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Salting operations are essential but expensive in northern winters. We aim to predict the quantity of salt and abrasive needed for a specific road segment for each hour. An estimate of the quantity allows managers to better manage the loads in the trucks and to propose optimal vehicle routes based on the forecast, which will allow them to optimize costs. This article uses machine‐learning techniques based on truck telemetry data, weather conditions, and segment attributes. Geographic information systems (GIS) allow us to exploit the street‐network characteristics, which were ignored by previous prediction models. The results show that the XGBoost method performs better than other techniques (R2 = 0.83).

The mixed capacitated general routing problem (MCGRP) is defined over a mixed graph, for which some nodes, arcs, and edges must be serviced. The problem consists of determining a set of routes of minimum cost that satisfy the demand. Some problems like salt spreading have a time-dependent demand which was ignored in the previous studies. This variation of demand is due to the weather or traffic conditions. This study presents a mixed integer programming model without graph transformation to node routing. We use CPLEX to solve small instances and we develop a Slack Induction by String Removals metaheuristic for large instances adapted to this problem. The proposed model and metaheuristic were tested on problems derived from a set of classical instances of the MCGRP with some modifications to include time-dependent demands.

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