Stochastic Time Research Articles

Cluster synchronization in finite/fixed time for semi-Markovian switching T-S fuzzy complex dynamical networks with discontinuous dynamic nodes

<abstract><p>In this paper, cluster synchronization in finite/fixed time for semi-Markovian switching complex dynamical networks (CDNs) with discontinuous dynamic nodes is studied. Firstly, the global fixed-time convergence principle of nonlinear systems with semi-Markovian switching is developed. Secondly, the novel state-feedback controllers, which include discontinuous factors and integral terms, are designed to achieve the global stochastic finite/fixed-time cluster synchronization. In the framework of Filippov stochastic differential inclusion, the Lyapunov-Krasovskii functional approach, Takagi-Sugeno(T-S) fuzzy theory, stochastic analysis theory, and inequality analysis techniques are applied, and the global stochastic finite/fixed time synchronization conditions are proposed in the form of linear matrix inequalities (LMIs). Moreover, the upper bound of the stochastic settling time is explicitly proposed. In addition, the correlations among the obtained results are interpreted analytically. Finally, two numerical examples are given to illustrate the correctness of the theoretical results.</p></abstract>

Identifying Partial Topology of Complex Networks with Stochastic Perturbations and Time Delay

Identifying Partial Topology of Complex Networks with Stochastic Perturbations and Time Delay

Variability in Specific Absorption Rate From Variation in Tissue Properties

This study evaluates the variance of specific absorption rate (SAR) due to expected variance in the dielectric properties of tissues in a 3D anatomical human head model exposed to a half-wave dipole antenna at 835 and 1900 MHz. Stochastic finite difference time domain (S-FDTD) is applied to calculate variations in the local SAR, and the 1- and 10-gram averaged SAR values. These are also compared at 835 MHz to variations found from Monte Carlo FDTD. It is found that for both frequencies dielectric property variance results in a variance of peak 1- or 10-gram SAR of approximately 30% to 55% of the mean SAR, depending on the frequency. These results show that to reach 95% confidence with the calculated SAR values for evaluating exposure guidelines, statistical variations in tissue electrical properties must be taken into account.

A Sustainable Multi-Biofuel Production with Stochastic Lead Time and the Optimum Energy Utilization Under Flexible Manufacturing

A Sustainable Multi-Biofuel Production with Stochastic Lead Time and the Optimum Energy Utilization Under Flexible Manufacturing

Analytical solution and optimisation of serial supply chains with multiple nodes, lost sales, continuous review replenishment policies, stochastic lead times and external demand

Analytical solution and optimisation of serial supply chains with multiple nodes, lost sales, continuous review replenishment policies, stochastic lead times and external demand

A Simulation-based Optimization Model for the Vehicle Routing Problem with Bimodal Stochastic Travel Time Distribution

This paper proposes a simulation-based optimization model for solving a realistic delivery problem within an urban area that respects the customers’ availability. Specifically, the originality of this work is to consider the bimodal realistic traffic pattern when solving the classic capacitated vehicle routing problem with time windows. Computational results have shown that under bimodal travel time distribution the routing solution changes considerately. Thus, this work emphasizes the need to fully integrate the traffic behavior when solving this class of routing problem with stochastic travel times. Furthermore, it highlights the easiness of such integration, when the routing problem is modeled and solved using this promising simulation-based optimization approach.

A simulation-optimization approach for solving the forestry logistics problem

Solving the forestry logistic problem is involved with multiple short- and long-term operational decisions such as truck routing, inventory management, and scheduling. Although these problems are separately studied in the literature, but in many situations, a near-optimal solution of the “comprehensive” problem suffices the managerial needs and directions. In this paper, we consider the forestry logistic and use a simulation-optimization method to reach a near-optimal solution. This method enables us to incorporate different uncertainties such as stochastic travel time and occurrence of external factors. We use real-world data to evaluate our model and measure the complexity of real-world constraints.

Performance Analysis and Evaluation of Assemble-to-Order Systems With Non-Stationary Demands

Assemble-to-order (ATO) systems, which keep inventory only for components and assemble the end products after demand is realized, have become prevalent in many industries. We study multi-products and multi-components ATO systems with stochastic lead times. Independent Base Stock (IBS) policies and First-Come-First-Serve (FCFS) allocation rules are used to control component inventories. To find the optimal inventory control parameters for such systems, it is necessary to first characterize the relationship between the system performance measures such as order-based backorders and order fulfillment rate and the inventory control parameters such as reorder points. Most previous studies have focused on ATO systems where the demands are homogeneous Poisson processes. In this paper, closed-form expressions are derived for the relationship between the above performance measures and inventory control parameters where the demands are non-homogeneous. We also examine the impact of common component scarcity and demand variability. Finally, the above conclusions are illustrated by numerical examples.

