Optimisation of sustainable urban recycling waste collection and routing with heterogeneous electric vehicles

Abstract

Solid waste management faces increasing challenges due to rapid urbanisation and population growth. In addition, the different types of waste sources are scattered in different geographical regions within the city increases the transportation and collection costs, as well as increases emissions. Therefore, a cost-effective and environmentally friendly solution can be found to optimise waste collection procedures and transportation operations. This study introduces the electric waste collection problem (EWCP) in which a heterogeneous fleet of electric vehicles has to be assigned to carry out a number of visits to the places where the waste bins are located. This problem is a generalisation of the well-known vehicle routing problem. We consider multiple types of wastes, time windows, multi-compartment, split deliveries, and waste bin-vehicle compatibility. This paper aims to optimise waste collection and transportation operations in a sustainable way. We mathematically formulate the problem as a mixed-integer programming (MIP) model and develop an adaptive variable neighbourhood search (AVNS) to solve the EWCP efficiently. We have generated a new instance set for the problem based on the real-life case study and conducted extensive computational experiments with our extended heuristic. The results indicate that the AVNS is highly effective compared to the MIP model with small-size instances. Our obtained results suggest that using an electric vehicle fleet in waste collection operations will help reduce the total travel costs and harmful gas emissions. • A new electric waste collection problem with multiple types of wastes is addressed. • Constraints such as split deliveries and bin-vehicle compatibility are considered. • An adaptive variable neighbourhood search heuristic is offered to solve the problem efficiently. • Extensive computational experiments are conducted for several insights. • The impact of the fleet mix and traffic congestion are also explored in the study.