A decision support tool for operational planning: a Digital Twin using simulation and forecasting methods

Santos, Carlos; Lima, Renan Delgado Camurça; Leal, Fabiano; Queiroz, José Antônio de; Balestrassi, Pedro Paulo; Montevechi, José Arnaldo Barra

doi:10.1590/0103-6513.20200018

Cited by 17 publications

(11 citation statements)

References 30 publications

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“…Discrete event simulation is a classical and effective tool for internal transport/material handling modelling and analysis (Banks et al, 2015). Moreover, the recent introduction of the digital twin concept as a tool for logistics operations analysis (Agalianos et al, 2020;Santos et al, 2020) give DES a new application framework, individually or in conjunction with optimization models and machine learning techniques (Kosacka-Olejnik et al, 2021). Particularly, DES has been used in the past as a tool for fleet sizing in waste collection operations (Larson et al, 1991).…”

Section: Literature Reviewmentioning

confidence: 99%

Improving the tactical planning of solid waste collection with prescriptive analytics: a case study

Vargas

Díaz²,

Jaramillo³

et al. 2022

Prod.

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Paper aims: This study presents several business analytics tools that allow improving the tactical planning of the collection process for a Colombian solid-waste management company. Originality:The extant literature of operations research/analytics applied to these systems focuses on facility location or vehicle routing. Tactical decisions are seldom studied in the operations research/analytics literature devoted to waste management systems. By contrast, the focus of this paper is on tactical decisions: fleet sizing, frequency assignment, route scheduling and internal resource allocation in a new waste transfer station. Research method:We follow a multimethodology approach that uses mathematical programming, metaheuristics, and discrete event simulation. The models use historical information of the system, and the solution of a model are used as input data for the other models. Main findings:Introducing a new waste transfer station allows an important reduction of the compactors fleet. However, to prevent a collapse in its internal operation an even operation is needed. This is achieved by rescheduling the routes to balance their arrival during the day. Additional benefits can be attained if some soft constraints are relaxed.Implications for theory and practice: Practitioners looking for tactical planning tools on waste collection systems have here an example of their application and benefits. Improvements can be achieved by tactical planning without heavily disrupting decisions at the operational level.

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Section: Literature Reviewmentioning

confidence: 99%

Improving the tactical planning of solid waste collection with prescriptive analytics: a case study

Vargas

Díaz²,

Jaramillo³

et al. 2022

Prod.

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“…The forecasting method predicts future value based on a provided time series data set by making assumptions on future trends and estimating historical data. This is employed for several regions of the decision-making process, like industrial process control, risk management, operations management, demography, and economics [ 1 ]. Forecasting is an important problem spanning several domains, involving finance, social science, government, economics, environmental science, politics, medicine, business, and industry.…”

Section: Introductionmentioning

confidence: 99%

Design of a Regional Economic Forecasting Model Using Optimal Nonlinear Support Vector Machines

Zhang¹

2022

Computational Intelligence and Neuroscience

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Forecasting regional economic activity is a progressively significant element of regional economic research. Regional economic prediction can directly assist local, national, and subnational policymakers. Regional economic activity forecast can be employed for defining macroeconomic forces, such as prediction of stock market and cyclicality of national labor market movement. The recent advances of machine learning (ML) models can be employed to solve the time series prediction problem. Since the parameters involved in the ML model considerably influence the performance, the parameter tuning process also becomes essential. With this motivation, this study develops a quasioppositional cuckoo search algorithm (QOCSA) with a nonlinear support vector machine (SVM)-based prediction model, called QOCSO-NLSVM for regional economic prediction. The goal of the QOCSO-NLSVM technique is to identify the present regional economic status. The QOCSO-NLSVM technique has different stages such as clustering, preprocessing, prediction, and optimization. Besides, the QOCSO-NLSVM technique employs the density-based clustering algorithm (DBSCAN) to determine identical states depending upon the per capita NSDP growth trends and socio-economic-demographic features in a state. Moreover, the NLSVM model is employed for the time series prediction process and the parameters involved in it are optimally tuned by the use of the QOCSO algorithm. To showcase the effective performance of the QOCSO-NLSVM technique, a wide range of simulations take place using regional economic data. To determine the current economic situation in a region, the QOCSO-NLSVM technique is used. The simulation results reported the better performance of the QOCSO-NLSVM technique over recent approaches. The QOCSO-NLSVM technique generated effective results with a minimal mean square error of 70.548 or greater. Astonishingly good results were obtained using the QOCSO-NLSVM approach, which had the lowest root mean square error (RMSE) of 8.399.

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“…In contrast to the traditional piecewise linear model that allows for model changes to occur in the time space, the TAR model uses threshold space to improve the linear approximation. A time series is said to follow Forecasting methods predict values in the future based on a given time series dataset, which considers assumptions in the future by evaluating historical data (Santos et al, 2020).…”

Section: Nonlinear Time-seriesmentioning

confidence: 99%

A nonlinear time-series prediction methodology based on neural networks and tracking signals

Bianchesi

Matta

Streitenberger

et al. 2022

Prod.

Self Cite

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Paper aims: This paper presents a nonlinear time series prediction methodology using Neural Networks and Tracking Signals method to detect bias and their responsiveness to non-random changes in the time series.Originality: This study contributes with an innovative approach of nonlinear time series prediction methodology. Furthermore, the Design of Experiments was applied to simulate datasets and to analyze the results of Average Run Length, identifying in which conditions the methodology is efficient.Research method: Datasets were generated to simulate different nonlinear time series by changing the error of the series. The methodology was applied to the datasets and the Design of Experiments was implemented to evaluate the results. Lastly, a case study based on total oil and grease was performed. Main findings:The results showed that the proposed prediction methodology is an effective way to detect bias in the process when an error is introduced in the nonlinear time series because the mean and the standard deviation of the error have a significant impact on the Average Run Length. Implications for theory and practice:This study contributes to a discussion about time series prediction methodology since this new technique could be widely used in several areas to improve forecast accuracy.

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A decision support tool for operational planning: a Digital Twin using simulation and forecasting methods

Cited by 17 publications

References 30 publications

Improving the tactical planning of solid waste collection with prescriptive analytics: a case study

Improving the tactical planning of solid waste collection with prescriptive analytics: a case study

Design of a Regional Economic Forecasting Model Using Optimal Nonlinear Support Vector Machines

A nonlinear time-series prediction methodology based on neural networks and tracking signals

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