A new data-driven framework to select the optimal replenishment strategy in complex supply chains

Motivated by the high variability of markets occurred in the last years, which in turns determined significant uncertainty in lead times and supply chain dynamics, this paper introduces a data-driven framework based on machine learning and metaheuristic optimization to dynamically select the most suitable replenishment strategy for a complex two-echelon (supplier-inventory-factory) supply chain (SC) problem with perishable product and stochastic lead times. Since the supplier dispatches the product (i.e., the raw material) with a fixed expiration date, the product shelf-life strictly depends on the related delivery lead time, which is subject to uncertainty. In addition, a minimum order quantity has to be fulfilled and the time between two consecutive orders cannot be less than one month. The aim of the work is to select the most suitable replenishment strategy able to minimize the average stock level, which is a surrogate cost metric, while respecting a target fill rate. Considering a smoothing order-up-to policy, the data-driven prediction-optimization framework makes use of Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) to select the best replenishment parameters (i.e., forecasting factor, proportional controller and safety stock factor) able to dynamically enhance the SC economic performance under the fill rate constraint. The ability of the framework under the predictive and the optimization perspective is assessed and a sensitivity analysis on the influence of replenishment parameters is presented as well.

Feasible Region Optimization of PI Control Parameters for Robust Tracking of First-Order Nonlinear Stochastic Time-Delay Systems

This paper discusses the feasible region of PI control parameters for first-order nonlinear stochastic time-delay systems. The system can robust asymptotically track a set-point under any PI control parameters within the feasible region. First, the first-order nonlinear stochastic time-delay systems is transformed into a linear stochastic time-delay systems with the uncertainty. Second, for the fixed PI control parameters, the maximal allowable time delay under PI control is obtained by using the genetic algorithm (GA) and the generalized eigenvalue problem. Third, a new algorithm is proposed to compute the feasible region of PI control parameters for a fixed allowable time delay. Moreover, the improved algorithm is also proposed to reduce the time complexity. In particular, the stochastic disturbance and time delay disappear, the feasible region of PI control parameters obtained by the algorithm is consistent with the previous work. Finally, two simulation examples show the feasibility and effectiveness of the proposed two algorithms, respectively.

Detecting Limit Cycles in Stochastic Time Series

Detecting Limit Cycles in Stochastic Time Series

Long-term forecasting of climatic parameters using parametric and non-parametric stochastic modelling

Climatic parameters fluctuate dynamically and their turbulences become more significant as the influence of the climate change increases. A robust model that is able to factor in the recent climate change for long-term climatic parameters forecasting is desired to strategically plan for future anthropogenic activities. In this study, two stochastic time series model, namely the seasonal auto-regressive integrated moving average (SARIMA) model and the artificial neural network (ANN) model are used to predict monthly mean temperature (Tmean), relative humidity (RH), wind speed (u) and pan evaporation (Epan) up to 12 months ahead. This study is conducted using data collected from three meteorological stations in the northern region of the Peninsular Malaysia. The stochastic models forecasted the Tmean with the highest accuracy, followed by RH, u and Epan. Besides, despite the increasing time step (from 1 to 12 months), the accuracy of the models remain consistent. However, both of the models are susceptible to the occurrence of extreme climates. In general, the SARIMA model performs better than the ANN model, probably attributed to its ability to consider the seasonality of the climatic data rather than depending solely on black-box computation.

Optimal Energy Consumption Optimization in a Smart House by Considering Electric Vehicles and Demand Response via a Hybrid Gravitational Search and Particle Swarm Optimization Algorithm

Buildings are the main energy consumers across the world, especially in urban communities. Building smartization, or the smartification of housing, therefore, is a major step towards energy grid smartization too. By controlling the energy consumption of lighting, heating, and cooling systems, energy consumption can be optimized. All or some part of the energy consumed in future smart buildings must be supplied by renewable energy sources (RES), which mitigates environmental impacts and reduces peak demand for electrical energy. In this paper, a new optimization algorithm is applied to solve the optimal energy consumption problem by considering the electric vehicles and demand response in smart homes. In this way, large power stations that work with fossil fuels will no longer be developed. The current study modeled and evaluated the performance of a smart house in the presence of electric vehicles (EVs) with bidirectional power exchangeability with the power grid, an energy storage system (ESS), and solar panels. Additionally, the solar RES and ESS for predicting solar-generated power prediction uncertainty have been considered in this work. Different case studies, including the sales of electrical energy resulting from PV panels’ generated power to the power grid, time-variable loads such as washing machines, and different demand response (DR) strategies based on energy price variations were taken into account to assess the economic and technical effects of EVs, BESS, and solar panels. The proposed model was simulated in MATLAB. A hybrid particle swarm optimization (PSO) and gravitational search (GS) algorithm were utilized for optimization. Scenario generation and reduction were performed via LHS and backward methods, respectively. Obtained results demonstrate that the proposed model minimizes the energy supply cost by considering the stochastic time of use (STOU) loads, EV, ESS, and PV system. Based on the results, the proposed model markedly reduced the electricity costs of the smart house.

