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

Smart waste management 4.0: The transition from a systematic review to an integrated framework

Smart Waste Management (SWM) discusses the waste management process for different types of waste while introducing an intelligent approach to controlling the amount of waste. This paper introduces SWM4.0, which applies Industry4.0 (I4.0) technologies in various related events. First, the paper presents a systematic literature review on the role of I4.0 technologies in SWM activities regarding waste types, waste management processes, and 5R strategies. Then, existing solutions supporting SWM4.0 are extracted to develop a framework for exploring the use of I4.0 technologies. This framework includes sharing the four main pillars that contribute to the success of SWM4.0, namely smart people, smart cities, smart enterprises, and smart factories. Furthermore, this review suggests the possibility of unifying and extending existing solutions and identifying the necessary links and interfaces for researchers. For managerial implications, the framework identifies future strategies to fulfill specific SWM tasks and to foster new technological solutions for future research.

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

With the development of the express delivery industry, how to increase the recycling rate of waste cartons has become a problem that needs to be solved. Recycling enterprises began to provide the new recycling mode, door-to-door recycling services, to residents with waste cartons. In this article, we constructed a site selection model for a carton recycling site with the aim of maximizing total profits. Considering the residents' recycling willingness and the government subsidy earned through the contribution to carbon emission reduction, this model achieves the task of site selection and unit price fixation for carton recycling. We used the particle swarm optimization (PSO) algorithm to solve the model and compared it with the genetic algorithm (GA) for validity testing. PSO algorithm was also used to carry out sensitivity analysis in this model. The proposed model and the results of the sensitivity analysis can be used for decision-making in recycling enterprises as well as for further research on waste recycling and reverse logistics.

Quantifying odour impacts from aged organic waste to be considered as a priority constraint in route optimization for waste collection trucks

Route optimization has been used for years to plan the routes for municipal solid waste (MSW) collection trucks to achieve cost reductions. Historically, optimized routes had overlooked a number of aspects and parameters in their design. This study aims to consider MSW odour detection as a performance indicator and a priority constraint in the optimization process by quantifying the impact of objectionable odours from uncollected aged MSW that contains a high percentage of food waste (typically called wet garbage). Odours from 48 aged food waste samples were rated on a scale from 0 to 3 to mark the beginning of the critical time of objectionable odour detection. The critical time was found to take place approximately at the hour 13.6, which was then used, along with the estimated food waste weight in the bin, to define the beginning of a time window that puts the bin on a high priority status for collection over the other, less odoriferous bins. Three optimization scenarios for collection of 100 MSW bins in the city of Madaba, Jordan, were conducted under different constraints: least travelled distance, maximum collected volume and least odour impact. Without the application of the odour consideration, a total travelled distance of 143 km was the shortest travelled distance achieved, with 53 bins emitting odours and leaving 81 m³ of uncollected waste. However, when odour impact was the main routing constraint, a total travelled distance of 161 km was needed and 13 m³ of waste was left uncollected.