Open Access
Using modelling techniques to analyze urban freight distribution. A case study in Pamplona (Spain)
(Elsevier, 2018) Alvarez Indave, Pablo; Serrano Hernández, Adrián; Faulín Fajardo, Javier; Juan Pérez, Ángel Alejandro; Institute of Smart Cities - ISC
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.
Open Access
A simheuristic for routing electric vehicles with limited driving ranges and stochastic travel times
(Institut d'Estadistica de Catalunya (Idescat), 2019) Reyes-Rubiano, Lorena Silvana; Ferone, Daniele; Juan Pérez, Ángel Alejandro; Faulín Fajardo, Javier; Institute of Smart Cities - ISC; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
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.
Open Access
Horizontal collaboration in freight transport: concepts, benefits and environmental challenges
(Institut d'Estadística de Catalunya, 2017) Serrano Hernández, Adrián; Juan Pérez, Ángel Alejandro; Faulín Fajardo, Javier; Pérez Bernabeu, Elena; Estatistika eta Ikerketa Operatiboa; Institute of Smart Cities - ISC; Estadística e Investigación Operativa
Since its appearance in the 1990s, horizontal collaboration (HC) practices have revealed them-selves 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 freighttransport 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.
Open Access
A review of simheuristics: extending metaheuristics to deal with stochastic combinatorial optimization problems
(Elsevier Ltd., 2015) Juan Pérez, Ángel Alejandro; Faulín Fajardo, Javier; Grasman, Scott Erwin; Rabe, Markus; Figueira, Gonçalo; Estadística e Investigación Operativa; Estatistika eta Ikerketa Operatiboa
Many combinatorial optimization problems (COPs) encountered in real-world logistics, transportation, production, healthcare, financial, telecommunication, and computing applications are NP-hard in nature. These real-life COPs are frequently characterized by their large-scale sizes and the need for obtaining high-quality solutions in short computing times, thus requiring the use of metaheuristic algorithms. Metaheuristics benefit from different random-search and parallelization paradigms, but they frequently assume that the problem inputs, the underlying objective function, and the set of optimization constraints are deterministic. However, uncertainty is all around us, which often makes deterministic models oversimplified versions of real-life systems. After completing an extensive review of related work, this paper describes a general methodology that allows for extending metaheuristics through simulation to solve stochastic COPs. ‘Simheuristics’ allow modelers for dealing with real-life uncertainty in a natural way by integrating simulation (in any of its variants) into a metaheuristic-driven framework. These optimization-driven algorithms rely on the fact that efficient metaheuristics already exist for the deterministic version of the corresponding COP. Simheuristics also facilitate the introduction of risk and/or reliability analysis criteria during the assessment of alternative high-quality solutions to stochastic COPs. Several examples of applications in different fields illustrate the potential of the proposed methodology.
Open Access
A biased-randomized learnheuristic for solving the team orienteering problem with dynamic rewards
(Elsevier, 2020) Reyes-Rubiano, Lorena Silvana; Juan Pérez, Ángel Alejandro; Bayliss, C.; Panadero, Javier; Faulín Fajardo, Javier; Copado, P.; Institute of Smart Cities - ISC
In this paper we discuss the team orienteering problem (TOP) with dynamic inputs. In the static version of the TOP, a fixed reward is obtained after visiting each node. Hence, given a limited fleet of vehicles and a threshold time, the goal is to design the set of routes that maximize the total reward collected. While this static version can be efficiently tackled using a biased-randomized heuristic (BR-H), dealing with the dynamic version requires extending the BR-H into a learnheuristic (BR-LH). With that purpose, a 'learning' (white-box) mechanism is incorporated to the heuristic in order to consider the variations in the observed rewards, which follow an unknown (black-box) pattern. In particular, we assume that: (i) each node in the network has a 'base' or standard reward value; and (ii) depending on the node's position inside its route, the actual reward value might differ from the base one according to the aforementioned unknown pattern. As new observations of this black-box pattern are obtained, the white-box mechanism generates better estimates for the actual rewards after each new decision. Accordingly, better solutions can be generated by using this predictive mechanism. Some numerical experiments contribute to illustrate these concepts.
Open Access
Optimizing energy consumption in transportation: literature review, insights, and research opportunities
(MDPI, 2020) Corlu, Canan Gunes; Torre Martínez, Rocío de la; Serrano Hernández, Adrián; Juan Pérez, Ángel Alejandro; Faulín Fajardo, Javier; Institute for Advanced Research in Business and Economics - INARBE; Institute of Smart Cities - ISC
From airplanes to electric vehicles and trains, modern transportation systems require large quantities of energy. These vast amounts of energy have to be produced somewhere—ideally by using sustainable sources—and then brought to the transportation system. Energy is a scarce and costly resource, which cannot always be produced from renewable sources. Therefore, it is critical to consume energy as efficiently as possible, that is, transportation activities need to be carried out with an optimal intake of energetic means. This paper reviews existing work on the optimization of energy consumption in the area of transportation, including road freight, passenger rail, maritime, and air transportation modes. The paper also analyzes how optimization methods—of both exact and approximate nature—have been used to deal with these energy-optimization problems. Finally, it provides insights and discusses open research opportunities regarding the use of new intelligent algorithms—combining metaheuristics with simulation and machine learning—to improve the efficiency of energy consumption in transportation.
Open Access
Electric vehicles in logistics and transportation: a survey on emerging environmental, strategic, and operational challenges
(MDPI, 2016) Juan Pérez, Ángel Alejandro; Méndez, Carlos Alberto; Faulín Fajardo, Javier; Armas, Jesica de; Grasman, Scott Erwin; Estadística e Investigación Operativa; Estatistika eta Ikerketa Operatiboa
Current logistics and transportation (L&T) systems include heterogeneous fleets consisting of common internal combustion engine vehicles as well as other types of vehicles using “green” technologies, e.g., plug-in hybrid electric vehicles and electric vehicles (EVs). However, the incorporation of EVs in L&T activities also raise some additional challenges from the strategic, planning, and operational perspectives. For instance, smart cities are required to provide recharge stations for electric-based vehicles, meaning that investment decisions need to be made about the number, location, and capacity of these stations. Similarly, the limited driving-range capabilities of EVs, which are restricted by the amount of electricity stored in their batteries, impose non-trivial additional constraints when designing efficient distribution routes. Accordingly, this paper identifies and reviews several open research challenges related to the introduction of EVs in L&T activities, including: (a) environmental-related issues; and (b) strategic, planning and operational issues associated with “standard” EVs and with hydrogen-based EVs. The paper also analyzes how the introduction of EVs in L&T systems generates new variants of the well-known Vehicle Routing Problem, one of the most studied optimization problems in the L&T field, and proposes the use of metaheuristics and simheuristics as the most efficient way to deal with these complex optimization problems.