Conditional Neural Process for nonparametric modeling of active galactic nuclei light curves

AbstractThe consequences of complex disturbed environments in the vicinity of a supermassive black hole are not well represented by standard statistical models of optical variability in active galactic nuclei (AGN). Thus, developing new methodologies for investigating and modeling AGN light curves is crucial. Conditional Neural Processes (CNPs) are nonlinear function models that forecast stochastic time series based on a finite amount of known data without the use of any additional parameters or prior knowledge (kernels). We provide a CNP algorithm that is specifically designed for simulating AGN light curves. It was trained using data from the All‐Sky Automated Survey for Supernovae, which included 153 AGN. We present CNP modeling performance for a subsample of five AGNs with distinctive difficult‐to‐model properties. The performance of CNP in predicting temporal flux fluctuation was assessed using a minimizing loss function, and the results demonstrated the algorithm's usefulness. Our preliminary parallelization experiments show that CNP can efficiently handle large amounts of data. These results imply that CNP can be more effective than standard tools in modeling large volumes of AGN data (as anticipated from time‐domain surveys such as the Vera C. Rubin Observatory's Legacy Survey of Space and Time).

Fractional stochastic configuration networks-based nonstationary time series prediction and confidence interval estimation

Time series prediction is an important topic in the field of data analytics for real industrial production. However, the time series from real system usually has strong nonstationarity, which affects the generalization ability of the prediction model. An improved forecasting technique, named as fractional stochastic configuration networks (FSCN), is proposed for the prediction of nonstationary time series. FSCN is built on the basis of traditional stochastic configuration network by introducing fractional differential operator. The fractional differential technique avoids the challenge of infinite variance caused by modeling nonstationary series and the over-difference problem caused by traditional integer order difference. First, several data analysis methods are introduced to find the tendency, periodicity and probability density distribution characteristics hidden in the raw industrial data. Hurst exponent is calculated to determine the order of fractional difference to eliminate the nonstationarity of the raw data. Then FSCN network is constructed to model and forecast the sequential data. An explicit prediction uncertainty is derived to provide the confidence interval for the FSCN prediction. The proposed method is tested on a nonstationary time series benchmark dataset and a real cooling system. The experiment result demonstrates that it has a good potential prediction performance compared with several traditional prediction methods.

Optimizing Customized Transit Service considering Stochastic Bus Arrival Time

The introduction of customized bus (CB) service intends to expand and elevate existing transit service, which offers an efficient and sustainable alternative to serve commuters. A probabilistic model is proposed to optimize CB service with mixed vehicle sizes in an urban setting considering stochastic bus arrival time and spatiotemporal demand, which minimizes total cost subject to bus capacity and time window constraints. The studied optimization problem is combinatorial with many decision variables including vehicle assignment, bus routes, timetables, and fleet size. A heuristic algorithm is developed, which integrates a hybrid genetic algorithm (HGA) and adaptive destroy-and-repair (ADAR) method. The efficiency of HGA-ADAR is demonstrated through numerical comparisons to the solutions obtained by LINGO and HGA. Numerical instances are carried out, and the results suggested that the probabilistic model considering stochastic bus arrival time is valuable and can dramatically reduce the total cost and early and late arrival penalties. A case study is conducted in which the proposed model is applied to optimize a real-world CB service in Xi’an, China. The relationship between decision variables and model parameters is explored. The impacts of time window and variance of bus arrival time, which significantly affect service reliability, are analysed.

