بهینه‌سازی سفارش و تخصیص چند تأمین با تصمیم‌گیری مارکوف

حسینی, لیلا; فلاح نژاد, محمد صابر; درهمی, ولی; اولیا, محمد صالح

بهینه‌سازی سفارش و تخصیص چند تأمین با تصمیم‌گیری مارکوف

نوع مقاله : پژوهشی

نویسندگان

¹ دانشجوی دکتری، گروه مهندسی صنایع، دانشکده مهندسی، دانشگاه یزد، یزد، ایران

² استاد، گروه مهندسی صنایع، دانشکده مهندسی، دانشگاه یزد، یزد، ایران،

³ استاد، گروه مهندسی کامپیوتر، دانشکده مهندسی، دانشگاه یزد، یزد، ایران

⁴ استاد، گروه مهندسی صنایع، دانشکده مهندسی، دانشگاه یزد، یزد، ایران

چکیده

در این مقاله، یک مدل مدیریت موجودی مبتنی بر چارچوب تصمیم‌گیری مارکوف برای افق زمانی محدود و دوره‌های گسسته توسعه ‌یافته است. هدف اصلی این مدل، کاهش هزینه‌های کلی مدیریت موجودی از طریق تعیین مقادیر بهینه سفارش‌دهی و تخصیص آن‌ها به تأمین‌کنندگان است. هزینه‌های سفارش‌دهی به‌عنوان متغیرهای تصادفی و هزینه‌های نگهداری به‌صورت تابعی خطی مدل‌سازی شده‌اند. با استفاده از روش برنامه‌ریزی پویای برگشت به عقب، سیاست‌های بهینه‌ای طراحی‌شده‌اند که هزینه‌های مرتبط با سفارش‌دهی و نگهداری را به حداقل می‌رسانند. برای ارزیابی مدل، یک مطالعه موردی در یک شرکت تولیدی انجام‌شده است که ماده اولیه پلی‌پروپیلن خود را از دو تأمین‌کننده دریافت می‌کند. نتایج نشان می‌دهد که تخصیص بهینه سفارش‌ها می‌تواند هزینه‌های کلی زنجیره تأمین را تا پایان دوره برنامه‌ریزی به میزان قابل‌توجهی کاهش دهد. این کاهش هزینه ناشی از بهینه‌سازی در مقادیر سفارش‌دهی و انتخاب مناسب تأمین‌کنندگان بر اساس سیاست‌های ارائه‌شده توسط مدل است. مدل پیشنهادی با در نظر گرفتن عدم قطعیت در هزینه‌های سفارش‌دهی، قابلیت کاربرد در محیط‌های واقعی را دارد. این مدل ابزارهای مؤثری برای بهبود تصمیم‌گیری و کاهش هزینه‌ها در زنجیره تأمین فراهم می‌کند و می‌تواند به‌عنوان رویکردی عملی برای شرکت‌های تولیدی با تأمین‌کنندگان متعدد استفاده شود.

کلیدواژه‌ها

موضوعات

برنامه‌ریزی تولید (پیش بینی و برآورد، مدیریت سفارش)

عنوان مقاله [English]

Multi-Supply Order Optimization and Allocation with Markov Decision Making

نویسندگان [English]

Leila Hosseini ¹
Mohammad Saber Fallah Nezhad ²
Vali Derhami ³
Mohammad Saleh Owlia ⁴

¹ Student

² Department of Industrial Engineering, Yazd University, P.O. BOX 89195-741, Pejoohesh Street, Safa-ieh, Yazd, Iran

³ Technical and Engineering Campus Building 1, Room 223. Yazd University. Yazd, Iran

⁴ Department of Industrial Engineering, Yazd University,, Pejoohesh Street, Safa-ieh, Yazd, Iran

چکیده [English]

In this study, an inventory management model based on a Markov decision process (MDP) framework is developed for a finite planning horizon with discrete time periods. The primary objective of the model is to minimize the total inventory management costs by determining the optimal order quantities and their allocation to suppliers. Ordering costs are modeled as random variables, while In this paper, an inventory management model based on the Markov Decision Process (MDP) framework is developed for a finite horizon and discrete time periods. The primary objective of this model is to minimize the total inventory management costs by determining the optimal order quantities and their allocation to suppliers. Ordering costs are modeled as stochastic variables, while holding costs are represented as linear functions. Utilizing a backward dynamic programming approach, optimal policies have been derived to minimize costs associated with ordering and holding inventory.To evaluate the model, a case study was conducted in a manufacturing company that sources polypropylene raw material from two suppliers. The results indicate that optimal order allocation can significantly reduce the overall supply chain costs by the end of the planning horizon. This cost reduction stems from the optimization of order quantities and the appropriate selection of suppliers based on the policies provided by the model. The proposed model, by incorporating uncertainty in ordering costs, demonstrates applicability in real-world settings. It offers effective tools for improving decision-making and cost reduction in supply chain management and can serve as a practical approach for manufacturing firms with multiple suppliers.

کلیدواژه‌ها [English]

Order Quantity
Supplier Allocation
Markov Decision Process
Backward Dynamic Programming

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