Spatio-Temporal State Space Model Application in the Dynamic Supply Chain System

2017 ◽  
Author(s):  
Phisut Apichayakul
Keyword(s):  
Supply Chain ◽  
State Space ◽  
State Space Model ◽  
Supply Chain System ◽  
Model Application ◽  
Space Model ◽  
Chain System ◽  
Spatio Temporal

scholarly journals Dynamic Complexities in a Supply Chain System with Lateral Transshipments

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Yongchang Wei ◽  
Fangyu Chen ◽  
Feng Xiong
Keyword(s):  
Supply Chain ◽  
Customer Service ◽  
State Space Model ◽  
Bullwhip Effect ◽  
Performance Measurements ◽  
Supply Chain System ◽  
Chain System ◽  
Customer Service Level ◽  
Lateral Transshipment ◽  
The Stability

The horizontal interaction between retailers, coupled with replenishment rules and time delays, makes the dynamics in supply chain systems highly complicated. This paper aims to explore the impacts of lateral transshipments on the stability, bullwhip effect, and other performance measurements in the context of a two-tiered supply chain system composed of one supplier and two retailers. In particular, we developed a unified discrete-time state space model to address two different scenarios of placing orders. Analytical stability results are derived, through which we found that inappropriate lateral transshipment policies readily destabilize the supply chain system. Moreover, the lead time of lateral transshipments further complicates the stability problem. Theoretical results are validated through simulation experiments and the influences of system parameters on performance measures are investigated numerically. Numerical simulations show that lateral transshipments help improve the customer service level for both retailers. It is also interesting to observe that the demand of the two retailers can be satisfied even if only one retailer places orders from the upstream supplier.

scholarly journals Constructing the Singular Roesser State-Space Model Description of 3D Spatio-Temporal Dynamics From the Polynomial System Matrix

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 45632-45641
Author(s):  
Mohamed S. Boudellioua ◽  
Bartlomiej Sulikowski ◽  
Krzysztof Galkowski ◽  
Eric Rogers
Keyword(s):  
State Space ◽  
Temporal Dynamics ◽  
Polynomial System ◽  
State Space Model ◽  
Model Description ◽  
System Matrix ◽  
Space Model ◽  
Spatio Temporal

Spatio-temporal statistical models for river monitoring networks

2006 ◽  
Vol 53 (1) ◽  
pp. 9-15 ◽  
Author(s):  
L. Clement ◽  
O. Thas ◽  
P.A. Vanrolleghem ◽  
J.P. Ottoy
Keyword(s):  
Water Quality ◽  
State Space ◽  
River Water ◽  
Spatial Dependence ◽  
State Space Model ◽  
Monitoring Network ◽  
The State ◽  
Dependence Structure ◽  
Space Model ◽  
Spatio Temporal

When introducing new wastewater treatment plants (WWTP), investors and policy makers often want to know if there indeed is a beneficial effect of the installation of a WWTP on the river water quality. Such an effect can be established in time as well as in space. Since both temporal and spatial components affect the output of a monitoring network, their dependence structure has to be modelled. River water quality data typically come from a river monitoring network for which the spatial dependence structure is unidirectional. Thus the traditional spatio-temporal models are not appropriate, as they cannot take advantage of this directional information. In this paper, a state-space model is presented in which the spatial dependence of the state variable is represented by a directed acyclic graph, and the temporal dependence by a first-order autoregressive process. The state-space model is extended with a linear model for the mean to estimate the effect of the activation of a WWTP on the dissolved oxygen concentration downstream.

A state-space model of a three tier APVIOBPCS supply chain

2015 ◽  
Vol 10 (1) ◽  
pp. 76-104 ◽  
Author(s):  
Anthony S White ◽  
Michael Censlive
Keyword(s):  
Supply Chain ◽  
State Space ◽  
Production Control ◽  
Production Systems ◽  
State Space Model ◽  
Chain Model ◽  
Content Type ◽  
Space Model ◽  
Production Delay ◽  
Parameter Values

Purpose – The purpose of this paper is to investigate a control engineering-based system model that allows for any value of production delay for a three-tier supply chain with information delays between tiers or systems with epos. Design/methodology/approach – A discrete z transform model of automatic pipeline, variable inventory and order based production control system three-tier supply chain is obtained using a state-space model using a reachable control formulation. This model provides a discrete time state-space model conversion using an exponential production delay with no loss of generality. Findings – This work allows a three-tier supply chain model to be computed via a spreadsheet using any production delay and can be modified to include different sales smoothing procedures. The model is fully controllable and observable. Stability of these models is obtained from the system eigenvalues and agrees with our previously published stability boundaries. Practical implications – The system is described by a linear control model of the production process and does not include production limits or other resource limitations, including history of sales demand and response. Originality/value – This present model is an extension of the model devised by White and Censlive, in that it allows accurate modelling of multi-tier inventory production systems by permitting flexible selection of delay parameter values for real systems.

scholarly journals Spatio-Temporal Dynamic Fields Estimating and Modeling of Missing Points in Data Sets Using a Flexible State-Space Model

2021 ◽  
Vol 11 (19) ◽  
pp. 9050
Author(s):  
Zhichao Shi ◽  
Xiaoguang Zhou
Keyword(s):  
Time Series ◽  
State Space ◽  
State Space Model ◽  
Universal Kriging ◽  
Statistical Characteristics ◽  
Data Sets ◽  
Observation Data ◽  
Temporal Dynamic ◽  
Space Model ◽  
Spatio Temporal

Modelling and estimating spatio-temporal dynamic field are common challenges in much applied research. Most existing spatio-temporal interpolation methods require massive prior calculations and consistent observational data, resulting in low interpolation efficiency. This paper presents a flexible state-space model for iteratively fitting time-series from random missing points in data sets, namely Flexible Universal Kriging state-space model(FUKSS). In this work, a recursive method similar to Kalman filter is used to estimate the time-series, avoiding the problem of increasing data caused by Kriging space-time extension. Based on the statistical characteristics of Kriging, this method introduces a spatial selection matrix to make the different observation data and state vectors identical at different times, which solves the problem of missing data and reduces the calculation complexity. In addition, a dynamic linear autoregressive model is introduced to solve the problem that the universal Kriging state-space model cannot predict. We have demonstrated the superiority of our method by comparing it with different methods through experiments, and verified the effectiveness of this method through practical cases.

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