A satellite navigation system to improve the management of intermodal drayage

The intermodal transport chain can become more efficient by means of a good organization of the drayage movements. Drayage in intermodal container terminals involves the pick up or delivery of containers at customer locations, and the main objective is normally the assignment of transportation tasks to the different vehicles, often with the presence of time windows. The literature shows some works on centralised drayage management, but most of them consider the problem only from a static and deterministic perspective, whereas the work we present here incorporates the knowledge of the real-time position of the vehicles, which permanently enables the planner to reassign tasks in case the problem conditions change. This exact knowledge of position of the vehicles is possible thanks to a geographic positioning system by satellite (GPS, Galileo, Glonass), and the results show that this additional data can be used to dynamically improve the solution

A evolutionary algorithm for dynamically optimisation of drayage operations

Proper planning of drayage operations is fundamental in the quest for the economic viability of intermodal freight transport. The work we present here is a dynamic optimization model which uses real-time knowledge of the fleet's position, permanently enabling the planner to reallocate tasks as the problem conditions change. Stochastic trip times are considered, both in the completion of each task and between tasks

A Genetic Algorithm for Real-Time Optimisation of Drayage Operations

Proper planning of drayage operations is fundamental in the quest for the economic viability of intermodal freight transport. The work we present here is a dynamic optimization model which uses real-time knowledge of the fleet’s position, permanently enabling the planner to reallocate tasks as the problem conditions change. Stochastic trip times are considered, both in the completion of each task and between tasks

Dynamic optimisation of urban intermodal freight transport with random transit times, flexible tasks and time windows

Es una ponencia de The Sixth International Conference on City Logistics, en Puerto Vallarta, México http://toc.proceedings.com/18996webtoc.pdfAn improvement on drayage operations is necessary for intermodal freight transport to become competitive. When drayage takes place in cities or urban centres transit times are usually random, as a consequence finding the optimal fleet schedule is very difficult, and this schedule can even change during the day. The work we present here is a dynamic optimisation model which uses real-time knowledge of the fleet’s position, permanently enabling the planner to reallocate tasks as the problem conditions change. Stochastic trip times are considered, both in the completion of each task and between tasks. Tasks can also be flexible or well-defined. We describe the algorithm in detail for a test problem and then apply it to a set of random drayage problems of different size and characteristics, obtaining significant cost reductions with respect to initial estimates.Junta de Andalucía SR0197/200

Impacto del conocimiento en tiempo real de la posición de una flota de vehículos sobre la mejora de acarreo intermodal

Este artículo resuelve el problema del acarreo en una red con enlaces cuya duración es estocástica. Y pone de relieve la importancia del conocimiento en tiempo real de la posición exacta de los vehículos de la flota para llevar a cabo una correcta asignación de tareas a la misma. El modelo tiene un carácter dinámico al ser capaz de reasignar tareas a medida que se conocen datos con mayor certeza

Working Notes from the 1992 AAAI Spring Symposium on Practical Approaches to Scheduling and Planning

The symposium presented issues involved in the development of scheduling systems that can deal with resource and time limitations. To qualify, a system must be implemented and tested to some degree on non-trivial problems (ideally, on real-world problems). However, a system need not be fully deployed to qualify. Systems that schedule actions in terms of metric time constraints typically represent and reason about an external numeric clock or calendar and can be contrasted with those systems that represent time purely symbolically. The following topics are discussed: integrating planning and scheduling; integrating symbolic goals and numerical utilities; managing uncertainty; incremental rescheduling; managing limited computation time; anytime scheduling and planning algorithms, systems; dependency analysis and schedule reuse; management of schedule and plan execution; and incorporation of discrete event techniques

Techniques for the allocation of resources under uncertainty

L’allocation de ressources est un problème omniprésent qui survient dès que des ressources limitées doivent être distribuées parmi de multiples agents autonomes (e.g., personnes, compagnies, robots, etc). Les approches standard pour déterminer l’allocation optimale souffrent généralement d’une très grande complexité de calcul. Le but de cette thèse est de proposer des algorithmes rapides et efficaces pour allouer des ressources consommables et non consommables à des agents autonomes dont les préférences sur ces ressources sont induites par un processus stochastique. Afin d’y parvenir, nous avons développé de nouveaux modèles pour des problèmes de planifications, basés sur le cadre des Processus Décisionnels de Markov (MDPs), où l’espace d’actions possibles est explicitement paramétrisés par les ressources disponibles. Muni de ce cadre, nous avons développé des algorithmes basés sur la programmation dynamique et la recherche heuristique en temps-réel afin de générer des allocations de ressources pour des agents qui agissent dans un environnement stochastique. En particulier, nous avons utilisé la propriété acyclique des créations de tâches pour décomposer le problème d’allocation de ressources. Nous avons aussi proposé une stratégie de décomposition approximative, où les agents considèrent des interactions positives et négatives ainsi que les actions simultanées entre les agents gérants les ressources. Cependant, la majeure contribution de cette thèse est l’adoption de la recherche heuristique en temps-réel pour l’allocation de ressources. À cet effet, nous avons développé une approche basée sur la Q-décomposition munie de bornes strictes afin de diminuer drastiquement le temps de planification pour formuler une politique optimale. Ces bornes strictes nous ont permis d’élaguer l’espace d’actions pour les agents. Nous montrons analytiquement et empiriquement que les approches proposées mènent à des diminutions de la complexité de calcul par rapport à des approches de planification standard. Finalement, nous avons testé la recherche heuristique en temps-réel dans le simulateur SADM, un simulateur d’allocation de ressource pour une frégate.Resource allocation is an ubiquitous problem that arises whenever limited resources have to be distributed among multiple autonomous entities (e.g., people, companies, robots, etc). The standard approaches to determine the optimal resource allocation are computationally prohibitive. The goal of this thesis is to propose computationally efficient algorithms for allocating consumable and non-consumable resources among autonomous agents whose preferences for these resources are induced by a stochastic process. Towards this end, we have developed new models of planning problems, based on the framework of Markov Decision Processes (MDPs), where the action sets are explicitly parameterized by the available resources. Given these models, we have designed algorithms based on dynamic programming and real-time heuristic search to formulating thus allocations of resources for agents evolving in stochastic environments. In particular, we have used the acyclic property of task creation to decompose the problem of resource allocation. We have also proposed an approximative decomposition strategy, where the agents consider positive and negative interactions as well as simultaneous actions among the agents managing the resources. However, the main contribution of this thesis is the adoption of stochastic real-time heuristic search for a resource allocation. To this end, we have developed an approach based on distributed Q-values with tight bounds to diminish drastically the planning time to formulate the optimal policy. These tight bounds enable to prune the action space for the agents. We show analytically and empirically that our proposed approaches lead to drastic (in many cases, exponential) improvements in computational efficiency over standard planning methods. Finally, we have tested real-time heuristic search in the SADM simulator, a simulator for the resource allocation of a platform