A novel solid waste instance creation for an optimized capacitated vehicle routing model using discrete smell agent optimization algorithm

This paper presents an optimal vehicle routing model for an efficient waste collection process using the Ogun State Waste Management Agency (OGWAMA) as a case study. Just like in many cases, the current manual predetermined routing method used by OGWAMA is inefficient and contributes to excessive fuel usage. These challenges, in addition to the small instances reported in most literature, inspire this research to propose an improved routing scheme that takes into account real-time costs and eventually develops a novel instance based on OGWAMA's operation mode. The developed model was optimized using a new discrete smell agent optimization (SAO) algorithm and compared to firefly algorithm (FA) and particle swarm optimization (PSO). The SAO outperformed FA and PSO, achieving 3.92% and 19.38% improvements in service cost (SC) and 2.65% and 14.96% improvements in total travel distance (TTD), respectively. The convergence rates of the algorithms were also compared; using the OD techniques and results shows the acceptability of the proposed approaches.