A simheuristic for routing electric vehicles with limited driving ranges and stochastic travel times

Green transportation is becoming relevant in the context of smart cities, where the use of electric vehicles represents a promising strategy to support sustainability policies. However the use of electric vehicles shows some drawbacks as well, such as their limited driving-range capacity. This paper analyses a realistic vehicle routing problem in which both driving-range constraints and stochastic travel times are considered. Thus, the main goal is to minimize the expected time-based cost required to complete the freight distribution plan. In order to design reliable Routing plans, a simheuristic algorithm is proposed. It combines Monte Carlo simulation with a multi-start metaheuristic, which also employs biased-randomization techniques. By including simulation, simheuristics extend the capabilities of metaheuristics to deal with stochastic problems. A series of computational experiments are performed to test our solving approach as well as to analyse the effect of uncertainty on the routing plans.Peer Reviewe

Multi-Criteria Optimization for Fleet Size with Environmental Aspects

[EN] This research concerns multi-criteria vehicle routing problems. Mathematical models are formulated with mixed-integer programming. We consider maximization of capacity of truck vs. minimization of utilization of fuel, carbon emission and production of noise. The problems deal with green logistics for routes crossing the Western Pyrenees in Navarre, Basque Country and La Rioja, Spain. We consider heterogeneous fleet of trucks. Different types of trucks have not only different capacities, but also require different amounts of fuel for operations. Consequently, the amount of carbon emission and noise vary as well. Companies planning delivery routes must consider the trade-off between the financial and environmental aspects of transportation. Efficiency of delivery routes is impacted by truck size and the possibility of dividing long delivery routes into smaller ones. The results of computational experiments modeled after real data from a Spanish food distribution company are reported. Computational results based on formulated optimization models show some balance between fleet size, truck types, utilization of fuel, carbon emission and production of noise. As a result, the company could consider a mixture of trucks sizes and divided routes for smaller trucks. Analyses of obtained results could help logistics managers lead the initiative in environmental conservation by saving fuel and consequently minimizing pollution.This work has been partially supported by the National Research Center (NCN), Poland (DEC2013/11/B/ST8/04458), by AGH, and by the Spanish Ministry of Economy and Competitiveness (TRA2013-48180- C3-P and TRA2015-71883-REDT), and the Ibero-American Program for Science and Technology for Development (CYTED2014-515RT0489). Likewise, we want to acknowledge the support received by the CAN Foundation in Navarre, Spain (Grants CAN2014-3758 and CAN2015-70473). The authors are grateful to anonymous reviewers for their comments.Sawik, B.; Faulin, J.; Pérez-Bernabeu, E. (2017). Multi-Criteria Optimization for Fleet Size with Environmental Aspects. Transportation Research Procedia. 27:61-68. https://doi.org/10.1016/j.trpro.2017.12.05661682

Public-Private Partnerships for Technology Growth in the Public Sector

Public-private partnerships (PPP) are a mechanism for financing large infrastructure development such as transportation projects, hospitals, schools, and public works facilities. In addition, the benefits of PPP stretch well into the realm of engineering management. Most notably, PPPs provide the opportunity for more efficient project management, proficient risk mitigation, and enhanced technological innovation. This paper provides a general description of the typical PPP process and how this process can be used to improve management of technology in the public sector

SR-2: A Hybrid Algorithm for the Capacitated Vehicle Routing Problem

During the last decades a lot of work has been devoted to develop algorithms that can provide near-optimal solutions for the capacitated vehicle routing problem (CVRP). Most of these algorithms are designed to minimize an objective function, subject to a set of constraints, which typically represents aprioristic costs. This approach provides adequate theoretical solutions, but they do not always fit real-life needs since there are some important costs and some routing constraints or desirable properties that cannot be easily modeled. In this paper, we present a new approach which combines the use of Monte Carlo simulation and parallel and grid computing techniques to provide a set of alternative solutions to the CVRP. This allows the decision-maker to consider multiple solution characteristics other than just aprioristic costs. Therefore, our methodology offers more flexibility during the routing selection process, which may help to improve the quality of service offered to clients

Horizontal collaboration in freight transport: concepts, benefits and environmental challenges

