مدل چند هدفه مکان¬یابی هاب برای طراحی شبکه زنجیره تامین پایدار: الگوریتم های NSGAII و MOGWO
محورهای موضوعی : مدیریت تولید
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کلید واژه: مکان یابی هاب , زنجیره تأمین پایدار, مسائل چند هدفه, الگوریتم های NSGA-II و MOGWO,
چکیده مقاله :
در بازارهای امروزی ضروری است کالا ها و خدمات متناسب با شرایط رقابتی محیطی و نیازهای مشتریان تامین گردند. افزایش آگاهی عمومی نسبت به آلاینده های زیست محیطی ناشی از فعالیت های صنعتی بر چگونگی طراحی شبکه های زنجیره تأمین تاثیر چشم گیری گذاشته است طوریکه شبکه های زنجیره تأمینی که بصورت سنتی طراحی می شوند و تنها بعد اقتصادی را در نظر دارند امروزه برای رسیدن به توسعه پایدار کافی نمی باشند. در این مقاله یک مسئله چندهدفه مکان یابی هاب برای طراحی شبکه زنجیره تأمین پایدار بر اساس تقسیم بندی مشتریان ارائه می شود که سه جنبه پایداری اقتصادی (کاهش هزینه ها)، زیست محیطی (کمینه سازی انتشار گازهای گلخانه ای توسط بخش حمل و نقل) و مسئولیت اجتماعی (ایجاد اشتغال و توسعه ی جامعه) را بهطور همزمان در نظر دارد. مدل پیشنهادی با استفاده از روش محدودیت اپسیلون و روش محدودیت اپسیلون تقویت شده برای مسائل با ابعاد کوچک توسط نرم افزار GAMS حل می شود. باتوجه به NP-Hard بودن مدل پیشنهادی از دو الگوریتم فراابتکاری NSGA-II و ﺑﻬﻴﻨﻪ ﺳﺎز ﭼﻨﺪﻫﺪﻓﻪ ﮔﺮگ ﺧﺎﻛﺴﺘﺮي یا MOGWO جهت حل مساله در ابعاد بزرگ استفاده شده و نتایج ارائه می گردند.
In today’s markets, it is necessary to supplying products and services basis on consumer’s needs in competitive environments. Awareness about environmental pollution that creates through industrial activities have been influenced on supply chain design network. Therefore designing traditional supply chain network considering economical dimension is not suitable for sustainable development in societies. In this paper a multi-objective hub location model is proposed to design a sustainable supply chain network considering three aspects of sustainability: economic aspects by reducing costs, environmental impacts by reducing greenhouse gasses emissions, and social responsibility through employments and social development. Proposed model is assessed by solving small scale sample problems through GAMS software using e- constraint and augmented e-constraint method. The proposed model is NP hard and two Meta heuristic algorithms: NSGAII and MOGWO are been used for solving large scale sample problems and the results are presented.
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