Dangerous industrial waste transportation network design considering time-dependent risk

Articles in Press

Document Type : Original Article

Authors

1 Supply chain management and Logistics, Industrial engineering, Iran university of science and Technology, Tehran, Iran

2 Industrial engineering, Iran university of science and technology, Tehran,Iran

10.22034/tri.2021.246098.2807

Abstract

Technology progress is a cause of industrial hazardous wastes increasing in the whole world. Management of hazardous waste is a significant issue due to the imposed risk on environment and human life. This risk can be a result of location of undesirable facilities and also routing hazardous waste. Industrial hazardous waste management involves the collection, transportation, recycling and disposal of industrial hazardous materials that pose risk to their surroundings. The present study extends a bi-objective mathematical model in the context of industrial hazardous waste management, which covers the integrated decisions of two levels with locating and vehicle routing. Analyzing these decisions simultaneously not only may lead to the most effective structure in the waste management network, but also may reduce the potential risk of managing the hazardous waste. The problem is formulated as a bi-objective Mixed-Integer Nonlinear Programming (MINLP) model, which can be easily converted into a MILP one. In the mathematical model, three criteria are considered: minimizing total cost, which includes total transportation cost of hazardous, earliness and tardiness penalties and fixed cost of establishing centers; minimizing total transportation risk related to the population exposure along transportation routes of hazardous materials and minimizing total risk for the population around treatment and disposal centers. A DEA is applied to select the locations with high priority in CPLEX. A simulated annealing algorithm combined with an MILP solver is proposed as the solution approach. For validation of the presented method, instances with various sizes are solved. The results of the GA in small size instances, has a small deviation from the optimal fitness values. Finally, a real case study is provided to demonstrate the applicability of the model in real-world environment. Sensitivity analysis is performed to show the effect of earliness and tardiness penalty on the objective function.

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Journal of Transportation Research

Articles in Press, Accepted Manuscript
Available Online from 30 June 2021

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