A dual-objective metaheuristic approach to solve practical pollution routing problem

With the growing public awareness of global warming, green supply chain management has received much attention. In particular, reducing the fuel consumption of trucks has been the topic of interest for both transportation providers (carriers) and manufacturing companies (shippers) over many years. As a way to reduce trucks' fuel burn, attention has been given lately to the development of a decision model (problem) that is designed to solve a variant of the standard vehicle routing problem, called the pollution routing problem (PRP), which minimizes the fuel burn or pollutants emission of trucks. While useful in many respects, existing PRP models require many inputs from the users, making it inconvenient to utilize them in the field. In this paper we consider the PRP from the users' viewpoint. We first identify, by reviewing the PRP literature and obtaining expert opinions from carrier managers, a practical PRP model that uses only a subset of the inputs required by other PRPs, and then develop an effective solution technique for this model. Based on the theoretical finding that the proposed PRP, which is a single-objective optimization problem, can be re-formulated as a dual-objective optimization problem, we develop a solution technique that utilizes the concept of efficient frontier to identify and search the most promising area of the solution space. Computational testing conducted with standard instances taken from the literature demonstrates the potential of the proposed approach.