The role of operational research in green freight transportation

Recent years have witnessed an increased awareness of the negative external impacts of freight transportation. The field of Operational Research (OR) has, particularly in the recent years, continued to contribute to alleviating the negative impacts through the use of various optimization models and solution techniques. This paper presents the basic principles behind and an overview of the existing body of recent research on ‘greening’ freight transportation using OR-based planning techniques. The particular focus is on studies that have been described for two heavily used modes for transporting freight across the globe, namely road (including urban and electric vehicles) and maritime transportation, although other modes are also briefly discussed

Estado da Arte da Logística Verde

TCC (graduação) - Universidade Federal de Santa Catarina, Centro Sócio Econômico, Administração.A logística verde busca o desempenho das atividades logísticas de forma ecologicamente adequada, de modo a desenvolver a economia e preservar recursos ambientais, e passou a ser um tema bastante discutido com o aumento da preocupação com a sustentabilidade e a percepção dos danos ambientais que são causados pelas operações logísticas. Este artigo tem como objetivo investigar as pesquisas referentes a logística verde, identificando assuntos relevantes e contribuições acerca do tema. Para isso realizou-se uma pesquisa estado da arte através de uma revisão sistêmica e análise bibliométrica a partir do Methodi Ordinatio. Foram selecionados os 10 (dez) artigos para análise, com base no ano de publicação, número de citações e fator de impacto. Dentre os aspectos avaliados encontra-se a distribuição temporal das publicações, o número de citações, publicações por autores, a metodologia utilizada, frequência de palavras-chave, o perfil dos periódicos e o contexto presente em cada publicação. Os resultados demonstram uma forte relevância da logística verde na literatura, com destaque para tópicos de gestão de cadeia de suprimentos verde, transporte verde e políticas governamentais.Green logistics seeks the performance of logistics activities in an ecologically appropriate way, in order to develop the economy and preserve environmental resources, and has become a widely discussed topic with the increased concern for sustainability and the perception of environmental damage that is caused by logistics operations. This article aims to investigate research related to green logistics, identifying relevant issues and contributions on the subject. For this, a state-of-the-art research was carried out through a systemic review and bibliometric analysis from the Methodi Ordinatio. There were 10 (ten) articles selected for analysis, based on the year of publication, number of citations and impact factor. Among the aspects evaluated are the temporal distribution of publications, the number of citations, publications by authors, the methodology used, frequency of keywords, the profile of the journals and the context present in each publication. The results demonstrate a strong relevance of green logistics in the literature, with emphasis on topics of green supply chain management, green transport and government policies

Evaluating the impact of new trends in urban freight transportation attending the triple bottom line: A case study

Urban freight transportation is considered one of the activities that has the greatest impact on urban areas in terms of sustainability and livability, therefore, new trends are emerging to reduce its impacts. However, there is a lack of methodologies to evaluate and validate the implementation of these trends. In this context, the proposed methodology presents the implementation of three KPIs attending the triple bottom line approach, which look at impacts from the social, environmental, and economic perspectives. This methodology is tested on a case study and the results conclude that the implementation of new trends in UFT can reduce its impact in urban areas

Smart city for sustainable urban freight logistics

International audienc

The multi-objective Steiner pollution-routing problem on congested urban road networks

This paper introduces the Steiner Pollution-Routing Problem (SPRP) as a realistic variant of the PRP that can take into account the real operating conditions of urban freight distribution. The SPRP is a multi-objective, time and load dependent, fleet size and mix PRP, with time windows, flexible departure times, and multi-trips on congested urban road networks, that aims at minimising three objective functions pertaining to (i) vehicle hiring cost, (ii) total amount of fuel consumed, and (iii) total makespan (duration) of the routes. The paper focuses on a key complication arising from emissions minimisation in a time and load dependent setting, corresponding to the identification of the full set of the eligible road-paths between consecutive truck visits a priori, and to tackle the issue proposes new combinatorial results leading to the development of an exact Path Elimination Procedure (PEP). A PEP-based Mixed Integer Programming model is further developed for the SPRP and embedded within an efficient mathematical programming technique to generate the full set of the non-dominated points on the Pareto frontier of the SPRP. The proposed model considers truck instantaneous Acceleration/Deceleration (A/D) rates in the fuel consumption estimation, and to address the possible lack of such data at the planning stage, a new model for the construction of reliable synthetic spatiotemporal driving cycles from available macroscopic traffic speed data is introduced. Several analyses are conducted to: (i) demonstrate the added value of the proposed approach, (ii) exhibit the trade-off between the business and environmental objectives on the Pareto front of the SPRP, (iii) show the benefits of using multiple trips, and (iv) verify the reliability of the proposed model for the generation of driving cycles. A real road network based on the Chicago's arterial streets is also used for further experimentation with the proposed PEP algorithm. © 2019 Elsevier Lt

