بهینه‌سازی یکپارچه مسیر و زمان‌بندی خدمات پرستاری با در نظر گرفتن محدودیت‌های عملیاتی حوزه سلامت: مطالعه موردی در شهر کرمانشاه

یوسفی یگانه, بابک

doi:10.61882/jii.3.1.66

بهینه‌سازی یکپارچه مسیر و زمان‌بندی خدمات پرستاری با در نظر گرفتن محدودیت‌های عملیاتی حوزه سلامت: مطالعه موردی در شهر کرمانشاه

نوع مقاله : مقاله پژوهشی

نویسنده

بابک یوسفی یگانه

گروه مهندسی صنایع، دانشکده مدیریت مهندسی، دانشگاه صنعتی کرمانشاه، کرمانشاه، ایران.

10.61882/jii.3.1.66

چکیده

مسیریابی پرستاران به‌عنوان یکی از مسائل کلیدی در مدیریت عملیات سلامت، نیازمند برنامه‌ریزی بهینه تخصیص بیماران و زمان‌بندی دقیق ویزیت‌ها با رعایت بازه‌های زمانی مشخصی است که بیماران باید در آن‌ها تحت مراقبت قرار گیرند. با توجه به پیچیدگی ترکیبی مسئله و محدودیت‌های زمانی، ارائه راهکارهای مؤثر و قابل‌اعتماد اهمیت ویژه‌ای دارد. در این مطالعه، یک مدل برنامه‌ریزی عدد صحیح مختلط توسعه‌یافته است که تخصیص بیماران به پرستاران و زمان‌بندی بازدیدها را با در نظر گرفتن این بازه‌های زمانی به‌طور جامع مدل می‌کند. حل دقیق مدل با استفاده از سالور GLPK و محیط Pyomo انجام شده است؛ اما با افزایش اندازه مسئله، زمان حل این روش‌ها به‌شدت افزایش می‌یابد. به‌منظور مقابله با این چالش، الگوریتم شبیه‌سازی تبرید مبتنی بر یادگیری طراحی و پیاده‌سازی شده است که با بهره‌گیری از مکانیزم‌های یادگیری، بهبود عملکرد جستجو و یافتن راه‌حل‌های با کیفیت بالا را در زمان‌های محاسباتی قابل‌قبول ممکن می‌سازد. نتایج تجربی نشان می‌دهد که این روش فراابتکاری، به‌خصوص در مسائل بزرگ‌تر، عملکرد به‌مراتب بهتری نسبت به روش‌های دقیق داشته و توانسته تعادل مناسبی میان کیفیت راه‌حل و سرعت اجرا برقرار کند. مدل بر روی یک مطالعه موردی واقعی مربوط به بیماران یکی از بیمارستان‌های شهر کرمانشاه پیاده‌سازی شده و نتایج حاکی از اثربخشی عملی مدل در بهبود کیفیت و کارایی خدمات پرستاری است.

کلیدواژه‌ها

مساله مسیریابی پرستاران

شبیه سازی تبرید

الگوریتم های یادگیری

بهینه سازی ترکیباتی

پنجره زمانی بیماران

عنوان مقاله English

Integrated Optimization of Routing and Scheduling for Nursing Services Considering Healthcare Operational Constraints: A case study in Kermanshah

نویسنده English

Babak Yousefi Yegane

Department of Industrial Engineering, Faculty of Engineering Management, Kermanshah University of Technology, Kermanshah, Iran.

چکیده English

Nurse routing is a key challenge in healthcare operations management, requiring optimal patient-to-nurse assignment and precise scheduling of visits within specific time windows during which patients must receive care. Given the combinatorial complexity of the problem and the presence of strict temporal and capacity constraints, developing efficient and reliable solution approaches is of critical importance for real-world applicability.
In this study, a comprehensive mixed-integer programming (MIP) model is developed to simultaneously address both the assignment of patients to available nurses and the detailed scheduling of visits. The model fully respects hard time windows, nurse availability, and care delivery requirements. The exact solution of the model using the open-source solver GLPK within the Pyomo framework demonstrates acceptable performance for small-scale problem instances. However, the computational time escalates significantly as the problem size increases, limiting the practical usability of the exact method for large cases. To overcome this limitation, a learning-based simulated annealing (SA) algorithm is designed and implemented. By incorporating adaptive learning mechanisms, including reinforcement learning strategies such as Q-learning, this approach improves the efficiency and intelligence of the search process. The proposed metaheuristic enables the discovery of high-quality solutions within reasonable computation times, even for large-scale instances. Experimental results demonstrate that the learning-enhanced SA algorithm significantly outperforms exact methods in terms of both solution quality and computational efficiency. This research not only contributes to the advancement of optimization techniques in healthcare logistics but also highlights the practical value of hybrid metaheuristics in handling complex, real-world scenarios. The model was validated through a real case study in Kermanshah, Iran, and results indicate considerable improvements in resource utilization, nurse workload balance, and service timeliness.

کلیدواژه‌ها English

Nurse Routing Problem

Simulated Annealing

Learning Algorithms

Combinatorial Optimization

Patient Time Windows

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