An innovative waste management system in a smart city under stochastic optimization using vehicle routing problem

A smart city (SC) is a sustainable and efficient urban center that provides a high quality of life to its inhabitants through optimal management of its resources that nowadays have been wider and wider. In modern societies, municipal solid waste management (MSWM) is an important part of SCs, the main problem of MSWM is the cost that it generates and must be reduced. To solve this situation in this paper are considered two sub-models. The first sub-model uses vehicle routing problem (VRP) for routing fleet among generation waste to separation facilities. The second sub-model is designed to allocate resources from separation facilities to set of recovery plants or landfill centers. From the best of our knowledge, most of the past studies related to this topic have focused only on deterministic implementations. Also, recent studies usually focus on uncertain parameters in the area of waste generation. In addition, a few related studies have developed the uncertain parameter which has focused on facilitating separation. This study considers the uncertain parameters in the output rate of separation facilities as well as the importance of value recovery from each bin; the aim is to enhance the efficiency of operations. The purpose of this study is to minimize the total transportation cost and to maximize recycled revenue. Chance-constrained programming has been used to deal with stochastic optimization model. Four metaheuristic algorithms are employed to identify the best solution. Besides, the performance of the proposed algorithms is evaluated. Finally, sensitivity analyses along with number of scenarios have developed to measure the tightness of the proposed problem. The results of the study illustrate the optimized number of vehicles that can help the managers and decision-makers in various tightness conditions.