Tactical Capacity Planning (TCP) is becoming a crucial part of logistics in the current environment of demand-driven economics. This paper proposes an innovative approach in the TCP setting, consisting of using the collected historical data of the geographical position and the volume of the orders to plan the capacity requirements for the next day. To this end, the clustering of the city to microzones is introduced using K-means clustering. Then, four different methods (Gaussian Process regression, ARIMA model, Neural Network regression, and Long Short Term Memory network) are used to forecast the next day order volume for each of the clusters. Finally, the Variable Cost and Size Bin Packing problem solved with the predicted demand to outline the usage of a heterogeneous fleet required to serve the next time period. Through experiments on the real data, we conclude, that the proposed algorithm is satisfying the decision safety framework with completely unknown demand and could also be used for other demand forecast applications.

Mixing machine learning and optimization for the tactical capacity planning in last-mile delivery / Fadda, Edoardo; Fedorov, Stanislav; Perboli, Guido; Cardenas Barbosa, Ivan Dario. - ELETTRONICO. - (2021), pp. 1-6. ((Intervento presentato al convegno 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC) tenutosi a Virtual nel July, 12-16, 2021 [10.1109/COMPSAC51774.2021.00180].

Mixing machine learning and optimization for the tactical capacity planning in last-mile delivery

Fadda, Edoardo;Fedorov, Stanislav;Perboli, Guido;
2021

Abstract

Tactical Capacity Planning (TCP) is becoming a crucial part of logistics in the current environment of demand-driven economics. This paper proposes an innovative approach in the TCP setting, consisting of using the collected historical data of the geographical position and the volume of the orders to plan the capacity requirements for the next day. To this end, the clustering of the city to microzones is introduced using K-means clustering. Then, four different methods (Gaussian Process regression, ARIMA model, Neural Network regression, and Long Short Term Memory network) are used to forecast the next day order volume for each of the clusters. Finally, the Variable Cost and Size Bin Packing problem solved with the predicted demand to outline the usage of a heterogeneous fleet required to serve the next time period. Through experiments on the real data, we conclude, that the proposed algorithm is satisfying the decision safety framework with completely unknown demand and could also be used for other demand forecast applications.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2922492