جستجوی مقالات مرتبط با کلیدواژه « Emergency logistics » در نشریات گروه « مدیریت »

Planning to empty warehouse cells is one of the most challenging issues in times of crisis. The need for emergency logistics for the efficient use of equipment is of great importance. In this study, a dual-objective planning model of routing and simultaneous scheduling of heterogeneous vehicles (picking containers)It has been suggested to evacuate the courier warehouse cells in emergency situations (non-compliance of the piece with the courier schedule) in order to minimize the movement time and maximize the reliability of the routes due to the congestion of the warehouse corridors. The developed Epsilon constraint method has been used to solve the proposed model. In the proposed model, the possibility of providing service for each warehouse cell that should be emptied by heterogeneous peak containers to logistics /warehouse areas with limited capacity is considered.To demonstrates the performance of the proposed model, the model is run on a random example and the computational results are presented. The results of problem-solving indicate a conflict between the objective functions used. In order to investigate the large-scale model, due to the Np-hard routing issues, three Particle Swarm Algorithms (PSO), Ant Colony (ACO), and Bee Colony (ABC), swarm intelligence algorithms were used and the results were compared with each other. The results of large-scale problem solving to find the best displacement path show better performance of the particle swarm algorithm.

The occurrence of natural disasters, such as earthquakes, is sometimes associated with many financial and personal injuries. Proper allocation of each city district to emergency resettlement sites, hospitals and clinics will accelerate the relief process and reduces injuries. The purpose of this research is to present an integer linear mathematical programming model for optimal locating and allocating of emergency resettlement points to city districts.

In this research, the volume of flow between the demand points and emergency resettlement sites, clinics, and hospitals is determined. The objective function of the presented mathematical model is to minimize the mathematical expectation the of total transfer time of people, so that the coefficient of fines is considered for the later transmission of the affected persons to the treatment centers. Due to the accidental nature of natural disasters, uncertainty in this study is seen as a scenario. In this study, the probability of occurrence of different scenarios, the probability of injury in each area in different scenarios, the classification of injured persons, and the limited reception capacity at resettlement and treatment centers are considered.

The proposed research model was coded and solved using Bojnord city information. The optimal allocation of each district of Bojnord to temporary hospitals, clinics and resettlement sites has been determined.

Although the probability of injury was considered small in different city districts, the hospitals did not have the capacity to repost to the injured persons and it is necessary to use the adjacent city hospital for hospitalization. Locating temporary treatment centers is also needed in areas close to the villages before such events occur.

The occurrence of natural and unnatural disasters threatens the lives of many people around the world every year. Disaster response consists of some elements such as transferring of healthy people from the affected areas to the camps. The purpose of this study is to provide a mixed-integer linear mathematical programming model for determining the best location of camps and the route of vehicles between affected points and camps. In the transfer operation, fuzzy uncertainties of transportation time, the amount of affected points' demand, the time of service, and the time window are considered. In this research, the optimal amount of arrival time of vehicles to camps is calculated in pessimistic conditions. Then this amount is considered as the time limitation in a model to minimize the expected cost of damage to people and vehicles. The cost has been calculated with regard to the security of each route from the warehouse to the affected points and from the points to the camps. To illustrate the performance of the proposed model, the model is implemented on a random example. The results of this research can be used as a model for planning the response of the possible crisis of the operating defense.

Ccurrence of Unexpected events and natural disasters and the effects of their occurrence, communities are required to do the planning necessary to meet current and reduce the adverse effects is relevant. Although in minor evens, this is experimentally possible and don’t need  to  special tools, but in the event bigger and natural disasters such as flood, earthquake, ad hoc disaster and especially war, because of the size and complexity of factors involved, do it was not possible in experimental and for decision making is need to decision making tools. The main objective of this paper is to present a model for the structure and identify the impact of factors affecting the emergency logistics so that any appropriate decisions for the activities of logistics crisis in a way adopted to make the best use of resources has been done and the highest efficiency in order to reduce the impact of disasters resulting be. Society and research sample consisted of experts in the field of logistics and analysis using the “Delphi method” and “dematel dematel technique” was conducted, the results showed, dynamism, speed, capable of sustaining support, preparation, integrity and effective communication among the elements involved, flexibility, reactivity and information Technology  are 8 main indicators affecting on emergency logistics. The results of the structuring factors affecting supply chain agility showed that the factors "IT" and "dynamism" has the greatest impact on the logistics system crisis and the "reactive" and "The sustainability of support" have the least impact and the richest are variable