Multi-factorial evolutionary algorithm based novel solution approach for multi-objective pollution-routing problem. (April 2019) - Rauniyar, Amit; Nath, Rahul; Muhuri, Pranab K.
Highlights: A novel formulation of the multi-objective pollution routing problem (PRP) and its solution by implementing integer-encoded NSGA-II. Designing of a Multi-Factorial Evolutionary Algorithm (MFEA) based novel solution approach for simultaneous optimization of distance of routes for the multi-objective PRP. Simulation experiments using benchmark datasets. Thorough comparative analysis in terms of objective values and well-known performance measures. Abstract: The rapid increase in transportation has led to alarming levels of pollution globally, which has, in turn adverse effects on both the environment and the health of people. This has motivated researchers to develop efficient solutions for limiting the fuel consumption of vehicles so that greenhouse gas emission can be reduced. The pollution emitted by a vehicle depends primarily on two controllable factors viz. load and distance traveled. This paper considers a Pollution-Routing Problem (PRP) formulation with two objectives, minimization of fuel consumption (CO2 emissions), and minimization of total distance to be traversed, and proposes a novel solution based on the well-known Non-Dominated Sorting Genetic Algorithm-II (NSGA-II). Since the problem requires optimization of several routes formed at the same time, traditional NSGA-II frameworks are incapable of handling it efficiently. Thus, we incorporate a new paradigm of evolutionary algorithm, called multi-factorial optimization into NSGA-II to solve the problem of several routes generated simultaneously. The results of our experiments with benchmark datasets confirm the feasibility of the proposed approach with better solutions and faster convergence.