[EN] Since its appearance in the 1990s, horizontal collaboration (HC) practices have revealed themselves as catalyzers for optimizing the distribution of goods in freight transport logistics. After introducing the main concepts related to HC, this paper offers a literature review on the topic and provides a classification of best practices in HC. Then, the paper analyses the main benefits and optimization challenges associated with the use of HC at the strategic, tactical, and operational levels. Emerging trends such as the concept of ` green' or environmentally- friendly HC in freight transport logistics are also introduced. Finally, the paper discusses the need of using hybrid optimization methods, such as simheuristics and learnheuristics, in solving some of the previously identified challenges in real- life scenarios dominated by uncertainty and dynamic conditions.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness & FEDER (TRA2013-48180-C3-P, TRA2015-71883-REDT), The Erasmus+ program (2016-1-ES01-KA108-023465), the Ibero American Program of Science and Technology for Development (CYTED2014-515RT-0489), the CAN Foundation (CAN2014-3758, CAN2015-70473), and the Spanish Ministry of Education (FPU-14/00024).Serrano-Hernandez, A.; Juan, AA.; Faulin, J.; Perez-Bernabeu, E. (2017). Horizontal collaboration in freight transport: concepts, benefits and environmental challenges. SORT. Statistics and Operations Research Transactions. 41(2):393-414. https://doi.org/10.2436/20.8080.02.65S39341441

Using Modelling Techniques to Analyze Urban Freight Distribution. A Case Study in Pamplona (Spain)

The city of Pamplona, in Spain, is currently experiencing several changes regarding sustainable mobility such as pedestrianization of some streets in the city center, and access control to the Old Town for motor vehicles through the use of automatic number-plate recognition. However, some groups including local neighbors and businesses are raising complaints as they are being affected by these measures. This is also the case for couriers and logistics companies which have now to comply with new regulations regarding delivery routes throughout the Old Town. This paper will present a comprehensive study of the situation that is being carried out, and in which social perceptions and freight traffic patterns in the Old Town of Pamplona are analyzed to understand how urban freight distribution could be improved in the area. For this purpose, we make use of a survey-based research to the stakeholders, i.e. pedestrians, logistics companies, retailers, and authorities of Pamplona. Results highlight pollution derived from transportation, lack of parking spaces as well as invasion of public spaces in the city center as the key issues for improving freight transportation in the Old Town. Finally, placing a distribution center in the Old Town and the promotion of the cycle-logistics are considered as the future of the urban distribution in Pamplona

The Effect of Environmental Criteria on Locating a Biorefinery: A Green Facility Location Problem

Underestimating facility location decisions may penalize business performance over the time. Those penalties usually have been studied from the economic point of view analyzing its impact on profitability. Additionally, the concern about the obtaining of sustainability is gaining importance leading to seek for renewable energy sources to reduce greenhouse gas emissions. However, little attention has been paid on choosing a location considering environmental criteria. Thus, this work aims at determining a biorefinery location considering its impacts on natural resources. Therefore, a mixed integer linear programming (MILP) model is developed taking into account the crop location and the biomass production seasonality to obtain an apposite location that minimizes environmental impact. The initial version of this paper was presented at ICIL 2016 Conference.

Electric vehicle routing, arc routing, and team orienteering problems in sustainable transportation

[EN] The increasing use of electric vehicles in road and air transportation, especially in last-mile delivery and city mobility, raises new operational challenges due to the limited capacity of electric batteries. These limitations impose additional driving range constraints when optimizing the distribution and mobility plans. During the last years, several researchers from the Computer Science, Artificial Intelligence, and Operations Research communities have been developing optimization, simulation, and machine learning approaches that aim at generating efficient and sustainable routing plans for hybrid fleets, including both electric and internal combustion engine vehicles. After contextualizing the relevance of electric vehicles in promoting sustainable transportation practices, this paper reviews the existing work in the field of electric vehicle routing problems. In particular, we focus on articles related to the well-known vehicle routing, arc routing, and team orienteering problems. The review is followed by numerical examples that illustrate the gains that can be obtained by employing optimization methods in the aforementioned field. Finally, several research opportunities are highlighted.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), the SEPIE Erasmus+Program (2019-I-ES01-KA103-062602), and the IoF2020-H2020 (731884) project.Do C. Martins, L.; Tordecilla, RD.; Castaneda, J.; Juan-Pérez, ÁA.; Faulin, J. (2021). Electric vehicle routing, arc routing, and team orienteering problems in sustainable transportation. Energies. 14(16):1-30. https://doi.org/10.3390/en14165131130141

A parameter-free approach for solving combinatorial optimization problems through biased randomization of efficient heuristics

This paper discusses the use of probabilistic or randomized algorithms for solving combinatorial optimization problems. Our approach employs non-uniform probability distributions to add a biased random behavior to classical heuristics so a large set of alternative good solutions can be quickly obtained in a natural way and without complex con guration processes. This procedure is especially useful in problems where properties such as non-smoothness or non-convexity lead to a highly irregular solution space, for which the traditional optimization methods, both of exact and approximate nature, may fail to reach their full potential. The results obtained are promising enough to suggest that randomizing classical heuristics is a powerful method that can be successfully applied in a variety of cases

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