ارائۀ راهکاری مکان‌مند به‌منظور بهبود مدیریت امداد و نجات پس از زلزله

زلزله، بحران و بلای طبیعی تقریباً پیش‌بینی‌ناپذیری است که همه‌ساله انسان‌های زیادی به خسارات جبران‌ناپذیر جانی و مالی آن دچار می‌شوند. مدیریت این‌گونه بحران‌ها هم به پیش از وقوع آنها مربوط می‌شود و هم به پس از آن. امداد و نجات، جزء مراحلی در وقوع حوادث است که می‌توان با مطالعه و بررسی آن پیش از وقوع به راهکاری برای بهبود عملکرد گروه‌های امداد و نجات در هنگام بحران رسید. در این پژوهش با بهره‌گیری از سیستم اطلاعات مکانی و الگوریتم ازدحام ذرات و شبیه‌سازی زلزله‌ای فرضی، راهکاری برای مدیریت بهینه گروه‌های امداد و نجات در وقوع زلزله پیشنهاد می‌شود. در این روش، زلزله‌ای فرضی در تهران شبیه‌سازی شد و 32 امدادرسان در قالب چهار گروه عملیاتی در 148 مجتمع مسکونی در محدودۀ تحقیق به انجام وظیفه پرداختند. امدادگران با استفاده از الگوریتم ازدحام ذرات در یک سیستم اطلاعات مکانی، به‌گونه‌ای به فعالیت‌های امداد و نجات اختصاص یافتند که در زمان کمتری، امداد و نجات بیشتر و نیز کارامدتر نسبت به حالت تجربی و سنتی انجام دهند. استفاده از این الگوریتم برای بهینه‌سازی شبیه‌سازی‌ها و نیز اجرای ساختار علمی و عملی فعالیت‌ها و گروه‌های عملیاتی امداد و نجات، راهکاری نوین برای بهبود کیفیت امداد و نجات پس از زلزله خواهد بود. نتایج اجرای الگوریتم پیشنهادی این پژوهش، بهبود حدود دوبرابری تخصیص صورت‌پذیرفته را نشان داد.

Providing a Spatial Approach in the Rescue and Relief Management after the Earthquake

 Fig. 4. Optimization of the Relief & Rescue TeamIn the study area of ​​the image above, the “Rescuers 34” relate to relief workers assigned to Light Collapse Lifting activities; “Rescuers32”, relief workers, and Pointing; “Rescuers31”, rescuers assigned to Searching activities. As well as “Rescuers33” for rescue workers who are engaged in Securing Pilot and relief workers “Rescuers 37”, engaged in Primary Helping activities. The allocation of people is carried out according to the priority, and the residential areas that have more damage are in the priority of the relief effort.In evaluating the efficiency of the proposed algorithm, the positive effect of the initial population selection method shown in the results obtained from the implementation of the proposed algorithm. Finally, a 2.2 fold improvement in the results obtained from the state that was not used by this algorithm. In the table below, the calculation of the cost function in the two modes of implementation of the proposed algorithm and its nonimplementation is set, which represents the calculating the cost of the allocation in the two situations for the entire operational team.Table 1. Comparison of the results of the proposed algorithm and its validationUsed modelCost calculated for the entire operational teamWithout using the proposed algorithm0.564Using the proposed algorithm0.252ConclusionDue to the facts that the problem is considered to be grouped of the subject of this research, the effectiveness of each person’s activity on the other people’s activities, and the group and the category of operations, as well as the structure of the particle swarm algorithm, which allows for more repetition in less time, the proposed algorithm of this study is identified as an appropriate solution to the postearthquake relief and rescue problem.The structure of the particle swarm algorithm is continuous; because of the discrete structure of the present, it is implemented discretely by applying changes to the structure of this algorithm. As previously stated, the context of individuals, their specializations, the activities, and the damaged sites have the same priorities as those that were implemented in the algorithm.Using the proposed algorithm of this research and applying the changes expressed in it, in order to optimize and implement the scientific and practical structure of relief and rescue operation activities and teams, is a novel and effective way to improve the quality of relief and rescue after it will be an earthquake. Finally, as shown in Table 1 in the findings, the proposed algorithm implementation in this study improved the 2.2% of the results from the allocation of relief workers to a state that was not used by the proposed algorithm of this study.For future researches, the optimization methods such as simulated annealing, ant colony, genetics, and game theory are suggested.