Applying genetic algorithm to a new bi-objective stochastic model for transportation, location, and allocation of hazardous materials. (1st June 2016)

• Record Type: Journal Article
• Authors: Ardjmand, Ehsan; Young, William A.; Weckman, Gary R.; Bajgiran, Omid Sanei; Aminipour, Bizhan; Park, Namkyu
• Is Part Of: Expert systems with applications. Volume 51(2016)
• Page Start: 49
• Page End: 58
• Publication Date: 2016-06-01
• Subjects: Hazardous material -- Transportation -- Location-allocation -- Stochastic programming; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2015.12.036
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A stochastic model for the locating and allocation of facilities of hazardous material is proposed. The model studies the transportation of hazardous material and supplying customers with goods. Environmental risk, profitability, and cost stochasticity are incorporated into the model. A genetic algorithm (GA) finds optimum and high quality solutions for both small and large problems. A (GA) is developed to obtain the best solution with run times of less than 10% of an exact algorithm. Abstract: In 2013, approximately 15, 600 HAZMAT accidents with 158 injuries and fatalities have been reported in the USA ("Transportation Statistics Bureau"). Managing hazardous material (HAZMAT) transportation and locating the disposal sites for these materials properly can significantly reduce the risk of accidents and its environmental and social aspects. In this research, a new stochastic model for transportation, location, and allocation of hazardous materials is proposed. The cost of transportation is considered to be of a stochastic nature. The objective function minimizes the total cost and risk of locating facilities and transportation of HAZMATs. The decisions which have to be made are: (1) where to open the facilities and disposal sites; (2) to which facilities every customer should be assigned; (3) to which disposal site each facility should be assigned; and (4) which routes a facility should choose to reach the customers and disposal sites. A novel genetic algorithm (GA) is applied to the model. The results show the efficiency of the proposed GA in terms of finding high quality solutions in a short time.