Biabanaki, Sayyed Amin, kargari, mehrdad. (1399). Routing relief teams by introducing new urban congestion parameter and solving using GACD-MDVRP clustering through genetic algorithm. نشریه مهندسی عمران امیرکبیر, 52(1), 77-88. doi: 10.22060/miscj.2020.17053.5171
Sayyed Amin Biabanaki; mehrdad kargari. "Routing relief teams by introducing new urban congestion parameter and solving using GACD-MDVRP clustering through genetic algorithm". نشریه مهندسی عمران امیرکبیر, 52, 1, 1399, 77-88. doi: 10.22060/miscj.2020.17053.5171
Biabanaki, Sayyed Amin, kargari, mehrdad. (1399). 'Routing relief teams by introducing new urban congestion parameter and solving using GACD-MDVRP clustering through genetic algorithm', نشریه مهندسی عمران امیرکبیر, 52(1), pp. 77-88. doi: 10.22060/miscj.2020.17053.5171
Biabanaki, Sayyed Amin, kargari, mehrdad. Routing relief teams by introducing new urban congestion parameter and solving using GACD-MDVRP clustering through genetic algorithm. نشریه مهندسی عمران امیرکبیر, 1399; 52(1): 77-88. doi: 10.22060/miscj.2020.17053.5171

Routing relief teams by introducing new urban congestion parameter and solving using GACD-MDVRP clustering through genetic algorithm

AUT Journal of Modeling and Simulation
مقاله 8، دوره 52، شماره 1، شهریور 2020، صفحه 77-88    اصل مقاله (1.5 M)
نوع مقاله: Research Article
شناسه دیجیتال (DOI): 10.22060/miscj.2020.17053.5171
نویسندگان
Sayyed Amin Biabanaki1؛ mehrdad kargari* 2
1Department of Industrial Engineering, Tarbiat Modares University (TMU), Jalal-Al-Ahmad St., Tehran, Iran
2Faculty of Industrial and Systems, Industrial and Systems, Tarbiat Modares, Tehran, Iran
چکیده
Emergency disaster-relief activities could dramatically reduce injuries and casualties, while routing and scheduling of the relief teams is also considered an important factor in reducing the fatalities. For this reason, in this paper, a new model is proposed for routing rescue teams considering time windows, capacitated and multi-depot vehicles. In this model, additional factors such as availability of relief centers, congestion and service standard for the vehicles. A new parameter has been developed to denote the congestion of each path and id incorporated into the model using the concept of Social Network Analysis (SNA). Finally, the model is solved using a COREI5 8GB system. The model is also implemented using the data obtained from the Roads and Transport Organization and the Iranian Red Crescent Society. The average accuracy of this algorithm was 87% after solving 23 problem samples and improvement of the runtime was 74% in large problems. The model is then applied to the case study of the 2017 earthquake in Kermanshah, Iran. A rescue scenario is generated using the historical data of I.R. Iran Road Maintenance Transportation Organization and the I.R. Relief and Rescue Organization of Red Crescent Society of Iran. In this study, simulations are conducted based on a case study with actual locations.
کلیدواژه‌ها
Vehicle Routing؛ Genetic algorithm؛ Metaheuristic؛ Mathematical model؛ Congestion؛ Time Window؛ Network Analysis؛ Multi-Depot
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