani, A., Mirhassani, S.: A hybrid Firefly-Genetic Algorithm for the capacitated facility location problem. Inf. Sci. 283, 70–78 (2014)MathSciNetCrossRef
28.
Redekop, W.K., Mladsi, D.: The faces of personalized medicine: a framework for understanding its meaning and scope. Value Health 16(6), (2013)
29.
Alliance for Regenerative Medicine: Clinical Trials in Europe: Recent Trends in ATMP Development. Technical report ARM, October 2019
30.
Sexauer, A., Sandlund, J.T., Marcus, K.J.: Paediatric Leukemia and Lymphoma. In: Gunder’s & Tepper’s Clinical Radiation Oncology, pp. 1468–1476, Elsevier (2020)
31.
Sinclair, A., Monge, M.: Measuring Manufacturing Cost and Its Impact on Organizations. BioProcess International, p. 36–44 (2010)
32.
Steliarova-Fourcher, E., Colombet, M., Ries, L., Moreno, F., Dolya, A., Bray, F., et. al.: International incidence of childhood cancer, 2001–2010: a population based registry study. Lancet Oncol. 18(6), 719–731 (2017)
33.
Sun, W., Malvar, J., Sposto, R., Verma, A., et al.: Outcome of children with multiply relapsed B-cell acute lymphoblastic leukemia: a therapeutic advances in childhood leukemia & lymphoma study. Leukemia 32(11), 2316–2325 (2018)CrossRef
34.
Villegas, J.G., Palacios, F., Medaglia, A.L.: Solution methods for the bi-objective (cost-coverage) unconstrained facility location problem with an illustrative example. Ann. Oper. Res. 147(1), 109–141 (2006)MathSciNetCrossRef
35.
Wang, B., Liang, Y., Zheng, T., Yuan, M., Zhang, H.: Multi-objective site selection optimization of the gas-gathering station using nsga-ii. Process Saf. Environ. Prot. 119, 350–359 (2018)CrossRef
36.
Wang, X., Kong, Q., Papathanasiou, M.M., Shah, N.: Precision healthcare supply chain design through multi-objective stochastic programming. 13th International Symposium on Process Systems Engineering (PSE 2018) Computer Aided Chemical Engineering, p. 2137–2142 (2018)
37.
Weiss, D., Rydland, H.T., Øversveen, E., Jensen, M.R., Solhaug, S., Krokstad, S.: Innovative technologies and social inequalities in health: A scoping review of the literature. PLoS One 13(4), e0195447 (2018)
38.
Wilson, B., Nicholls, S.G.: The Human Genome Project, and recent advances in personalized genomics. Risk Management and Healthcare Policy, p. 9–20 (2015)
39.
Yang, L., Jones, B.F., Yang, S.H.: A fuzzy multi-objective programming for optimization of fire station locations through genetic algorithms. Eur. J. Oper. Res. 181(2), 903–915 (2007)CrossRef
40.
Metadaten
Titel
A Multi-objective Multi-type Facility Location Problem for the Delivery of Personalised Medicine
verfasst von
Andreea Avramescu
Richard Allmendinger
Manuel López-Ibáñez
Copyright-Jahr
2021
Buch
Applications of Evolutionary Computation

Print ISBN: 978-3-030-72698-0

Electronic ISBN: 978-3-030-72699-7

Copyright-Jahr: 2021

DOI
https://doi.org/10.1007/978-3-030-72699-7_25

Premium Partner

    Bildnachweise