Ora (ITSON) for financing help by means of the project Pyridaben site PROFAPI 2021. Grant quantity: DFP300320569. Institutional Review Board Statement: The study did not involve humans or animals. Information Availability Statement: The study didn’t report any information.Appl. Sci. 2021, 11,18 ofAcknowledgments: The author E.A.L.L., is grateful to the National Science and Technology Council of M ico (CONACYT) by means of the Instituto Tecnol ico de Sonora (ITSON), National Laboratory in Transportation and LogisticITSON; specific thanks to the Firm Sistema Comercial Cerrado for opening their facilities and provide key information. Conflicts of Interest: The author declares no conflict of interest. The funders had no role within the style in the study; within the collection, analyses, or interpretation of data; in the writing from the manuscript, or inside the decision to publish the results.
applied sciencesArticleFuzzy Simheuristics for Optimizing Transportation Systems: Dealing with Stochastic and Fuzzy UncertaintyRafael D. Tordecilla 1,2 , Leandro do C. Martins 1 , Javier Panadero 1,three , Pedro J. Copado 1,3 , Elena PerezBernabeu four and Angel A. Juan 1,3, two 3IN3 omputer Science Division, Universitat Oberta de Catalunya, 08018 Barcelona, Spain; [email protected] (R.D.T.); [email protected] (L.d.C.M.); [email protected] (J.P.); [email protected] (P.J.C.) School of Engineering, Universidad de La Sabana, Chia 250001, Colombia Department of Information Analytics Company Intelligence, Euncet Enterprise College, 08221 Terrassa, Spain Division of Applied Statistics and Operations Study, Universitat Polit nica de Val cia, 03801 Alcoy, Spain; [email protected] Correspondence: [email protected]: Tordecilla, R.D.; Martins, L.d.C.; Panadero, J.; Copado, P.J.; PerezBernabeu, E.; Juan, A.A. Fuzzy Simheuristics for Optimizing Transportation Systems: Coping with Stochastic and Fuzzy Uncertainty. Appl. Sci. 2021, 11, 7950. https:// doi.org/10.3390/app11177950 Academic Editor: Ludmila Dymova Received: 24 July 2021 Accepted: 26 August 2021 Published: 28 AugustAbstract: In the context of logistics and transportation, this paper discusses how simheuristics can be extended by adding a fuzzy layer that permits us to take care of complicated optimization troubles with both stochastic and fuzzy uncertainty. This hybrid method combines simulation, metaheuristics, and fuzzy logic to generate nearoptimal solutions to substantial scale NPhard issues that generally arise in several transportation activities, like the automobile routing problem, the arc routing dilemma, or the team orienteering trouble. The methodology makes it possible for us to model different componentssuch as travel occasions, service occasions, or customers’ demands s deterministic, stochastic, or fuzzy. A series of computational experiments contribute to validate our hybrid approach, which also can be extended to other optimization complications in locations including manufacturing and production, clever cities, telecommunication networks, and so forth. Keywords and phrases: transportation; automobile routing problems; metaheuristics; simulationoptimization; fuzzy techniques1. Introduction Managers have a tendency to rely on Allyl methyl sulfide In Vivo analytical approaches that let them to produce informed choices. This explains why optimization models play a essential role in several industries and organization, including the logistics and transportation sector. Whenever correct facts around the inputs and constraints of your optimization challenge is available, the resulting deterministic models could be solved by using wellknown methods, either of precise o.