طراحی شبکه زنجیره تامین خون مورد اطمینان با امکان گسیختگی
ترجمه نشده

طراحی شبکه زنجیره تامین خون مورد اطمینان با امکان گسیختگی

عنوان فارسی مقاله: طراحی شبکه زنجیره تامین خون مورد اطمینان با امکان گسیختگی: یک برنامه کاربردی دنیای واقعی
عنوان انگلیسی مقاله: Reliable blood supply chain network design with facility disruption: A real-world application
مجله/کنفرانس: برنامه های کاربردی مهندسی هوش مصنوعی - Engineering Applications Of Artificial Intelligence
رشته های تحصیلی مرتبط: مهندسی صنایع
گرایش های تحصیلی مرتبط: لجستیک و زنجیره تامین، برنامه ریزی و تحلیل سیستم ها، مهندسی سیستم های سلامت
کلمات کلیدی فارسی: تدارکات، شبکه زنجیره تامین خون، تجزیه و تحلیل تخصیص مکانی، فاجعه و خطرات گسیختگی، بهینه سازی استوار، تجزیه و تحلیل عملکرد
کلمات کلیدی انگلیسی: Logistics، Blood supply chain network، Location–allocation analysis، Disruption risks and disaster، Robust optimization، Performance analysis
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus - Master Journals List - JCR
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.engappai.2020.103493
دانشگاه: School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
صفحات مقاله انگلیسی: 18
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2020
ایمپکت فاکتور: 4/530 در سال 2019
شاخص H_index: 86 در سال 2020
شاخص SJR: 0/881 در سال 2019
شناسه ISSN: 0952-1976
شاخص Quartile (چارک): Q1 در سال 2019
فرمت مقاله انگلیسی: PDF
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: بله
آیا این مقاله مدل مفهومی دارد: دارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: دارد
کد محصول: E14750
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست مطالب (انگلیسی)

Abstract

1- Introduction

2- Literature review

3- Problem definition and the proposed model

4- Solution methodology

5- Experimental results

6- Managerial insight

7- Conclusion

References

بخشی از مقاله (انگلیسی)

Abstract

The blood supply of hospitals in disasters is a crucial issue in supply chain management. In this paper, a dynamic robust location–allocation model is presented for designing a blood supply chain network under facility disruption risks and uncertainty in a disaster situation. A scenario-based robust approach is adapted to the model to tackle the inherent uncertainty of the problem, such as a great deal of a periodic variation in demands and facilities disruptions. It is considered that the effect of disruption in facilities depends on the initial investment level for opening them, which are affected by the allocated budget. The usage of the model is implemented by a real-world case example that addresses the demand and disruption probability as uncertain parameters. For large-scale problems, two meta-heuristic algorithms, namely the self-adaptive imperialist competitive algorithm and invasive weed optimization, are presented to solve the model. Furthermore, several numerical examples of managerial insights are evaluated.

Introduction

Supply chain management (SCM) is often described as a procedure of planning, implementation, and control of supply chain operations based on efficient practices (Melo et al., 2009). The supply chain network design (SCND) has played a dominant role in the performance of the supply chain (SC). It copes with so many prospects of the SC such as information, location of facilities and allocation of material. The SCND is considered as a significant issue in strategic and operational decisions in the SCM scope (Devika et al., 2014; Amin et al., 2017; Fu and Fu, 2015). Blood supply management and its products are vital issues for humankind. Blood is not a regular commodity since its demand is relatively random, and efficient coordination between supply and demand has not been resolved in various researches yet (Beliën and Forcé, 2012). Human blood is a rare and vital source that is produced only by human beings, and since there is currently no other product that can produce blood and also its uncertainty supply and demand side, keeping an adequate supply level is very important to fulfill demands (Duan and Liao, 2014).