Authors:
Madalena S. Rodrigues
;
Marta C. Gomes
;
Alexandre B. Gonçalves
and
Sílvia Shrubsall
Affiliation:
Universidade de Lisboa, Portugal
Keyword(s):
Hazardous Materials Transportation, Bi-level Linear Programming, Road Safety in Urban Areas, Geographical Information Systems (GIS).
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Operational Research
;
OR in Transportation
;
Pattern Recognition
;
Software Engineering
Abstract:
Hazardous materials (hazmats) are essential for the competitiveness of contemporary societies, however their transportation is potentially dangerous and expensive. In Portugal, despite the interest of both academia and industry, studies enabling the identification of preferable road routes for hazmats distribution were not identified. Hence, this research aims at contributing to advancing knowledge in identifying these routes in the national current context by balancing two frequently intrinsically conflicting aspects of hazmats transportation: the safety and the economic viability of the available routes. For that, a bi-level linear programming model was implemented in the GAMS modelling system and applied to a real-world case study using petrol and diesel fuels delivery data from a prominent energy group acting in the country. The company shared data of distribution loads over one calendar year to both petrol stations and direct clients in Lisbon. A geographical information system
(GIS) was used to map Lisbon road network, which was found to be significantly larger than other networks used in similar studies described in the literature. The model was solved to optimality in a short computation time leading to the clear identification of the preferable road routes for liquid fuel distribution in the Lisbon district of Olivais. The success of the methodology applied in this study, including the generic implementation of the bi-level linear programming model, offers an optimistic prospect for a gradual increase of the geographical coverage, assessed risks and general complexity of the initial model.
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