حل مسئلۀ چیدمان تسهیلات بیمارستانی با الگوریتم‌های فراابتکاری مطالعۀ موردی: بیمارستان خاتم‌الانبیای ایرانشهر

هدف اصلی مسئلۀ جایابی تسهیلات بیمارستانی، قرار‏دادن پلی‌کلنیک‌ها‏، آزمایشگاه‌ها‏، واحدهای رادیولوژی و غیره، به‏خصوص در بیمارستان‌هایی با مقیاس بزرگ در مناطقی از پیش تعیین شده است؛ به‏طوری که هزینۀ جابه‏جایی بیماران و کارکنان بهداشت و درمان را به حداقل برساند. ازنظر کارایی بیمارستان لازم است بخش‌هایی که تعامل بیشتری با هم دارند، نسبت‏به بخش‎‍های با تعامل کم‎‍تر، نزدیک یکدیگر قرار گیرند؛ بنابراین یکی از عوامل اصلی برای بالابردن کارایی بیمارستان، کم‏کردن حرکت بیمار و کادر بهداشت و درمان در داخل بخش‌ها و بین بخش‌ها و نیز تا ورودی بیمارستان است‎‎‏. برای این منظور، تابع هدف‎‎ مناسب را با توجه به فاکتور جابه‏جایی‎‎‏، طوری تعریف کردیم که فاصلۀ بین بخش‌های با تعامل بالا کمینه شود. این امر خود باعث کم‏شدن بعد مسافت طی‏شده از سوی بیماران و کادر درمان و همین‏طور همراهان بیماران (در صورت داشتن همراه) و کاهش هزینۀ جابه‏جایی بیماران و افزایش کارایی بیمارستان می‌شود.‏ همین‏طور در تابع هدف‏، فاصلۀ هر بخش تا ورودی بیمارستان و تعداد بیماران هر بخش را در نظر گرفته‌ایم تا بخش‌های پرترافیک نزدیک‏تر به ورودی بیمارستان قرار گیرند و بیماران برای رسیدن به هر بخش، هزینۀ کمتری را به‏لحاظ جابه‏جایی بپردازند. با توجه به اینکه مسائل جایابی تسهیلات، مسائلی ان‎‍پی‎‍-سخت‏ هستند، برای حل آنها از الگوریتم‌های فراابتکاری استفاده می‌شود. در این مقاله برای حل مسئلۀ به دست آوردن بهترین چیدمان برای بخش‌های مختلف بیمارستان خاتم‏الانبیای شهرستان ایرانشهر‏، از الگوریتم‌های فراابتکاری ژنتیک و الگوریتم شبیه‌سازی تبرید استفاده کردیم.

solving hospital facility layout problem using metaheuristic algorithms the case of khatam-al-anbia hospital in iranshahr

purpose: this study presents a case analysis on facility layout in a specific hospital, addressing a gap in the literature where limited research has investigated the application of metaheuristic algorithms to real-world hospital facility layout challenges. by utilizing these methods, not only was the relocation cost of patients and staff effectively optimized, but the impacts of this optimization were also assessed within an actual hospital environment. the study demonstrates how these selected algorithms can significantly reduce costs and enhance operational efficiency, while also uniquely examining their tangible outcomes in a real-world hospital context. unlike similar studies, this work directly focuses on the concrete results of algorithm implementation and provides a thorough comparison of costs before and after their application. these practical insights represent a notable advancement in the real-world application of such algorithms and offer actionable solutions for hospital managers.  design/methodology/approach: improving hospital efficiency largely relies on minimizing the movement of patients and healthcare staff within and between departments, as well as to and from the hospital entrance. to achieve this, the study formulates an objective function that minimizes the distance between highly interactive departments. given that facility layout problems are classified as np-hard, metaheuristic algorithms are frequently employed to find effective solutions. in this research, two metaheuristic algorithms—genetic algorithm (ga) and simulated annealing (sa)—were utilized to determine the optimal layout for the departments of khatam-al-anbia hospital in iranshahr.findings: the solutions derived from the genetic algorithm and simulated annealing demonstrated a notable enhancement compared to the hospital’s current layout. the results clearly indicate, from a theoretical standpoint, that the existing departmental arrangement is highly inefficient when juxtaposed with the optimized layouts produced by these two algorithms. consequently, utilizing these metaheuristic algorithms for facility layout facilitates the identification of cost-effective configurations, providing significant practical value for real-world hospital applications. research limitations/implications: this study encountered two primary limitations. first, accurate data regarding the number of patients’ companions, accompanying healthcare personnel, and their movement patterns within and between departments were unavailable. consequently, this information was omitted from the analysis. the second limitation pertained to the non-uniform area sizes of hospital departments in the facility layout problem of khatam-al-anbia hospital in iranshahr. this simplification may pose practical challenges for implementing the proposed layout. to mitigate this issue, departments with significantly larger or smaller areas compared to others were excluded from the model, and their locations were treated as fixed, based on their current positions in the hospital layout. practical implications: based on the findings of this study, it is advisable that scientific methods—such as those outlined in this research—be utilized in the initial planning and layout of hospital departments. this approach can significantly improve the operational efficiency of healthcare facilities, resulting in substantial enhancements in resource allocation, workflow efficiency, and service delivery quality.social implications: enhancing hospital department layouts through scientific and data-driven methods can lead to more efficient healthcare delivery, decreased patient and staff fatigue, and reduced waiting or transfer times. these enhancements can improve the overall patient experience and quality of care, especially in resource-constrained or underserved areas. ultimately, such optimizations support broader public health objectives by fostering more effective and equitable healthcare services. originality/value: the findings of this study illustrate how applied metaheuristic algorithms can effectively lower costs and boost operational efficiency in real-world contexts. these practical aspects underscore a significant innovation in the real-world application of such algorithms, providing actionable insights for hospital managers and other organizations aiming to enhance facility layout through data-driven decision-making.