Hybrid Decision Support for the Logistics of Refuse Collection in a Large Metropolitan Area

P. Giribone, A. Orsoni, and R. Revetria (Italy)


Decision Support Systems, Genetic Algorithms, Transportation Logistics, Urban Refuse Collection, Logistic Re-Engineering, Optimization Techniques.


This paper proposes a decision support system (DSS) for scenario testing and optimization of the logistics of the refuse collection service in a large metropolitan area. A hybrid architecture was chosen for the development of the DSS, which combines the scenario-testing strength of simulation with the “propositive” capabilities of genetic algorithms (GAs) to iteratively generate and test continuously improved sets of logistic solutions, until an optimum is found. The structure and features of the DSS are outlined in the paper with reference to a case-study on the re-engineering of the refuse collection process in the Greater Genoa Area. The application of the DSS to the case-study leads to the identification of the allocation policy of minimum cost for the current pool of available resources, and to the formulation of guidelines and recommendations for future investments aimed at achieving the most effective mix of resources.

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