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Multi-Layer Distributed Constraint Satisfaction for Multi-criteria Optimization Problem: Multimodal Transportation Network Planning Problem

Multi-Layer Distributed Constraint Satisfaction for Multi-criteria Optimization Problem: Multimodal Transportation Network Planning Problem

Mouna Gargouri Mnif, Sadok Bouamama
Copyright: © 2020 |Volume: 11 |Issue: 2 |Pages: 22
ISSN: 1947-8283|EISSN: 1947-8291|EISBN13: 9781799802853|DOI: 10.4018/IJAMC.2020040107
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MLA

Mnif, Mouna Gargouri, and Sadok Bouamama. "Multi-Layer Distributed Constraint Satisfaction for Multi-criteria Optimization Problem: Multimodal Transportation Network Planning Problem." IJAMC vol.11, no.2 2020: pp.134-155. http://doi.org/10.4018/IJAMC.2020040107

APA

Mnif, M. G. & Bouamama, S. (2020). Multi-Layer Distributed Constraint Satisfaction for Multi-criteria Optimization Problem: Multimodal Transportation Network Planning Problem. International Journal of Applied Metaheuristic Computing (IJAMC), 11(2), 134-155. http://doi.org/10.4018/IJAMC.2020040107

Chicago

Mnif, Mouna Gargouri, and Sadok Bouamama. "Multi-Layer Distributed Constraint Satisfaction for Multi-criteria Optimization Problem: Multimodal Transportation Network Planning Problem," International Journal of Applied Metaheuristic Computing (IJAMC) 11, no.2: 134-155. http://doi.org/10.4018/IJAMC.2020040107

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Abstract

This article introduces a new approach to solve the multimodal transportation network planning problem (MTNP). In this problem, the commodities must be transported from an international network by at least two different transport modes. The main purpose is to identify the best multimodal transportation strategy. The present contribution focuses on efficient optimization methods to solve MTNP. This includes the assignment and the scheduling problems. The authors split the MTNP into layered. Each layer is presented by an agent. These agents interact, collaborate, and communicate together to solve the problem. This article defines MTNP as a distributed constraint satisfaction multi-criteria optimization problem (DCSMOP). This latter is a description of the constraint optimization problem (COP), where variables and constraints are distributed among a set of agents. Each agent can interact with other agents to share constraints and to distribute complementary tasks. Experimental results are the proof of this work efficiently.

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