Fuzzy Simheuristics for Optimizing Transportation Systems: Dealing with Stochastic and Fuzzy Uncertainty

Tordecilla, Rafael D.; Martins, Leandro do C.; Panadero, Javier; Copado-Méndez, Pedro J.; Perez‐Bernabeu, Elena; Juan, Ángel A.

doi:10.3390/app11177950

Cited by 10 publications

(10 citation statements)

References 76 publications

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“…There are several lines of research that are still open in the field of simheuristics; among them we can highlight the following ones: (i) the introduction of more advanced machine learning methods-especially those based on supervised learning and reinforcement learning-that enrich the feedback provided by the simulation component to the metaheuristic one, which allow for an accurate classification of promising solutions, and expedite the buildup of surrogate models that can speed up computations even further; (ii) the efficient and easy integration of metaheuristic code developed with modern programming languages with commercial simulators like FlexSim, which currently supports a friendly interaction with Python; and (iii) the extension of simheuristics into fuzzy simheuristics, which allow us to consider non-stochastic as well as stochastic uncertainty, as illustrated in Tordecilla et al [54].…”

Section: Discussionmentioning

confidence: 99%

“…Currently, the simheuristics concept is extended to solve optimization problems considering fuzzy uncertainty. For example, Tordecilla et al [54] introduced a fuzzy layer to combine simulation, metaheuristics, and fuzzy logic to handle fuzzy uncertainty of travel times and customers' demands. Another type of uncertainty in problem characteristics might be modeled as a correlation between different problem elements.…”

Section: Recent Work On Simheuristics In Landtmentioning

confidence: 99%

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Optimizing Transport Logistics under Uncertainty with Simheuristics: Concepts, Review and Trends

et al. 2022

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Background: Uncertainty conditions have been increasingly considered in optimization problems arising in real-life transportation and logistics activities. Generally, the analysis of complex systems in these non-deterministic environments is approached with simulation techniques. However, simulation is not an optimization tool. Hence, it must be combined with optimization methods when our goal is to: (i) minimize operating costs while guaranteeing a given quality of service; or (ii) maximize system performance using limited resources. When solving NP-hard optimization problems, the use of metaheuristics allows us to deal with large-scale instances in reasonable computation times. By adding a simulation layer to the metaheuristics, the methodology becomes a simheuristic, which allows the optimization element to solve scenarios under uncertainty. Methods: This paper reviews the indexed documents in Elsevier Scopus database of both initial as well as recent applications of simheuristics in the logistics and transportation field. The paper also discusses open research lines in this knowledge area. Results: The simheuristics approaches to solving NP-hard and large-scale combinatorial optimization problems under uncertainty scenarios are discussed, as they frequently appear in real-life applications in logistics and transportation activities. Conclusions: The way in which the different simheuristic components interact puts a special emphasis in the different stages that can contribute to make the approach more efficient from a computational perspective. There are several lines of research that are still open in the field of simheuristics.

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Section: Discussionmentioning

confidence: 99%

Section: Recent Work On Simheuristics In Landtmentioning

confidence: 99%

Optimizing Transport Logistics under Uncertainty with Simheuristics: Concepts, Review and Trends

et al. 2022

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“…There are several lines of research that are still open in the field of simheuristics, among them we can highlight the following ones: (i) the introduction of more advanced machine learning methods -specially those based on supervised learning and reinforcement learning-that enrich the feedback provided by the simulation component to the metaheuristic one, allow for an accurate classification of promising solutions, and expedite the buildup of surrogate models that can speed up computations even further -notice that, by integrating a machine learning component into a simheuristic, we are already exploring the integration of the former with learnheuristics (Arnau et al 2018), which is a broad research topic in itself-; (ii) the efficient and easy integration of metaheuristic code developed with modern programming languages with commercial simulators like FlexSim, which currently supports a friendly interaction with Python; and (iii) the extension of simheuristics into fuzzy simheuristics, which allow us to consider non-stochastic uncertainty as well as the stochastic one, as illustrated in Tordecilla et al (2021).…”

