axioms Article Location of Urban Logistics Spaces (ULS) for Two-Echelon Distribution Systems José Ruiz-Meza 1 , Karen Meza-Peralta 1,2 , Jairo R. Montoya-Torres 1, * and Jesus Gonzalez-Feliu 2   Citation: Ruiz-Meza, J.; Meza-Peralta, K.; Montoya-Torres, J.R.; Gonzalez-Feliu, J. Location of Urban Logistics Spaces (ULS) for Two-Echelon Distribution Systems. Axioms 2021, 10, 214. https:// doi.org/10.3390/axioms10030214 Academic Editor: Sidney A. Morris Received: 25 June 2021 Accepted: 23 August 2021 Published: 7 September 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1 Research Group in Logistics Systems, School of Engineering, Universidad de La Sabana, km 7 Autopista Norte de Bogotá, Chia 250001, Colombia; joserume@unisabana.edu.co (J.R.-M.); karen.meza@unisabana.edu.co (K.M.-P.) 2 Centre de Recherche en Innovation et Intelligences Managériales, Excelia Business School, 102 Rue de Coureilles, 17024 La Rochelle, France; gonzalezfeliuj@excelia-group.com * Correspondence: jairo.montoya@unisabana.edu.co Abstract: The main concern in city logistics is the need to optimize the movement of goods in urban contexts, and to minimize the multiple costs inherent in logistics operations. Inspired by an application in a medium-sized city in Latin America, this paper develops a bi-objective mixed linear integer programming (MILP) model to locate different types of urban logistics spaces (ULS) for the configuration of a two-echelon urban distribution system. The objective functions seek to minimize the costs associated with distance traveled and relocation, in addition to the costs of violation of time windows. This model considers heterogeneous transport, speed assignment, and time windows. For experimental evaluation, two operational scenarios are considered, and Pareto frontiers are obtained to identify the efficient non-dominated solutions to select the most feasible ones from such a set. A case study of a distribution company of goods for supermarkets in the city of Barranquilla, Colombia, is also used to validate the proposed model. These solutions allow decision-makers to define the configuration of ULS networks for urban product delivery. Keywords: Urban Logistics Spaces (ULS); two-echelon distribution systems; location; mixed-integer linear programming; multi-objective; case study 1. Introduction The location of logistics centers in urban areas and the scarcity of alternative transport systems are some of the factors contributing to the inefficient flow of cargo transport in cities. Therefore, the main concern in the general analysis of urban logistics systems is the need to optimize the movement of goods in cities [1]. These flows include a variety of organizations involving both movements of goods and people, mainly when dealing with B2C deliveries [2] or shopping mobility including both personal mobility and freight transport [3]. This systemic vision of urban logistics is needed for all stakeholders, not only of freight transport and supply chains [4,5] but also of urban transport [6], which include public stakeholders, organizers, orchestrators, and control/regulation actors, but also for the users of the public space, i.e., shippers, receivers (mainly companies but also individuals or associations), transport companies, and also citizens being impacted by urban mobility. Moreover, urban logistics deals with a plethora of flows going beyond the last mile of retailers, and including B2B flows, B2C flows, personal mobility flows transporting good, and city management flows, among others [7]. In that context, an efficient use of the resources required for general logistics operations (e.g., number of vehicles, operation times, labor), and the minimization of costs associated to the operation of such urban systems seem of paramount importance as they represent between 15% and 20% of the total operational cost [8]. In the literature, authors have traditionally considered that logistics is a function of costs [9–12]. Axioms 2021, 10, 214. https://doi.org/10.3390/axioms10030214 https://www.mdpi.com/journal/axioms