مدل ترکیبی انتخاب تأمین‌کننده و تخصیص سفارش در شرایط عدم قطعیت تصادفی و حل با رویکرد بهینه‌سازی فازی استوار (مطالعه موردی: شرکت پشتیبانی اقلام عمومی ایثار)

حسن پور, حسینعلی; غفاری توران, حسین; زارعی, مصطفی; محمدی, علی

مدل ترکیبی انتخاب تأمین‌کننده و تخصیص سفارش در شرایط عدم قطعیت تصادفی و حل با رویکرد بهینه‌سازی فازی استوار (مطالعه موردی: شرکت پشتیبانی اقلام عمومی ایثار)

نوع مقاله : پژوهشی

نویسندگان

¹ استادیار گروه مهندسی صنایع، دانشکده فنی و مهندسی، دانشگاه جامع امام حسین ‌(ع)، تهران، ایران

² دانشجوی دکتری گروه مهندسی صنایع، دانشکده فنی و مهندسی، دانشگاه جامع امام حسین ‌(ع)، تهران، ایران

³ کارشناسی ارشد گروه مهندسی صنایع، دانشکده فنی و مهندسی، دانشگاه جامع امام حسین ‌(ع)، تهران، ایران

چکیده

هدف تحقیق ارائه یک مدل برنامه‌‌ریزی عدد صحیح غیرخطی دوهدفه‌‌ شامل کمینه کردن هزینه‌های خرید و زمان تأخیر در تحویل و بیشینه کردن میزان تاب‌‌آوری برای بهینه‌‌سازی زنجیره تأمین اقلام عمومی به‌طوری‌که تمام پارامترهای توابع هدف و محدودیت‌‌ها غیرقطعی میباشد، است. برای حل مدل، ابتدا اوزان مناسب معیارهای مرتبط برای تولیدکنندگان از طریق روش تصمیم‌‌گیری چندمعیاره به دست آمده که این اوزان ورودی مدل ریاضی پیشنهادی می‌‌باشند. ازآنجاکه این مسئله جزو مسائل بهینهسازی ترکیبی در خانوادۀ مسائل NP-hard محسوب میشود، برای حل مدل از الگوریتمهای تکاملی چندهدفه NSGA-II و MOPSO استفاده شد. برای مقایسه نتایج حاصل از الگوریتمها با کمک شاخص‌‌های مقایسه‌‌ای استفاده گردید. در این تحقیق برای تبدیل مدل فازی به مدل قطعی از برنامه‌ریزی محدودیت شانس امکانی استفاده شده که این روش دو مدل تخمین LAMو UAM را به‌طور مناسبی بر روی تغییرات بدبینانه-خوشبینانه ناشی از تفاوت نگرش تصمیم‌گیرندگان فیت میکند. یافتههای حاصل از مقایسه این الگوریتم‌‌ها بیانگر این امر است که در حالتهای خوشبینانه، الگوریتم ژنتیک عملکرد بهتری داشته و در حالتهای بدبینانه، الگوریتم MOPSO عملکرد بهتری ارائه میدهد. به‌طور نمونه در زمان حل و معیار فاصله از نقطه ایدئال در تمام مسائل عملکرد بهتری نسبت به الگوریتم دیگر دارد.

کلیدواژه‌ها

20.1001.1.20089198.1401.24.75.2.1

موضوعات

طراحی و توسعه شبکه زنجیره تأمین/ لجستیک

عنوان مقاله [English]

Providing a Combined Model of Supplier Selection and Order Allocation in Random Uncertainty Conditions and Solution with the Robust Fuzzy Optimization Approach (Case Study: Isar General Commodities Company)

نویسندگان [English]

Hossein-ali Hassan-pour ¹
Hossein Ghaffari Turan ¹
M. Zareei ²
ali mohamadi ³

¹ Industrial Eng Group/ Imam Hussein Comprehensive University

² ihu

³ ull

چکیده [English]

The purpose of this research is to present a two-objective nonlinear integer programming model for optimization of a general commodities supply chain, minimizing the purchase costs and delivery delays and maximizing the resilience with uncertain constraints and parameters of the objective functions. To solve the model, the weights of relevant metrics and sub-criteria for producers are obtained through multi-criteria decision making. These metrics are used as the input data to the proposed mathematical model. Since this is classified as a hybrid optimization problem in the NP-hard problem family, NSGA-II and MOPSO multi-objective evolutionary algorithms are used to solve the proposed model. Comparing the results of the algorithm is done with the help of comparative indices. In this study, the probability constraint programming is used for the fuzzy to deterministic conversion of models. This method fits the LAM and UAM estimation models appropriately to the pessimistic-optimistic changes due to the differences in the decision makers' attitudes. Results of comparing these algorithms indicate that the genetic algorithm performs better in the optimistic view, whilst the MOPSO algorithm performs better in the pessimistic case. The results of NSGA-II and MOPSO algorithm for the designed sample problem show that NSGA-II algorithm performs better than MOPSO algorithm in different criteria. For instance, better performance regarding the solution time and the criterion of the distance from the ideal point, is observed in all problems.

کلیدواژه‌ها [English]

Resilient Supply Chain
Random uncertainty
Robust Fuzzy Optimization
NSGA-II
MOSPO
Comparative Indicators

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