A comprehensive study on time-dependent multi-objective sustainable vehicle routing problem considering real-time traffic congestion

Abstract

Although routing transportation is a major issue due to increased traffic congestion, adequate attention has not been paid in the past studies on important real-life factors like actual route distances, real-time vehicle speeds, and load-dependent varying engine efficiency during transportation planning. Therefore, the present study proposes a mixed integer programming model of a capacitated vehicle routing and scheduling problem to develop a sustainable transportation framework for practical decisiveness, considering real congestion scenarios. The study introduces a novel generalized speed function that captures the varying daily traffic conditions, incorporating heterogeneous vehicles with hard time windows. Classical ant colony optimization (ACO) is applied as the solution methodology, integrating Pareto optimization, and multi-criteria optimization and compromise solution (VIKOR – in Serbian abbreviation) to solve the multi-objective optimization problem and obtain the compromise solution. The effectiveness of the proposed model is validated by conducting a case study based on a real-world rural distribution network. A combined approach of partial rank correlation coefficient (PRCC) and regression analysis has been conducted to check the sensitivity of the objectives.