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This article presents a novel algorithm based on the cluster first-route second method, which executes a solution through K-means and Optics clustering techniques and Nearest Neighbor and Local Search 2-opt heuristics, for the solution of a vehicle routing problem with time windows (VRPTW). The objective of the problem focuses on reducing distances, supported by the variables of demand, delivery points, capacities, time windows and type of fleet in synergy with the model's taxonomy, based on data referring to deliveries made by a logistics operator in Colombia. As a result, good solutions are generated in minimum time periods after fulfilling the agreed constraints, providing high performance in route generation and solutions for large customer instances. Similarly, the algorithm demonstrates efficiency and competitiveness compared to other methods detailed in the literature, after being benchmarked with the Solomon instance data set, exporting even better results.

The urban freight distribution (UFD) processes represent externalities and inefficiencies to the community and the private actors involved. The enterprise collaborative approach has been implemented during the last decades as a solution to the referred problems. The aim of this study is to present the results of two pilot tests for collaborative logistics strategies carried out in Bogotá, Colombia. The tests were conducted to assess the collaborative logistics strategies’ efficacy in the reduction of congestion and contamination as well as their impact on companies’ efficiency. The initiatives evaluated were: (a) freight consolidation and (b) unload scheduling at retailers. In both cases, benefits were evidenced for the companies (-26% on average freight and + 83% on vehicles occupancy) as well as the community (-23.5% parked trucks on roads and -7% emissions). Nonetheless, the results do not possess statistical sufficiency. Further research could complement the factors and methodologies that facilitate organizational collaboration.