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The delivery of goods to customers is considered to be one of the most challenging activities in logistic sectors. It has a major effect on the overall costs of industrial firms as well as on the environmental resources. Transportation and distribution problems are generally modeled as a capacitated vehicle routing problems (CVRPs) since it consists in minimizing the overall cost while satisfying routing constraints and customers' ordering. Over the past five decades, the CVRP, first evoked by Dantzig and Ramser (1959), has been one of the widely studied optimization problems in logistics and supply-chain management due to both its hardness and relevance in practice (Escobar et al., 2013; Xiao et al., 2012; Fung et al., 2013). In order to better fit real life applications, the CVRP has been enriched with additional constraints, e.g., time windows to visit customers, pick-up and delivery operations and heterogeneous fleet of vehicles.
As today's logistic field is dynamic and complex, the decision maker needs interactive tools to identify the most appropriate solution. The complexity of routing problems led to the development of a new kind of an interactive computer-based system, namely the Decision Making Support System (DMSS), which aids logistic managers to achieve lower costs and greater flexibility.
Basically a DMSS aims to:
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Supply DMs with easy access to data to support poorly structured or unstructured tasks;
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Satisfy the DM needs through the embedded expert system and the modeling engine;
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Provide the requested inputs and outputs in a user friendly way;
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Display geographical features such as the transport network, workable roads and administrative boundaries.
The main contribution of this paper is the modeling and development of a routing DMSS (RDMSS) for solving the CVRP. Specifically, the framework integrates an optimization engine, based on a metaheuristic, into an open source GIS tool. The proposed approach that combines the Dijkstra and the particle swarm optimization algorithms, is tested on a set of benchmark instances and the use of the RDMSS is described in a case study.
The rest of the paper is organized as follows. Section 2 presents an overview of related works. Section 3 provides a brief description of the CVRP. Section 4 describes the architecture of the proposed DMSS. In section 5, a brief review of the proposed approach is provided in solving the CVRP by presenting its pseudocode and the simulation results. The case study, explaining the use of the RDMSS and its process flow, is provided in section 6.