ارایه یک مدل بهینه سیستم خدماتی رقابتی در حالت عدم قطعیت با استفاده از الگوریتم تکامل تفاضلی بهبود یافته
محورهای موضوعی : آمار
محمد فلاح
1
,
رضا توکلی مقدم
2
,
علی پهلوانی
3
,
علی رضا سلامت بخش ورجوی
4
1 - گروه مهندسی صنایع، واحد تهران مرکزی، دانشگاه آزاد اسلامی، تهران، ایران
2 - دانشکده مهندسی صنایع، پردیس دانشکده‌های فنی، دانشگاه تهران، تهران، ایران
3 - دانشکده مهندسی صنایع، دانشگاه علم و صنعت ایران، تهران، ایران
4 - گروه مهندسی صنایع، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران
کلید واژه: Competition, Differential Evolution, Congestion, Location allocation,
چکیده مقاله :
در این مقاله، ارائه یک مدل بهینه‌سازی همزمان جایابی و تخصیص سرور برای شرکت تازه وارد در یک بازار رقابتی مورد توجه قرار گرفته است. هدف بیشینه‌سازی سود شرکت است. مشتریان بهصورت احتمالی و بر اساس معیارهای قیمت، زمان سفر و زمان انتظار در صف، تسهیلات مورد نظر را انتخاب می‌کنند. همچنین فرض می‌شود میزان آگاهی مشتریان از سطوح زمان‌های انتظار در مراکز به صورت مرحله‌ای و طی استفاده‌های متوالی از شبکه افزایش می‌یابد. تقاضا به صورت کششدار و تابعی از مطلوبیت مشتریان از طراحی کل شبکه و هزینه ارائه یک واحد خدمات در یک مرکز به صورت تابعی از تقاضای جذب شده آن تعریف می‌شود. برای حل مدل یک الگوریتم تکامل تفاضلی بهبود یافته توسعه داده شده و مسائل نمونه برای نمایش کارایی آن حل شده است. نتایج با روش‌های معرفی شده در تحقیقات پیشین مقایسه گردیدند. نتایج عددی حاکی از آن است که رویکرد پیشنهادی قادر است نسبت به روش‌های پیشین نتایج بهتری ارایه نماید و می‌تواند در حل مسائل کاربردی مورد استفاده قرار بگیرد.
This paper presents a simultaneous optimization model of location and server allocation for a company that enters into a competitive market. The goal is company's profit maximization. Customers are likely to select the facility based on price, travel time and queue time. Furthermore, as a contribution to the literature, it is assumed that customer awareness of the levels of waiting times in the facilities will increase in stages and over the successive uses of the facilities network. Demand is defined elastic and as a function of the customer's desirability of network’s design and the cost of delivering service at a facility is defined as a function of its demand attraction. An improved differential evolution algorithm has been developed to solve the model and sample problems to demonstrate its efficiency have been solved.
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