Simulation-Optimization for the Planning of Off-Site Construction Projects: A Comparative Study of Recent Swarm Intelligence Metaheuristics

Off-site construction is a modern construction method that brings many sustainability merits to the built environment. However, the sub-optimal planning decisions (e.g., resource allocation, logistics and overtime planning decisions) of off-site construction projects can easily wipe away their sustainability merits. Therefore, simulation modelling—an efficient tool to consider the complexity and uncertainty of these projects—is integrated with metaheuristics, developing a simulation-optimization model to find the best possible planning decisions. Recent swarm intelligence metaheuristics have been used to solve various complex optimization problems. However, their potential for solving the simulation-optimization problems of construction projects has not been investigated. This research contributes by investigating the status-quo of simulation-optimization models in the construction field and comparing the performance of five recent swarm intelligence metaheuristics to solve the stochastic time–cost trade-off problem with the aid of parallel computing and a variance reduction technique to reduce the computation time. These five metaheuristics include the firefly algorithm, grey wolf optimization, the whale optimization algorithm, the salp swarm algorithm, and one improved version of the well-known bat algorithm. The literature analysis of the simulation-optimization models in the construction field shows that: (1) discrete-event simulation is the most-used simulation method in these models, (2) most studies applied genetic algorithms, and (3) very few studies used computation time reduction techniques, although the simulation-optimization models are computationally expensive. The five selected swarm intelligence metaheuristics were applied to a case study of a bridge deck construction project using the off-site construction method. The results further show that grey wolf optimization and the improved bat algorithm are superior to the firefly, whale optimization, and salp swarm algorithms in terms of the obtained solutions’ quality and convergence behaviour. Finally, the use of parallel computing and a variance reduction technique reduces the average computation time of the simulation-optimization models by about 87.0%. This study is a step towards the optimum planning of off-site construction projects in order to maintain their sustainability advantages.

A simulation-optimization framework for generating dynamic dispatching rules for stochastic job shop with earliness and tardiness penalties

Most of real-world problems regarding production and manufacturing contain stochastic parameters. In this research, job shop scheduling problem with stochastic process times and weighted earliness-tardiness objective function is considered. When machines are freed in the course of manufacturing, a job has to be selected from the line and passed on the machine. In deterministic job shop scheduling, the selection process is static. In the stochastic variants however, dynamic or real-time dispatching rules may be used. The goal of this paper is to develop a solution method for stochastic job shop scheduling problem that delivers dynamic and global dispatching rules that use information pertaining to the entire shop floor. In order to achieve this, the problem is converted to a near-Markov decision process that comprises an alternate sequence of states (machines becoming free) and actions (selecting a job). Using simulation and ant colony, a database of states is generated and a meaningful pheromone trail for each state is formed gradually. The pheromones act as a memory mechanism and, in combination with a heuristic based on the Central Limit Theorem, comprise the dispatching rule. Efficiency of this approach is investigated through extensive simulation. Results show an average of 92% cost reduction in comparison with the random dispatching rule. Moreover, the approach yields better results in congested scheduling systems.

Heterogeneous Route Choice Model Incorporating Group Segmentation Based on Travel Experience

Travelers make different choices about their routes based on the perceived travel time. This study aims to analyze the route choice behaviors by grouping them according to their perceived travel times. Reliability preference is a behavioral characteristic for travelers' on-time arrival according to stochastic travel time, and it is used to classify the travelers into four groups. From Dedicated Short Range Communication (DSRC) data in Daegu, 9,758 travelers are observed to analyze their route choice behaviors. A stochastic model with generated sets of choices is developed using the probabilistic characteristics of travel times. A route-choice model is also estimated for each group, and the travelers' route-choice behaviors are analyzed using a mixed path-size correction logit (MPSCL) model. The statistical analysis indicates that there are different route-choice behaviors among the four groups. The different behaviors concerning the routes are analyzed by comparing the magnitude of the different coefficients. This study contributes to constructing different strategies for acquiring information considering the route-choice behaviors of each group. Providing travel information that considers individual preferences in travel choice is essential in smart mobility transportation systems.

An Ensemble of Random Forest Gradient Boosting Machine and Deep Learning Methods for Stock Price Prediction

Stochastic time series analysis of high-frequency stock market data is a very challenging task for the analysts due to the lack availability of efficient tool and techniques for big data analytics. This has opened the door of opportunities for the developer and researcher to develop intelligent and machine learning based tools and techniques for data analytics. This paper proposed an ensemble for stock market data prediction using three most prominent machine learning based techniques. The stock market dataset with raw data size of 39364 KB with all attributes and processed data size of 11826 KB having 872435 instances. The proposed work implements an ensemble model comprises of Deep Learning, Gradient Boosting Machine (GBM) and distributed Random Forest techniques of data analytics. The performance results of the ensemble model are compared with each of the individual methods i.e. deep learning, Gradient Boosting Machine (GBM) and Random Forest. The ensemble model performs better and achieves the highest accuracy of 0.99 and lowest error (RMSE) of 0.1.