Pickup and Delivery Problem (PDP) consists of searching an optimal set of vehicles and an optimal set of routes, one route by each vehicle, in order to pickup items from a set of origins and deliver them to another set of destinations. Pickup and delivery problem is a class of complex systems whose complexity is NP Hard. In PDP real life applications, heuristics and meta heuristics methods are used in order to obtain an acceptable solution in reasonable execution time. When unpredictable events, like for example path cut and vehicles failure, may occur during the PDP schedule execution, we say that the PDP is dynamic (DPDP) and in this case we have to revise this schedule. In this paper, we propose a multi agent architecture for DPDP based on an organizational architecture. Supported by a formal framework, the proposed architecture allows us to show, through a case study that computed solution for the studied problem could be done in a parallel manner which attenuates substantially the problem complexity.
ACM CCS (2012) Classification
In this work, we propose a new approach for coordinating generated agents’ plans dynamically. The purpose is to take into consideration new conflicts introduced in new versions of agents’ plans. The approach consists in finding the best combination which contains one plan for each agent among its set of possible plans whose execution does not entail any conflict. This combination of plans is reconstructed dynamically, each time agents decide to change their plans to take into account unpredictable changes in the environment. This not only ensures that new conflicts are likely to be introduced in the new plans that are taken into account but also it allows agents to deal, solely, with the execution of their actions and not with the resolution of conflicts. For this, we use genetic algorithms where the proposed fitness function is defined based on the number of conflicts that agents can experience in each combination of plans. As part of our work, we used a concrete case to illustrate and show the usefulness of our approach.
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