Dynamic routing for waste collection and transportation with multi-compartment electric vehicle using smart waste bins

Abstract

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.

Demonstration of a contactless waste collection system: A Japanese case study

“…The wireless nature of smart bins requires an optimal transportation route to warrant lower cost and energy consumption [21][22][23]. Furthermore, using data analytics and IoT technology-based optimisation strategies, the collection frequency can also be based on temperature to avoid smell.…”

An electric vehicle routing model with charging stations consideration for sustainable logistics

“…(2022) proposed a bi-level ant colony optimization approach for the capacitated electric vehicle routing problem, while Karakatic (2021) developed a two-layer genetic algorithm to solve the capacitated electric vehicle routing problem considering multi distribution centers and time windows. Literature reveals that heuristic algorithms used for resolving the electric vehicle routing problem include genetic algorithm (Karakatic, 2021; Moazzeni et al ., 2022), ant colony algorithm (Leite et al ., 2022; Mao et al ., 2020) and particle swarm optimization (PSO) (Yang et al ., 2022; Zhen et al ., 2020). However, there is currently a lack of discussion on the application of novel heuristic algorithms in the field of electric vehicle routing problems.…”

Optimization of carton recycling site selection using particle swarm optimization algorithm considering residents’ recycling willingness

“… Shen & Chen (2017) proposed a multi-depot model and used PSO algorithm to solve its location and path optimization problems. Yang, Tao & Zhong (2022) improved the particle swarm optimization algorithm by introducing diversity enhancement mechanism and neighborhood search mechanism, and applied it in the optimization of urban wet waste recovery path optimization.…”

“…PSO has the advantages of fewer required parameters, easy particle coding, fast convergence, and strong universality. Its optimization is mainly based on the fitness function, which is very suitable for solving complex optimization problems ( Wang et al, 2022 ; Yang, Tao & Zhong, 2022 ; Huang, Li & Liu, 2018 ), and it is an ideal method to solve NP-hard problems such as the carton recycling site selection problem.…”