Multi-Robot Pickup and Delivery via Distributed Resource Allocation

In this article, we consider a large-scale instance of the classical pickup-and-delivery vehicle routing problem that must be solved by a network of mobile cooperating robots. Robots must self-coordinate and self-allocate a set of pickup/delivery tasks while minimizing a given cost figure. This results in a large, challenging mixed-integer linear problem that must be cooperatively solved without a central coordinator. We propose a distributed algorithm based on a primal decomposition approach that provides a feasible solution to the problem in finite time. An interesting feature of the proposed scheme is that each robot computes only its own block of solution, thereby preserving privacy of sensible information. The algorithm also exhibits attractive scalability properties that guarantee solvability of the problem even in large networks. To the best of our knowledge, this is the first attempt to provide a scalable distributed solution to the problem. The algorithm is first tested through Gazebo simulations on a ROS 2 platform, highlighting the effectiveness of the proposed solution. Finally, experiments on a real testbed with a team of ground and aerial robots are provided

Programación de carrotanques de transporte de hidrocarburos mediante una búsqueda tabú

Como proyecto aplicativo de conocimientos de ingeniería, se evidenció una posible oportunidad de mejora de procesos en el sector de hidrocarburos de Colombia. Abordando el problema en específico, se observó que los métodos aplicados para la selección de las rutas del transporte terrestre de crudo no aplicaban un proceso óptimo, incurriendo en costos elevados en este rubro. Para poder entender el problema, se tiene en cuenta que en el sector petrolero existen puntos de extracción de crudo con el fin de ser transportado a refinerías u otras ubicaciones donde se procede a destilarlo o a enviarlo a otros puntos por otros medios de transporte. Al evidenciar esta oportunidad, se procedió a evaluar la posibilidad de implantar un sistema de ahorro de costos el cual beneficiará al sector, además de poder planear con antelación sus rutas y saber cómo se mueve su sistema. Luego de esta evaluación y búsqueda bibliográfica, se procedió a realizar una meta heurística para la asignación de rutas que ahorren costos y una simulación para medir el rendimiento de los resultados que se obtienen de la meta heurística, encontrando resultados para el problema. Finalmente se procede a evaluar el modelo frente a los objetivos planteados de ahorro dando como resultado un modelo de ahorro de costos factible.As an application project of knowledge in engineering, it was evidenced a possible process improvement opportunity in the Colombian hydrocarbons sector. Considering that in the Oil field there are locations where the crude oil is extracted in order to be transported to refineries or other places where it is distilled or sent to different locations by other transport means. Therefore, the problem that is going to be solved is a variant of the well know multi-depot vehicle routing problem, with a homogenous fleet, under the constraints of minimum amount of tank-trucks hired from available companies. After searching for bibliographical sources regarding the observed situation, it was proceeded to define a mathematical model then, design and elaborate a hybrid Tabu Search to determine how many tank-trucks to hire and the respective routes for each one. Finally, to be able to measure the performance of the result obtained with the application, in contrast with the current method, a simulation of the routes network for the tankers was made. The feasibility of the solution under random scenarios was set as a performance indicator. The method proposed in this project achieved to improve the current process in an 18.77%, reducing the transportation cost by $ 3.465.065.575.Ingeniero (a) IndustrialPregrad

Risk Assessment and Collaborative Information Awareness for Plan Execution

Joint organizational planning and plan execution in risk-prone environment, has seen renewed research interest given its potential for agility and cost reduction. The participants are often asked to quickly plan and execute tasks in partially known or hostile environments. This requires advanced decision support systems for situational response whereby state-of-the-art technologies can be used to handle issues such as plan risk assessment, appropriate information exchange, asset localization and adaptive planning with risk mitigation. Toward this end, this thesis contributes innovative approaches to address these issues, focusing on logistic support over risk-prone transport network as many organizational plans have key logistic components. Plan risk assessment involves property evaluation for vehicle risk exposure, cost bounds and contingency options assessment. Appropriate information exchange involves participant specific shared information awareness under unreliable communication. Asset localization mandates efficient sensor network management. Adaptive planning with risk mitigation entails limited risk exposure replanning, factoring potential vehicle and cargo loss. In this pursuit, this thesis first investigates risk assessment for asset movement and contingency valuation using probabilistic model-checking and decision trees, followed by elaborating a gossip based protocol for hierarchy-aware shared information awareness, also assessed via probabilistic model-checking. Then, the thesis proposes an evolutionary learning heuristic for efficiently managing sensor networks constrained in terms of sensor range, capacity and energy use. Finally, the thesis presents a learning based heuristic for cost effective adaptive logistic planning with risk mitigation. Instructive case studies are also provided for each contribution along with benchmark results evaluating the performance of the proposed heuristic techniques

Collaborative Planning and Event Monitoring Over Supply Chain Network

The shifting paradigm of supply chain management is manifesting increasing reliance on automated collaborative planning and event monitoring through information-bounded interaction across organizations. An end-to-end support for the course of actions is turning vital in faster incident response and proactive decision making. Many current platforms exhibit limitations to handle supply chain planning and monitoring in decentralized setting where participants may divide their responsibilities and share computational load of the solution generation. In this thesis, we investigate modeling and solution generation techniques for shared commodity delivery planning and event monitoring problems in a collaborative setting. In particular, we first elaborate a new model of Multi-Depot Vehicle Routing Problem (MDVRP) to jointly serve customer demands using multiple vehicles followed by a heuristic technique to search near-optimal solutions for such problem instances. Secondly, we propose two distributed mechanisms, namely: Passive Learning and Active Negotiation, to find near-optimal MDVRP solutions while executing the heuristic algorithm at the participant's side. Thirdly, we illustrate a collaboration mechanism to cost-effectively deploy execution monitors over supply chain network in order to collect in-field plan execution data. Finally, we describe a distributed approach to collaboratively monitor associations among recent events from an incoming stream of plan execution data. Experimental results over known datasets demonstrate the efficiency of the approaches to handle medium and large problem instances. The work has also produced considerable knowledge on the collaborative transportation planning and execution event monitoring