Decision Making Using Simulation Methods in Sustainable Transportation

Rabe, Markus; Goldsman, David

doi:10.1016/b978-0-12-814242-4.00012-0

Cited by 10 publications

(5 citation statements)

References 11 publications

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“…The L&T sector was the initial area where the simheuristic methodology was developed. Rabe and Goldsman [25] presented an efficient combination of optimization-simulation techniques using metaheuristics as a core element for decision support systems to solve many L&T problems. Also, Rabe et al [23] used simheuristics for the optimization of reconfigurable production systems.…”

Section: A Review Of the Simheuristics Concept And Recent Applicationsmentioning

confidence: 99%

Solving NP-Hard Challenges in Logistics and Transportation under General Uncertainty Scenarios Using Fuzzy Simheuristics

Juan,

Rabe,

Ammouriova

et al. 2023

Algorithms

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In the field of logistics and transportation (L&T), this paper reviews the utilization of simheuristic algorithms to address NP-hard optimization problems under stochastic uncertainty. Then, the paper explores an extension of the simheuristics concept by introducing a fuzzy layer to tackle complex optimization problems involving both stochastic and fuzzy uncertainties. The hybrid approach combines simulation, metaheuristics, and fuzzy logic, offering a feasible methodology to solve large-scale NP-hard problems under general uncertainty scenarios. These scenarios are commonly encountered in L&T optimization challenges, such as the vehicle routing problem or the team orienteering problem, among many others. The proposed methodology allows for modeling various problem components—including travel times, service times, customers’ demands, or the duration of electric batteries—as deterministic, stochastic, or fuzzy items. A cross-problem analysis of several computational experiments is conducted to validate the effectiveness of the fuzzy simheuristic methodology. Being a flexible methodology that allows us to tackle NP-hard challenges under general uncertainty scenarios, fuzzy simheuristics can also be applied in fields other than L&T.

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Section: A Review Of the Simheuristics Concept And Recent Applicationsmentioning

confidence: 99%

Solving NP-Hard Challenges in Logistics and Transportation under General Uncertainty Scenarios Using Fuzzy Simheuristics

Juan,

Rabe,

Ammouriova

et al. 2023

Algorithms

View full text Add to dashboard Cite

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“…Some of the initial ideas of the simheuristic implementation were taken from the Vehicle Routing Problems (Juan et al 2011), or Facility Location Schemes (de Armas et al 2017. The clearest contribution to transport problems resolution, where simheuristics is presented as the natural procedure to solve mobility problems in sustainable transportation has been written by Rabe and Goldsman (2019). These two authors presented the conjoint use of optimization-simulation techniques using metaheuristics as the core of many procedures inside decision support systems which solve many logistic and transport problems.…”

Section: Transportation and Logisticsmentioning

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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“…The social dimension includes labor psychology and aging workforce. Simulation studies with sustainability concerns in transportation and logistics have mostly focused on the economic and environmental dimensions with the goal of reducing energy consumption [65]. Table 2 classifies the papers covered in this section according to their focus on sustainability pillars.…”

Section: Applications Of Simulation To Sustainable Transportation Systemsmentioning

confidence: 99%

“…Therefore, combining simulation and optimization has been a growing research area. Different approaches have been proposed to combine simulation and optimization [65]. Among them, simheuristic approaches, which integrate simulation within metaheuristics, have been found to be promising [81].…”

Section: Applications Of Simulation To Sustainable Transportation Systemsmentioning

confidence: 99%

Simulation, Optimization, and Machine Learning in Sustainable Transportation Systems: Models and Applications

et al. 2021

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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.

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Decision Making Using Simulation Methods in Sustainable Transportation

Cited by 10 publications

References 11 publications

Solving NP-Hard Challenges in Logistics and Transportation under General Uncertainty Scenarios Using Fuzzy Simheuristics

Solving NP-Hard Challenges in Logistics and Transportation under General Uncertainty Scenarios Using Fuzzy Simheuristics

Simheuristics: An Introductory Tutorial

Simulation, Optimization, and Machine Learning in Sustainable Transportation Systems: Models and Applications

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