طراحی شبکه زنجیره تامین مبتنی بر توزیع با یک معیار جدید
ترجمه نشده

طراحی شبکه زنجیره تامین مبتنی بر توزیع با یک معیار جدید

عنوان فارسی مقاله: بهینه سازی تصادفی طراحی شبکه زنجیره تامین مبتنی بر توزیع با یک معیار برگشت پذیری جدید
عنوان انگلیسی مقاله: Stochastic optimization of disruption-driven supply chain network design with a new resilience metric
مجله/کنفرانس: مجله بین المللی اقتصاد تولید - International Journal Of Production Economics
رشته های تحصیلی مرتبط: مهندسی صنایع
گرایش های تحصیلی مرتبط: بهینه سازی سیستم ها، برنامه ریزی و تحلیل سیستم ها، لجستیک و زنجیره تامین
کلمات کلیدی فارسی: معیارهای برگشت پذیری، طراحی شبکه زنجیره تأمین، برنامه ریزی تصادفی، برنامه عدد صحیح مختلط مخروطی
کلمات کلیدی انگلیسی: Resilience metrics، Supply chain network design، Stochastic programming، Conic mixed-integer program
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus - Master Journals List - JCR
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.ijpe.2020.107755
دانشگاه: Department of Industrial Engineering and Management, Shahrood University of Technology, Shahrood, Iran
صفحات مقاله انگلیسی: 33
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2020
ایمپکت فاکتور: 6/344 در سال 2019
شاخص H_index: 155 در سال 2020
شاخص SJR: 2/475 در سال 2019
شناسه ISSN: 0925-5273
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: E14749
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست مطالب (انگلیسی)

Abstract

1- Introduction

2- Literature review

3-  Optimization model

4- Sample average approximation method

5- Computational results

6- Managerial implications

7- Conclusion

References

بخشی از مقاله (انگلیسی)

Abstract

The supply chain (SC) ability to return quickly and effectively to its initial condition or even a more desirable state after a disruption is critically important, and is defined as SC resilience. Nevertheless, it has not been sufficiently quantified in the related literature. This study provides a new metric to quantify the SC resilience by using the stochastic programming. Our metric measures the expected value of the SC's cost increase due to a possible disruption event during its recovery period. Based on this measure, we propose a two-stage stochastic program for the supply chain network design under disruption events that optimizes location, allocation, inventory and order-size decisions. The stochastic program is formulated using quadratic conic optimization, and the sample average approximation (SAA) method is employed to handle the large number of disruption scenarios. A comprehensive computational study is carried out to highlight the applicability of the presented metric, the computational tractability of the stochastic program, and the performance of the SAA. Several key managerial and practical insights are gained based on the computational results. This new metric captures the time and cost of the SC's recovery after disruption events contrary to most of previous studies and main impacts of these two aspects on design decisions are highlighted. Further, it is shown computationally that the increase of SC's capacity is not a suitable strategy for designing resilient SCs in some business environments.

Introduction

Resilience is the ability of a system or firm to recover after a disruption event effectively and quickly, and in supply chain management, this ability is affected by SC’s design decisions and resources. As emphasized by Chopra & Sodhi (2014), Simchi-Levi et al. (2014), and, Ivanov et al. (2016) from 2000 to 2015, disruption events such as economic crises, earthquakes, terrorist attacks, and strikes occurred in SCs in more frequency and severity, and hence such an ability is crucial for SCs. The Business Continuity Institute reports that one-third of 408 surveyed companies experienced at least one SC disruption in 2017 and one-fifth of the corresponding disrupted companies stated cumulative losses of at least one million euros because of disruption events (Alcantara et al., 2017). Recently, quantitative models have received significant attention to optimize design and planning decisions in SCs with consideration of disruption events, and many companies such as IBM and Ford Motor used these methods (Simchi-Levi et al., 2014; Lu et al., 2015; Hosseini et al., 2019).