Section: Discussionmentioning

confidence: 99%

Simheuristics: An Introductory Tutorial

Juan¹,

Ammouriova

et al. 2022

2022 Winter Simulation Conference (WSC)

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Both manufacturing and service industries are subject to uncertainty. Probability techniques and simulation methods allow us to model and analyze complex systems in which stochastic uncertainty is present. When the goal is to optimize the performance of these stochastic systems, simulation by itself is not enough and it needs to be hybridized with optimization methods. Since many real-life optimization problems in the aforementioned industries are NP-hard and large scale, metaheuristic optimization algorithms are required. The simheuristics concept refers to the hybridization of simulation methods and metaheuristic algorithms. This paper provides an introductory tutorial to the concept of simheuristics, showing how it has been successfully employed in solving stochastic optimization problems in many application fields, from production logistics and transportation to telecommunication and insurance. Current research trends in the area of simheuristics, such as their combination with fuzzy logic techniques and machine learning methods, are also discussed. INTRODUCTIONSince uncertainty is present in most real-life systems, simulation methods are frequently employed to analyze complex systems in industries such as manufacturing and production logistics, transportation, health care, finance, smart cities, and telecommunication. Usually, the uncertainty in these systems is modeled via probability distributions, either theoretical or empirical ones. Then, a logical model of the system is built, and computer simulation is utilized to get insight on the system performance under different scenarios, each of which represent a possible system configuration. As pointed out by some authors, the advances in computer power and simulation software have transformed simulation into a 'first-resource' method for analyzing complex systems under uncertainty (Lucas et al. 2015).Still, simulation by itself is not an optimization tool, since it does not contain a mechanism to efficiently explore the vast solution spaces that arise in combinatorial optimization problems. Therefore, if our goal is

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“…Simheuristics have been effectively used in several application fields, including production planning and scheduling [1,14,48,62], portfolio selection [53], supplier selection [27], telecommunication networks [2], facility location [52], defense [36] and transportation systems [24,54,61]. In order to solve the CARP with stochastic demand, Gonzalez-Martin et al [21] presented a simheuristic approach combining Monte Carlo simulation with the RandSHARP metaheuristic.…”

Section: Fuzzy and Simheuristic Approaches In Transportation Problemsmentioning

confidence: 99%

Solving the time capacitated arc routing problem under fuzzy and stochastic travel and service times

et al. 2023

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Stochastic, as well as fuzzy uncertainty, can be found in most real‐world systems. Considering both types of uncertainties simultaneously makes optimization problems incredibly challenging. In this paper we propose a fuzzy simheuristic to solve the Time Capacitated Arc Routing Problem (TCARP) when the nature of the travel time can either be deterministic, stochastic or fuzzy. The main goal is to find a solution (vehicle routes) that minimizes the total time spent in servicing the required arcs. However, due to uncertainty, other characteristics of the solution are also considered. In particular, we illustrate how reliability concepts can enrich the probabilistic information given to decision‐makers. In order to solve the aforementioned optimization problem, we extend the concept of simheuristic framework so it can also include fuzzy elements. Hence, both stochastic and fuzzy uncertainty are simultaneously incorporated into the CARP. In order to test our approach, classical CARP instances have been adapted and extended so that customers' demands become either stochastic or fuzzy. The experimental results show the effectiveness of the proposed approach when compared with more traditional ones. In particular, our fuzzy simheuristic is capable of generating new best‐known solutions for the stochastic versions of some instances belonging to the tegl, tcarp, val, and rural benchmarks.

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Fuzzy Simheuristics for Optimizing Transportation Systems: Dealing with Stochastic and Fuzzy Uncertainty

Cited by 10 publications

References 76 publications

Optimizing Transport Logistics under Uncertainty with Simheuristics: Concepts, Review and Trends

Optimizing Transport Logistics under Uncertainty with Simheuristics: Concepts, Review and Trends

Simheuristics: An Introductory Tutorial

Solving the time capacitated arc routing problem under fuzzy and stochastic travel and service times

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