نوع مقاله : مقاله پژوهشی
نویسندگان
گروه مهندسی لجستیک و زنجیره تامین، دانشکده مهندسی صنایع، دانشگاه علم و صنعت، تهران، ایران.
چکیده
هدف: با توجه به افزایش پیچیدگیهای ناشی از عدمقطعیت و تاثیرات آن در شبکههای زنجیره تامین، بسیاری از محققین به استفاده از رویکردهای تقابلی با عدمقطعیت دادهها روی آوردهاند. به علاوه، وقوع هرگونه اختلال در شبکههای توزیع، می‌تواند خسارتهای جبرانناپذیری به بار بیاورد؛ بنابراین اتخاذ استراتژیهای مناسب برای بالا بردن سطح تابآوری شبکه زنجیره تامین به دنبال کاهش آثار مخرب ناشی از هرگونه اختلال امری مهم و ضروری به نظر میرسد.
روش‌شناسی پژوهش: در این مقاله، یک مدل ریاضی چندهدفه، چند دورهای و سناریو محور ارایه شده است که در آن علاوه بر کمینهسازی دو هدف زمان تحویل و هزینه، به منظور افزایش تابآوری شبکه، معیارهای منفی تابآوری نیز کمینه میگردند. برای غلبه بر ماهیت غیرقطعی دادهها نیز از رویکرد برنامهریزی تصادفی دو مرحلهای استفاده شده است. همچنین برای تبدیل مدل، به یک مدل تکهدفه، از برنامهریزی آرمانی بهره گرفته شده است.
یافته‌ها: به منظور اثبات کاربردی بودن مدل، دادههای واقعی یک مطالعه موردی در مشهد پیادهسازی شده است. در نهایت، بر اساس اعتبارسنجی و تحلیل حساسیت صورت گرفته، مدل غیرقطعی پیشنهادی از برتری مشهودی نسبت به مدل قطعی برخوردار است.
اصالت/ارزش افزوده علمی: این مقاله یک مدل ریاضی خطی چندهدفه را برای طراحی شبکه زنجیره تامین دارو تحت شرایط کووید-19 ارایه می‌کند که در آن دو شاخص زمان و تابآوری به عنوان ابزارهای بهینهسازی به طور همزمان در نظر گرفته شدهاند.
کلیدواژه‌ها
موضوعات
عنوان مقاله [English]
A two-stage stochastic programming approach to design a resilient pharmaceutical supply chain network: a case study of COVID-19
نویسندگان [English]
- Alireza Roshani
- Mohammad Reza Gholamian
- Mahsa Arabi
Department of Logistics and Supply Chain, School of Industrial Engineering, Iran University of Science and technology (IUST), Tehran, Iran.
چکیده [English]
Purpose: Due to the increasing complexity of uncertainty and its impact on the supply chain network, many researchers have resorted to coping approaches with data uncertainty. In addition, the occurrence of any disruption in the supply chain networks can cause irreparable damage. Therefore, adopting appropriate strategies to increase the level of the supply chain network resilience toward any disruptive events seem to be necessary.
Methodology: In this paper, a multi-objective, multi-period, and scenario-based mathematical model is presented in which objective functions of delivery time and total network cost are minimized, and to increase network resilience, non-resilience measures are also minimized. Furthermore, a Two-Stage Stochastic Programming (TSSP) approach has been utilized to overcome the uncertain nature of the input parameters. Goal programming has also been used to transform the model into a single-objective one.
Findings: In order to prove the model's applicability, the real-world data of a case study of Mashhad has been implemented. Eventually, according to the validation and sensitivity analysis results, the proposed uncertain model has clear superiority over the deterministic model.
Originality/Value: This paper presents a multi-objective linear mathematical model for designing the Pharmaceutical Supply Chain (PSC) network under the COVID-19 situation. Two indicators of time and resilience as optimization tools have been considered simultaneously.
کلیدواژه‌ها [English]
- Supply chain network design
- Two-stage stochastic programming
- Supply chain resilience
- Pharmaceutical supply chain
- COVID-19
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