Showing papers by "Ana Paula Barbosa-Póvoa published in 2023"
TL;DR: In this paper , the authors explore the economic and the environmental impacts of the VRPDDP, with and without restrictions on the free capacity, and compare it with the traditional Vehicle Routing Problem with Simultaneous Deliveries and Pickups (VRPSDP), on savings achieved by splitting customers visits.
Abstract: The Vehicle Routing Problem with Divisible Deliveries and Pickups (VRPDDP) is under-explored in the literature, yet it has a wide application in practice in a reverse logistics context, where the collection of returnable items must also be ensured along with the traditional delivery of products to customers. The problem considers that each customer has both delivery and pickup demands and may be visited twice in the same or different routes (i.e., splitting customers’ visits). In several reverse logistics problems, free capacity restrictions are required to either allow the movement of the driver inside the vehicle to rearrange the loads or to avoid cross-contamination between delivery and pickup loads. In this work, we explore the economic and the environmental impacts of the VRPDDP, with and without restrictions on the free capacity, and compare it with the traditional Vehicle Routing Problem with Simultaneous Deliveries and Pickups (VRPSDP), on savings achieved by splitting customers visits. An exact method, solved through Gurobi, and an ALNS metaheuristic are coded in Python and used to test well-known and newly generated instances. A multi-objective approach based on the augmented ϵ-constraint method is applied to obtain and compare solutions minimizing costs and CO2 emissions. The results demonstrate that splitting customer visits reduces the CO2 emissions for load-constrained distribution problems. Moreover, the savings percentage of the VRPDDP when compared to the VRPSDP is higher for instances with a random network than when a clustered network of customers is considered.
2 citations
TL;DR: In this article , a scenario-based approach built upon a mixed-integer linear programming (MILP) formulation is proposed aiming at designing and planning, under demand uncertainty, a sugar-bioethanol supply chain network whose harvesting, production, storage, and distribution activities are integrated.
Abstract: In today’s society, the so-called green consciousness about environmental issues has triggered an intense search for renewable energy sources. Bioenergy, obtained by transformation processes of biomass such as castor bean or sugar cane, plays a decisive role in this context, providing much of the energy used in the electricity production, heat supply, and transport sector. Sugar cane, in particular, has assured considerable economic relevance due to its multiple applications. It is commonly employed as fodder for animal feed or as raw material for producing electricity, bioethanol, sugar, molasses, and other bioproducts. Currently, the integrated management of all stages of the transformation processes of this biomass has become a major challenge due to the complex interactivity and exchanges between all the actors present in the logistics chain. In this work, a scenario-based approach built upon a mixed-integer linear programming (MILP) formulation is proposed aiming at designing and planning, under demand uncertainty, a sugar-bioethanol supply chain network whose harvesting, production, storage, and distribution activities are integrated. The model’s optimization objective is to maximize the expected net present value (ENPV) when deciding on the location, size, and technologies of industrial parks and storage sites, the size of truck fleets, which markets to serve, and inventory levels, among other important issues. The adequacy and efficiency of the MILP model are illustrated through a case study based on the Brazilian sugar-bioethanol industry.
TL;DR: In this paper , the authors present an exploratory analysis of two waste bin monitoring approaches: (1) ultrasonic sensors installed in the bins and (2) visual observations (VO) of the waste collection truck drivers.
Abstract: Waste bin monitoring solutions are an essential step towards smart cities. This study presents an exploratory analysis of two waste bin monitoring approaches: (1) ultrasonic sensors installed in the bins and (2) visual observations (VO) of the waste collection truck drivers. Bin fill level data was collected from a Portuguese waste management company. A comparative statistical analysis of the two datasets (VO and sensor observations) was performed and a predictive model based on Gaussian processes was applied to enable a trade-off analysis of the number of collections versus the number of overflows for each monitoring approach. The results demonstrate that the VO are valuable and reveal that significant improvements can be achieved for either of the monitoring approaches in relation to the current situation. A monitoring approach based on VO combined with a predictive model is shown to be viable and leads to a considerable reduction in the number of collections and overflows. This approach can enable waste collection companies to improve their collection operations with minimal investment costs during their transition to fully sensorized bins.