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.
Highlights
This systemic vision of urban logistics is needed for all stakeholders, of freight transport and supply chains [4,5] and of urban transport [6], which include public stakeholders, organizers, orchestrators, and control/regulation actors, and for the users of the public space, i.e., shippers, receivers, transport companies, and citizens being impacted by urban mobility
The aim of this paper is to propose a methodological approach to support decisionmaking for urban logistics spaces (ULS) location
This paper models the problems as a a multiple objective mixed-integer linear program (MILP)
Summary
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]. 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
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