First-mile reverse logistics: An agent-based modelling and simulation application for glass bottle recovery

Distribution networks aim to get the right products to consumers at the right time and in the right place. When consumers return these products, another type of logistics network is involved. This function of returning end-of-life products is referred to as reverse logistics. A critical characteristic of these reverse logistics networks is the dependence of material flow on the voluntary action of the consumer. Therefore, the success of an end-of-life collection strategy depends on the ability to anticipate the collection rate of end-of-life products at specific locations by understanding the factors that influence consumer behavior. However, there is no established tool or methodology for assessing the impact of consumer decisions on reverse logistics material flows. Therefore, this paper proposes an agent-based simulation tool to predict the performance of a reverse logistics network, taking into account the location of collection points, socio-demographic information, attitudes towards sustainable development, as well as the consumption profile. More specifically, this paper presents the configuration, calibration and validation of a simulation tool modelling the return of empty glass bottles in a small group of municipalities in Québec, Canada. Experiments also demonstrate the tool's ability to provide useful information to support the design of collection strategies that maximize return rates while minimizing installation and operating costs by optimizing the number of drop-off depots in the reverse logistics network.