بررسی تأثیرتخفیفات حجمی بر مدل مقدار اقتصادی سفارش تحت رویکرد فازی

محورهای موضوعی : مدیریت صنعتی

Gholamreza Amini Khiabani ¹ , Karim Hamdi ²

1 - MSc. Faculty of Economic and management, Islamic Azad University, Science and Research Branch, Tehran, Iran
2 - Associate Prof., Faculty of Economic and management, Islamic Azad University, Science and Research Branch, Tehran, Iran

تاریخ دریافت : 1394/10/01 تاریخ پذیرش : 1395/04/28 تاریخ انتشار : 1395/06/14

کلید واژه: Inventory, EOQ, Fuzzification, Quantitative Discounts, رویکرد فازی, تخفیفات حجمی, مقدار اقتصادی سفارش,

چکیده مقاله :

مسائل برنامه ریزی تولید و کنترل موجودی از جمله موضوعات کلیدی و تحت تابعیت شرایط عدم اطمینان در سازمان های تولیدی هستند زیرا از یک طرف این عدم قطعیت، باعث افزایش هزینه های سیستم می‌شود و از طرفی میزان صحیح سفارش مواد اولیه سود قابل توجهی را عاید سازمان می‌نماید. لذا در این پژوهش یک مدل کنترل موجودی در شرایط اعمال تخفیفات حجمی (مقداری ) تحت رویکرد فازی طراحی می‌گردد. برای محاسبه فاکتور های بهینه مدل از رویکرد دیفازی سازی پارامترهای ورودی قبل از حل مدل و همچنین دیفازی سازی پارامترهای خروجی بعد از حل مدل بر مبنای روش گشتاورها در قطعی سازی پارامترها استفاده گردید. همچنین پارامتر‌های مدل پیشنهادی، از طریق تبدیل روش های دیفازی سازی به روش های کلاسیک و سپس جایگذاری اعداد دیفازی شده محاسبه شدند. جهت نشان دادن حساسیت مدل از یک مطالعه موردی استفاده شد و نتایج به دست آمده با استفاده از نرم افزار Solver 2013 نشان داد این رویکرد ابتکاری، مدل را با تفصیل بیشتری مورد بررسی قرار می‌دهد و برای هر سطح برش، مقادیر بهینه فاکتورها را ارایه می نماید. به بیان دیگر تصمیم گیری با استفاده از این رویکرد ابتکاری یک تصمیم گیری چندسطحی است و می توان بنا به سیاست های مدنظر سازمان، سطح مورد نظر را انتخاب نمود.

چکیده انگلیسی:

Production Programming and Inventory Control are known as most important topics in manufacturing companies subordinated the uncertainty and risk. This uncertainty increases the system costs while economic order quantity (EOQ) gives direct benefit to the organization. In this research, a new inventory control model will be devised due to the volume discount and under fuzzy approach. To calculate the optimal parameters, we used pre-solving de-fuzzification for all entrance parameters and pro-solving de-fuzzification for all output parameters based on torques method in parameters certainty. Meanwhile, converting de-fuzzification models to classical models and replacing de-fuzzy digits finally led to calculate the suggestive parameters. To better illustrate the model sensitivity, we used a case study. The result showed this heuristic approach would analytically examine the model and determine optimal parameters quantity for each level and each optimal level could be selected depends on the organization policies. 

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