چكيده به لاتين
Nowadays, transportation of hazardous materials (HAZMAT) and emergency response during the incident is a very important issue. Hazmats refer to all substances that are harmful to people, property and the environment. Although hazmats are being used in the industry and daily life, they can badly harm the environment and humans. Due to the risk of fire and flammability, leakage and explosion of hazmats during transportation, hazmats incidents could be very dangerous. Although incidents involving hazmat transportation occur with a low probability, in the event of occurrence, they cause irreparable damages such as mortality, death, pollution and environmental damages and economic problems. Therefore, in the event of a hazmat incident, the most important subject is the emergency response to these incidents. For this purpose, a bi-level hazmat transportation network design problem is presented. The upper level (leader) is government and seeks to locate hazmat emergency response stations, purchase equipment for these stations considering the available budget and selection of allowable routes for hazmats in order to maximize the coverage of routes (emergency response) in the event of a hazmat incident. The lower level (follower) are carrier companies and seek to minimize their transportation costs according to the government's selected routes. In order to cope with the uncertainty in the model, a robust optimization based on scenario is used and given the optimality conditions, the presented bi-level model converts to a single-level model by the use of KKT conditions. Finally, in order to evaluate the efficiency of the proposed model, a case study was analyzed using actual data for transportation of hazmats in Fars province network and management solutions were presented. In order to solve the problem in the large scale of the case study, a genetic algorithm has been used and the sensitivity analysis on important parameters validates the efficiency of the proposed genetic algorithm.
Keywords: hazardous materials Transportation, hazmat, network design, emergency response, bi-level optimization, robust optimization, covering, genetic algorithm