Simulation, optimization, and machine learning in sustainable transportation systems: Models and applications

[EN] The need for effective freight and human transportation systems has consistently increased during the last decades, mainly due to factors such as globalization, e-commerce activities, and mobility requirements. Traditionally, transportation systems have been designed with the main goal of reducing their monetary cost while offering a specified quality of service. During the last decade, however, sustainability concepts are also being considered as a critical component of transportation systems, i.e., the environmental and social impact of transportation activities have to be taken into account when managers and policy makers design and operate modern transportation systems, whether these refer to long-distance carriers or to metropolitan areas. This paper reviews the existing work on different scientific methodologies that are being used to promote Sustainable Transportation Systems (STS), including simulation, optimization, machine learning, and fuzzy sets. This paper discusses how each of these methodologies have been employed to design and efficiently operate STS. In addition, the paper also provides a classification of common challenges, best practices, future trends, and open research lines that might be useful for both researchers and practitioners.This work has been partially supported by the Spanish Ministry of Science, Innovation, and Universities (PID2019-111100RB-C21-C22/AEI/10.13039/501100011033, RED2018-102642-T) and the SEPIE Erasmus+ Program (2019-I-ES01-KA103-062602), and the IoF2020-H2020 (731884) project.Torre-Martínez, MRDL.; Corlu, CG.; Faulin, J.; Onggo, BS.; Juan-Pérez, ÁA. (2021). Simulation, optimization, and machine learning in sustainable transportation systems: Models and applications. Sustainability. 13(3):1-21. https://doi.org/10.3390/su1303155112113

Green intermodal freight transportation: bi-objective modeling and analysis

Efficient planning of freight transportation requires a comprehensive look at wide range of factors in the operation and management of any transportation mode to achieve safe, fast, and environmentally suitable movement of goods. In this regard, a combination of transportation modes offers flexible and environmentally friendly alternatives to transport high volumes of goods over long distances. In order to reflect the advantages of each transportation mode, it is the challenge to develop models and algorithms in Transport Management System software packages. This paper discusses the principles of green logistics required in designing such models and algorithms which truly represent multiple modes and their characteristics. Thus, this research provides a unique practical contribution to green logistics literature by advancing our understanding of the multi-objective planning in intermodal freight transportation. Analysis based on a case study from hinterland intermodal transportation in Europe is therefore intended to make contributions to the literature about the potential benefits from combining economic and environmental criteria in transportation planning. An insight derived from the experiments conducted shows that there is no need to greatly compromise on transportation costs in order to achieve a significant reduction in carbon-related emissions

Optimization of route choice, speeds and stops in time-varying networks for fuel-efficient truck journeys

A method is presented for the real-time optimal control of the journey of a truck, travelling between a pair of pick-up/drop-off locations in a time-varying traffic network, in order to reduce fuel consumption. The method, when applied during the journey, encapsulates the choice of route, choice of speeds on the links, and choice of stop locations/durations; when applied pre-trip, it additionally incorporates choice of departure time. The problem is formulated by using a modified form of space-time extended network, in such a way that a shortest path in this network corresponds to an optimal choice of not only route, stops and (when relevant) departure time, but also of speeds. A series of simple illustrative examples are presented to illustrate the formulation. Finally, the method is applied to a realistic-size case study

Dynamic Stochastic Electric Vehicle Routing with Safe Reinforcement Learning

Dynamic routing of electric commercial vehicles can be a challenging problem since besides the uncertainty of energy consumption there are also random customer requests. This paper introduces the Dynamic Stochastic Electric Vehicle Routing Problem (DS-EVRP). A Safe Reinforcement Learning method is proposed for solving the problem. The objective is to minimize expected energy consumption in a safe way, which means also minimizing the risk of battery depletion while en route by planning charging whenever necessary. The key idea is to learn offline about the stochastic customer requests and energy consumption using Monte Carlo simulations, to be able to plan the route predictively and safely online. The method is evaluated using simulations based on energy consumption data from a realistic traffic model for the city of Luxembourg and a high-fidelity vehicle model. The results indicate that it is possible to save energy at the same time maintaining reliability by planning the routes and charging in an anticipative way. The proposed method has the potential to improve transport operations with electric commercial vehicles capitalizing on their environmental benefit