The paper illustrates a comprehensive procedure for the design of a delivery scheme in an urban area, where a set of possible locations for logistic transit-points exists. In such a scheme it is assumed that deliveries for Business-to-Consumer (BtoC) e-commerce of goods are performed at specific drop-points, suitably selected to match the pick-up of the parcels to usual activities of the customers, like the breakfast at the bar or the purchase of the newspaper. The solution procedure, which integrates transportation system theory and operational research techniques, applies a disaggregate Nested Logit Model (NLM) for demand estimation, an Analytic Hierarchy Process (AHP) to compare and select possible drop-points, and a double string Genetic Algorithm (GA) to solve jointly both the problems of transit-point location and sizing and of drop-point clustering for deliveries tours. A GA performance function is computed by solving a standard Travel Salesman Problem (TSP) on the road graph, whose travel times have been estimated by assigning the O/D matrix of car trips. The first application of the procedure to the town of Terni, in Italy, has provided very encouraging results.
Last-mile, a Procedure to set up an Optimized Delivery Scheme / Fusco, Gaetano; L., Tatarelli; M. P., Valentini. - STAMPA. - (2004), pp. 147-161. (Intervento presentato al convegno 3rd International Conference on City Logistics tenutosi a Madeira nel 25-27 giugno 2003) [10.1016/B978-008044260-0/50013-1].
Last-mile, a Procedure to set up an Optimized Delivery Scheme
FUSCO, Gaetano;
2004
Abstract
The paper illustrates a comprehensive procedure for the design of a delivery scheme in an urban area, where a set of possible locations for logistic transit-points exists. In such a scheme it is assumed that deliveries for Business-to-Consumer (BtoC) e-commerce of goods are performed at specific drop-points, suitably selected to match the pick-up of the parcels to usual activities of the customers, like the breakfast at the bar or the purchase of the newspaper. The solution procedure, which integrates transportation system theory and operational research techniques, applies a disaggregate Nested Logit Model (NLM) for demand estimation, an Analytic Hierarchy Process (AHP) to compare and select possible drop-points, and a double string Genetic Algorithm (GA) to solve jointly both the problems of transit-point location and sizing and of drop-point clustering for deliveries tours. A GA performance function is computed by solving a standard Travel Salesman Problem (TSP) on the road graph, whose travel times have been estimated by assigning the O/D matrix of car trips. The first application of the procedure to the town of Terni, in Italy, has provided very encouraging results.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.