06 اردیبهشت 1403
خداكرم سليمي فرد

خداکرم سلیمی فرد

مرتبه علمی: دانشیار
نشانی: دانشکده کسب و کار و اقتصاد - گروه مدیریت صنعتی
تحصیلات: دکترای تخصصی / تحقیق در عملیات
تلفن: 07731222118
دانشکده: دانشکده کسب و کار و اقتصاد

مشخصات پژوهش

عنوان
یک رویکرد آمیخته فراابتکاری و شبیه­ سازی برای بهینه­سازی الگوهای زمان­بندی در درمانگاه­های دسترسی آزاد
نوع پژوهش پارسا
کلیدواژه‌ها
scheduling, clinic scheduling, walk-in patient, open access, Genetic algorithm, simulation
پژوهشگران سعادتمند سارا (دانشجو) ، خداکرم سلیمی فرد (استاد راهنما) ، رحیم قاسمیه (استاد مشاور)

چکیده

Background: scheduling is one of the major topics in operations research in which a set of jobs is assigned to a set of time slots such that the total time is minimized. This research topic includes clinic scheduling where patients are assigned to a specific time slot to be visited by a doctor. Aim: the main objective of this research is to find an optimal scheduling for a clinics with open access. Methodology: in order to satisfy the multi objective nature of the problem, an integer mathematical model is developed. Due to the NP-hard property of the model, it is then formulated in Genetic Algorithm, where a chromosome represents a feasible scheduling template. In order to overcome the complexity of the calculation of different objective functions, the model is solved using NSGA-II metaheuristic algorithm. To obtain the solution, Matlab programming tool is used. The solution procedure has efficiently produced Pareto front, where a set of non-dominant solution is obtained. Results: research results show that for a single day solution, the obtained scheduling template is able to assign suitable time slots to all types of patients including having appointment, walk-in, and open access. The scheduling is also minimized patients waiting time, and doctor overtime and ideal time. Conclusions: based on research findings, it could be claimed that the modeling approach and the solution procedure were sufficient enough to capture the complexity of the problem and to generate optimal solution. Since the proposed model is a multi-objective optimization, it could be used to automatically generate scheduling templates to satisfy both patients and doctors.