The municipal solid waste (MSW) collection and transportation issue has been studied by numerous researchers; however, a few studies consider the chance-constrained programming for co-collection of sorted waste with electric vehicles (EVs). Therefore, this article attempts to study on the chance-constrained collection and transportation problem for sorted waste with multiple separated compartments EVs. Considering the uncertainty of the waste generation rate under the scenario of application of smart waste bins, chance-constrained programming is applied to transform the uncertain model into a certain one. A Chance-Constrained Multi-Compartment Electric Vehicle Routing Problem (CCMCEVRP) is introduced and the corresponding mathematical formulation is established. A diversity-enhanced particle swarm optimisation with neighbourhood search and simulated annealing (DNSPSOSA) is proposed to solve this problem, and effectiveness of the proposed algorithms is verified by extensive numerical experiments on the newly generated instances. In addition, the application of the model is tested by comparing different compartment and different type vehicles. It is found that, compared with fuel vehicles, 32.66% of the average cost could be saved with EVs. Furthermore, the rate of cost-saving of EVs increases with the increase in the number of compartments: the improvement rate of cost-saving of two-compartment EVs and three-compartment EVs is 52.77% and 68.13%, respectively.
Keywords: Waste collection and transportation; chance-constrained; electric vehicles; multi-compartment; particle swarm optimisation; simulated annealing; sorted waste.