Multi-objective transportation route optimization for hazardous materials based on GIS

Safety and security are of paramount importance, it is important to optimize and improve the routes of trucks that carry hazardous materials. In this study, we not only ensure the risk in the network, but also consider the transportation cost and the factors such as buildings and emergency facilitie...

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Vydáno v:Journal of loss prevention in the process industries Ročník 81; s. 104954
Hlavní autoři: Wang, Yang, Roy, Nitin, Zhang, Bin
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.02.2023
Témata:
Geographic information systems (GIS)
Hazmat transportation
Mixed-integer linear programing (MILP)
Multi-objective optimization
Route optimization
Hazmat transportation
Geographic information systems (GIS)
Multi-objective optimization
Mixed-integer linear programing (MILP)
Route optimization
ISSN:0950-4230
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Shrnutí:Safety and security are of paramount importance, it is important to optimize and improve the routes of trucks that carry hazardous materials. In this study, we not only ensure the risk in the network, but also consider the transportation cost and the factors such as buildings and emergency facilities around the routes. The Geographic Information System (GIS) is used to quantify the factors on each section in the network. We present an epsilon constrained multi-objective mixed-integer linear programming optimization model to find the robust and stable transportation optimization solutions. At the end, we complete a case analysis of the proposed methodology to determine the motorway segments in Jiangsu province, China and test the above algorithm on the network, which has 144 nodes and 388 sections. The results we get show that the factors of buildings play a very important role in the model, and the multi-objective mixed-integer linear optimization model is reasonable and performs good quality. •Development and validation of the epsilon constrained multi-objective optimization model based on GIS.•Investigation of the road network information, population density and the building factors.•Using GIS data and tools to create buffers from which to build the model.•The validated model helps provide a scientific basis to optimize the route for minimizing the transportation risk and cost.
ISSN:0950-4230
DOI:10.1016/j.jlp.2022.104954