A New Multi-Layer Distributed Approach for a Multi-objective Planning Problem
Author(s) -
Mouna Mnif,
Sadok Bouamama
Publication year - 2019
Publication title -
procedia computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.334
H-Index - 76
ISSN - 1877-0509
DOI - 10.1016/j.procs.2019.09.311
Subject(s) - computer science , mathematical optimization , convergence (economics) , computational complexity theory , planner , optimization problem , layer (electronics) , constraint (computer aided design) , distributed computing , multi agent system , artificial intelligence , algorithm , mathematics , chemistry , organic chemistry , economics , economic growth , geometry
This paper introduces a new distributed approach to solve multi-objective planning problem applied to multimodal transport network planning (MTNP) problem. In this problem, the commodities should be transported on the international network by at least two different transport modes. The main goal is to find the best multimodal transportation modes and itineraries. The aim of the new approach has assured us that a distributed optimization. We split the MTNP problem into two sub-problems. These sub-problems are the assignment and the planning problems. Each sub-problem is solved at a corresponding layer. Each layer is executed by an agent. These agents interact, collaborate and communicate together to solve the MTNP problem. In this paper, we contribute by introducing a multi-layer distributed approach to solve real case’s problems. Firstly, we define the MTNP problem as a distributed constraint satisfaction multi-criteria optimization problem (DCSMOP). Secondly, we show that the split of the main problem reduces the computational complexity and the communication between the planner agent and the modes agents lead to faster convergence. The experimental results are proof of this work efficiently. This method proves their efficiency, according to the complexity of the problem and the exchange of information, the computational time and the solution quality.
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