In this paper, we present a routing heuristic for the Flying Sidekick Traveling Salesman Problem. This problem combines a single truck with a single drone. The drone may be launched from the truck from a customer location and can make a single package delivery before being recovered by the truck. While the drone is in flight, the truck can deliver packages independently. The problem is one of simultaneously assigning the customers to each vehicle and determining the synchronized routes for both vehicles to satisfy all customer demands in the shortest time possible. The problem is NP-hard. Our main contribution lies in developing a novel split algorithm that utilizes a shortest path approach for determining the optimal routing solution to a given order of customer locations. This split algorithm has polynomial complexity. We develop a heuristic solution to the problem by combining the split algorithm with local search techniques. This overall heuristic is shown to be accurate and computationally scalable to large, realistically sized problems applicable in last-mile logistics.

In this article, we propose a novel solution to address the current challenges related to charging an Electric Vehicle (EV). The Power on the Go: Single-drop and Double-drop problems allow an EV to be charged at a convenient location, where a service vehicle (drone), which is launched from a nearby charging station, can bring a compatible power bank at the request of the driver through an app. The objective is to reduce the cumulative wait time between service requests and the start of charging. We present mathematical formulations and two order-first split-second-based heuristic approaches for both problems. While the mathematical formulations can generate optimal solutions for small instances in a reasonable amount of time, the heuristics are fast and perform very well, with gaps < 5% for up to 20 node instances. Moreover, the formulations highlight the mean savings in wait time (29.37%) when the power bank can charge two EVs consecutively before a replacement is needed. The implications of the proposed model can be extensive, as we seek to overcome the availability and technological challenges of EV charging while advocating its adoption.

Chile’s commitment to achieve carbon neutrality by 2050 underscores the need for robust decarbonization strategies across various sectors. Despite making progress in integrating renewable energy, sectors like transportation and residential heating, which are heavily reliant on fossil fuels, present significant opportunities for decarbonization. This study develops and evaluates pathways based on Chile’s Long-Term Energy Plan for assessing the effect of flexibilities from the power-to-heat and power-to-transportation sectors. Using EnergyPLAN, we model different scenarios of Chile’s 2050 energy plan that incorporate varying levels of individual heating and electromobility and assess their impacts on excess (surplus) electricity generation, different cost metrics, and renewable energy penetration. Findings indicate that increasing flexibility within the transportation sector through smart charging and vehicle-to-grid technologies can reduce excess generation, enhance grid stability, and lower operational costs. Flexibilities in individual heating, when coupled with more renewable energy capacity, show the potential to decrease reliance on fossil fuels significantly. The evidence of major efficiency gains in Chile’s 2050 energy plan, which is achievable by investing in heating and transportation flexibilities and further augmented by the country’s unique renewable potential, should interest stakeholders. Future work will focus on optimizing these technologies to address Chile’s specific infrastructure and regulatory challenges.

This study addresses the optimization of the bottling process in the wine industry, focusing particularly on the filtration stage, which accounts for approximately 30% of the total costs in the value chain. In the context of significant global production, but with challenges arising from declining demand and increasing costs, precise planning becomes necessary to ensure product quality and operational efficiency in order to control and minimize the costs associated with resource usage. The work proposes an operational approach based on a mixed-integer programming model that optimizes the sequencing of batches on the filtration and bottling lines, integrating setup times that depend on the processing order. An analysis of prevailing practices in the Chilean wine industry is conducted, complemented by a literature review that identifies knowledge gaps, particularly regarding the influence of the wine’s physicochemical properties and the potential for filter blockage. The results of the model allow for reduced processing times and improved batch allocation, highlighting the importance of considering hydraulic and operational variables for more precise planning. Furthermore, the study discusses the limitations of the current approach and suggests future directions to continue optimizing production processes in the wine industry.