رویکردهای همسایگی جدید در الگوریتم ممتیک برای یافتن نوع مشتری
محورهای موضوعی : مهندسی کامپیوتر و فناوری اطلاعات
حامد شرافت مولا
1
,
هادی یعقوبیان
2
,
راضیه ملک حسینی
3
,
کرم الله باقری فرد
4
1 - گروه مهندسی کامپیوتر، واحد یاسوج، دانشگاه آزاد اسلامی، یاسوج، ایران
2 - گروه مهندسی کامپیوتر، واحد یاسوج، دانشگاه آزاد اسلامی، یاسوج، ایران
3 - گروه مهندسی کامپیوتر، واحد یاسوج، دانشگاه آزاد اسلامی، یاسوج، ایران
4 - گروه مهندسی کامپیوتر ، واحد یاسوج، دانشگاه آزاد اسلامی، یاسوج، ایران
کلید واژه: مدیریت درآمد, الگوریتم‌های فراابتکاری, الگوریتم ژنتیک, الگوریتم ممتیک,
چکیده مقاله :
سیستم‌های «مدیریت سود» امروزه به‌صورت فراوان در صنایع مختلفی استفاده می‌شوند. یکی از پایه‌های اصلی مدیریت سود، «برآورد تقاضا» است که بر اساس آن تقاضای محصولات و خدمات پیش‌بینی می‌شود. شناخت مشتریان و علایق آنها زیربنای برآورد تقاضاست و این شناخت با حل مسئله «کشف نوع مشتری» به‌دست می‌آید. به تازگی این مسئله با استفاده از روش فراابتکاری «ژنتیک» حل‌شده¬است و در این تحقیق با استفاده از رویکردهایی دیگر برای یافتن همسایگی، این مسئله را با روش فراابتکاری «ممتیک» حل خواهیم¬کرد. برای ارزیابی تحقیق خود، از داده‌های واقعی پنج هتل استفاده خواهیم¬کرد و در ادامه نشان می‌دهیم که روش پیشنهادی درمجموع با 10.5 درصد تعداد نسل کمتر نسبت به روش «ژنتیک» اولین راه‌حل قابل‌قبول مسئله را ارائه می‌دهد.
"Revenue management" systems are extensively utilized across various industries today. One of the primary pillars of revenue management lies in demand estimation, which predicts the demand for products and services. Understanding customers and their preferences forms the cornerstone of demand estimation, and this understanding is acquired through solving the "customer type discovery" problem. Recently, this problem has been addressed using the "genetic" meta-heuristic method. In this research, we propose solving this problem utilizing the "memetic" meta-heuristic method, employing alternative approaches to identify the neighborhood. By evaluating real data from five hotels, we demonstrate that our method offers the first viable solution to the problem, resulting in a total of 10.5% fewer iterations compared to the "genetic" method.
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