Combining symbiotic simulation systems with enterprise data storage systems for real-time decision-making

[EN] A symbiotic simulation system (S3) enables interactions between a physical system and its computational model representation. To support operational decisions, an S3 uses real-time data from the physical system, which is gathered via sensors and saved in an enterprise data storage system (EDSS). Both real-time and historical data are then used as inputs to the different components of an S3. This paper proposes a generic system architecture for an S3 and discusses its integration within EDSSs. The paper also reviews the literature on S3 and analyses how these systems can be used for real-time decision-making.This work has been partially funded by the Staff Mobility programme from the Erasmus+ (2020-2021).Onggo, B.; Corlu, CG.; Juan, AA.; Monks, T.; Torre-Martínez, MRDL. (2021). Combining symbiotic simulation systems with enterprise data storage systems for real-time decision-making. Enterprise Information Systems. 15(2):230-247. https://doi.org/10.1080/17517575.2020.177758723024715

The role of simulation and serious games in teaching concepts on circular economy and sustainable energy

[EN] The prevailing need for a more sustainable management of natural resources depends not only on the decisions made by governments and the will of the population, but also on the knowledge of the role of energy in our society and the relevance of preserving natural resources. In this sense, critical work is being done to instill key concepts-such as the circular economy and sustainable energy-in higher education institutions. In this way, it is expected that future professionals and managers will be aware of the importance of energy optimization, and will learn a series of computational methods that can support the decision-making process. In the context of higher education, this paper reviews the main trends and challenges related to the concepts of circular economy and sustainable energy. Besides, we analyze the role of simulation and serious games as a learning tool for the aforementioned concepts. Finally, the paper provides insights and discusses open research opportunities regarding the use of these computational tools to incorporate circular economy concepts in higher education degrees. Our findings show that, while efforts are being made to include these concepts in current programs, there is still much work to be done, especially from the point of view of university management. In addition, the analysis of the teaching methodologies analyzed shows that, although their implementation has been successful in favoring the active learning of students, their use (especially that of serious games) is not yet widespread.This work has been partially supported by the Spanish Ministry of Science (PID2019-111100RB-C21-C22/AEI/10.13039/501100011033, RED2018-102642-T) and the SEPIE Erasmus+ Program (2019-I-ES01-KA103-062602).Torre-Martínez, MRDL.; Onggo, BS.; Corlu, CG.; Nogal, M.; Juan-Pérez, ÁA. (2021). The role of simulation and serious games in teaching concepts on circular economy and sustainable energy. Energies. 14(4):1-21. https://doi.org/10.3390/en1404113812114

Simulation, optimization, and machine learning in sustainable transportation systems: Models and applications

[EN] 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.This work has been partially supported by the Spanish Ministry of Science, Innovation, and Universities (PID2019-111100RB-C21-C22/AEI/10.13039/501100011033, RED2018-102642-T) and the SEPIE Erasmus+ Program (2019-I-ES01-KA103-062602), and the IoF2020-H2020 (731884) project.Torre-Martínez, MRDL.; Corlu, CG.; Faulin, J.; Onggo, BS.; Juan-Pérez, ÁA. (2021). Simulation, optimization, and machine learning in sustainable transportation systems: Models and applications. Sustainability. 13(3):1-21. https://doi.org/10.3390/su1303155112113

Estimating the Parameters of the Generalized Lambda Distribution: Which Method Performs Best?

Generalized lambda distribution (GLD) is a flexible distribution that can represent a wide variety of distributional shapes. This property of the GLD has made it very popular in simulation input modeling in recent years, and several fitting methods for estimating the parameters of the GLD have been proposed. Nevertheless, there appears to be a lack of insights about the performances of these fitting methods in estimating the parameters of the GLD for a variety of distributional shapes and input data. Our primary goal in this article is to compare the goodness-of-fits of the popular fitting methods in estimating the parameters of the GLD introduced in Freimer etal. (1988), i.e., Freimer-Mudholkar-Kollia-Lin (FMKL) GLD, and provide guidelines to the simulation practitioner about when to use each method. We further describe the use of the genetic algorithm for the FMKL GLD, and investigate the performances of the suggested methods in modeling the daily exchange rates of eight currencies

Simulation of inventory systems with unknown input models: a data-driven approach

<p>Stochastic simulation is a commonly used tool by practitioners for evaluating the performance of inventory policies. A typical inventory simulation starts with the determination of the best-fit input models (e.g. probability distribution function of the demand random variable) and then obtains a performance measure estimate under these input models. However, this sequential approach ignores the uncertainty around the input models, leading to inaccurate performance measures, especially when there is limited historical input data. In this paper, we take an alternative approach and propose a simulation replication algorithm that jointly estimates the input models and the performance measure, leading to a credible interval for the performance measure under input-model uncertainty. Our approach builds on a nonparametric Bayesian input model and frees the inventory manager from making any restrictive assumptions on the functional form of the input models. Focusing on a single-product inventory simulation, we show that the proposed method improves the estimation of the service levels when compared to the traditional practice of using the best-fit or the empirical distribution as the unknown demand distribution.</p

