چكيده به لاتين
The society being challenged by the advancement of technology and industry because of the respective damages inflicted. The increasing pollution caused by industrial growth results in great national, regional and global concerns. Compare with all industries transportation sector especially road transportation having the largest contribution. Therefore, the optimization of the road transportation sector can highly affect the reduction of air pollution.
In this research, an integer linear programming model is developed for Pollution-Routing Problem with Path Flexebility (PRP-PF). The proposed model tends to minimize driver and CO2 emissions costs by considering traffic congestion, which is a real issue observed in everyday urban living. To avoid congestion at rush hours or any traffic jam, we propose a novel approach, named alternate routing, to identify longer but least congested arcs and also optimize the vehicle speed in each of them. In this strategy, which has done in this paper, a trade-off is explicitly made between the mentioned costs and the longer identified alternate arc. We also develop a metaheuristic, called Alternate Routing Adaptive Large Neighborhood Search (ARALNS), tailored based on the above issue, for solving a large-scale problem. ARALNS is consist of initialization algorithm, Adaptive Large Neighborhood Search for improving the initial solution, Speed optimization Algorithm for optimizing the speed and reducing the objective value and finally, a novel method called alternate routing phase proposed to assess the alternate routing and reducing CO2 emissions.
This research is motivated by a real problem in the city of Tehran. Traffic congestion and its localized pollutants are one of the everyday struggling issues of this city and dealing with them has been a major problem. Computational experiments and analyses of results indicate that the proposed approach can lead to a reduction of greenhouse gas emissions and could be applicable for the PRPs in practice.
Keywords: Vehicle routing, Greenhouse gas emissions, Traffic congestion, Integer programming, Heuristic algorithm.
