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Reyes-Rubiano, Lorena Silvana

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Reyes-Rubiano

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Lorena Silvana

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Estadística e Investigación Operativa

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0000-0003-0995-4049

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811229

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2019 - 20202
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Author: Juan Pérez, Ángel Alejandro ×

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    PublicationOpen 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.
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    PublicationOpen 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.