Empirical distributions of daily equity index returns: A comparison

The normality assumption concerning the distribution of equity returns has long been challenged both empirically and theoretically. Alternative distributions have been proposed to better capture the characteristics of equity return data. This paper investigates the ability of five alternative distributions to represent the behavior of daily equity index returns over the period 1979-2014: the skewed Student-t distribution, the generalized lambda distribution, the Johnson system of distributions, the normal inverse Gaussian distribution, and the g-and-h distribution. We find that the generalized lambda distribution is a prominent alternative for modeling the behavior of daily equity index returns. (C) 2016 Elsevier Ltd. All rights reserved

Stochastic Simulation under Input Uncertainty: A Review

Stochastic simulation is an invaluable tool for operations-research practitioners for the performance evaluation of systems with random behavior and mathematically intractable performance measures. An important step in the development of a simulation model is input modeling, which is the selection of appropriate probability models that characterize the stochastic behavior of the system inputs. For example, in a queueing-system simulation, input modeling includes choosing the probability distributions for stochastic interarrival and service times. The lack of knowledge about the true input models is an important practical challenge. The impact of the lack of information about the true input model on the simulation output is referred to as ‘input uncertainty’ in the simulation literature. Ignoring input uncertainty often leads to poor estimates of the system performance, especially when there is limited amount of historical data to make inference on the input models. Therefore, it is critically important to assess the impact of input uncertainty on the estimated performance measures in a statistically valid and computationally efficient way. The goal of this paper is to present input uncertainty research in stochastic simulations by providing a classification of major research streams and focusing on the new developments in recent years. We also review application papers that investigate the value of representing input uncertainty in the simulation of real-world stochastic systems in various industries. We provide a self-contained presentation of the major research streams with a special attention on the new developments in the last couple of years

Demand fulfillment probability in a multi-item inventory system with limited historical data

<p>In a budget-constrained multi-item inventory system with independent demands, we consider the case of unknown demand parameters that are estimated from limited amounts of historical demand data. In this situation, the probability of satisfying all item demands, as a measure of demand fulfillment, is a function of the finite-sample estimates of the unknown demand parameters; thus, the demand fulfillment probability is a random variable. First, we characterize the properties of an asymptotical approximation to the mean and variance of this random variable due to the use of limited data for demand parameter estimation. Second, we use the characterization of the variance of the demand fulfillment probability for quantifying the impact of demand parameter uncertainty on demand fulfillment via numerical experiments. Third, we propose an inventory optimization problem that minimizes the variance of the demand fulfillment probability due to demand parameter uncertainty subject to a budget constraint on the total inventory investment. Our numerical experiments demonstrate that, despite the availability of limited amounts of historical demand data, it is possible to manage inventory with significantly reduced variance in the demand fulfillment probability.</p

On the scarcity of observations when modelling random inputs and the quality of solutions to stochastic optimisation problems

Most of the literature on supply chain management assumes that the demand distributions and their parameters are known with certainty. However, this may not be the case in practice since decision makers may have access to limited amounts of historical demand data only. In this case, treating the demand distributions and their parameters as the true distributions is risky, and it may lead to sub-optimal decisions. To demonstrate this, this paper considers an inventory-routing problem with stochastic demands, in which the retailers have access to limited amounts of historical demand data. We use simheuristic method to solve the optimisation problem and investigate the impact of the limited amount of demand data on the quality of the simheuristic solutions to the underlying optimisation problem. Our experiment illustrates the potential impact of input uncertainty on the quality of the solution provided by a simheuristic algorithm.</p

Driving inventory system simulations with limited demand data: Insights from the newsvendor problem

<p>Stochastic inventory system simulation is often the tool of choice by industry practitioners who struggle with the evaluation of the quality of proposed inventory targets using service levels. However, driving simulations with unknown input distribution parameters has its own challenges. In this paper, we focus on the newsvendor problem and quantify the amount of demand parameter uncertainty – the uncertainty around the unknown demand distribution parameters which are estimated from the limited historical demand data – in the confidence interval of the mean service level. We use this quantification to understand how the variance of the mean service level, due to the amount of the demand parameter uncertainty in the simulation output process, changes with the choice of Type-1 and Type-2 service-level criteria, the historical data length, the ratio of the unit shortage cost to the unit holding cost, and the distributional shape of the demand’s density function.</p