Portfolio design for home healthcare devices production using a new data-driven optimization methodology
SOFT COMPUTING, cilt.28, sa.2024, ss.5765-5784, 2024 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 28 Sayı: 2024
- Basım Tarihi: 2024
- Doi Numarası: 10.1007/s00500-023-09391-3
- Dergi Adı: SOFT COMPUTING
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, Compendex, Computer & Applied Sciences, INSPEC, zbMATH
- Sayfa Sayıları: ss.5765-5784
- Ankara Yıldırım Beyazıt Üniversitesi Adresli: Hayır
Özet
Covid-19 pandemic left scars on different industries, and now that we
are experiencing the post-pandemic situation, it is essential to plan
our next moves. When it comes to home healthcare devices (HHDs), they
can be useful in many aspects such as keeping the patients safe at the
home. Nowadays, the world is experiencing the post-pandemic situation,
then it is needed to adapt to new circumstances as it was done during
the pandemic. One of the factors that has been impacted by the new
situation is the demand for HHDs which had been increased tremendously
during the pandemic. The current study aims to forecast the so-called
demand by utilizing machine learning techniques to design a product
portfolio considering the shortage cost by benefiting from the Bayesian
Best–Worst method (BBWM). A mixed-integer non-linear mathematical model
is proposed to reach this goal which designs a portfolio of devices and
determines the number of needed machines to produce them. An HHD
manufacturing factory has been considered as a real-life case study to
approve the functionality of the proposed methodology. Several
businesses that have experienced post-pandemic demand fluctuations may
benefit from the findings of this study.