Supply Chain Simulation and Optimization Methods: An Overview

2012 ◽  
Author(s):  
Siti Norsyahida Othman ◽  
Noorfa Haszlinna Mustaffa
Keyword(s):  
Supply Chain ◽  
Optimization Methods ◽  
Simulation And Optimization ◽  
Supply Chain Simulation

Multienterprise supply chain: Simulation and optimization

AIChE Journal ◽  
2016 ◽  
Vol 62 (9) ◽  
pp. 3392-3403 ◽  
Author(s):  
Nihar Sahay ◽  
Marianthi Ierapetritou
Keyword(s):  
Supply Chain ◽  
Simulation And Optimization ◽  
Supply Chain Simulation

scholarly journals Smart Master Production Schedule for the Supply Chain: A Conceptual Framework

Computers ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 156
Author(s):  
Julio C. Serrano-Ruiz ◽  
Josefa Mula ◽  
Raúl Poler
Keyword(s):  
Supply Chain ◽  
Conceptual Framework ◽  
Control Process ◽  
Optimization Methods ◽  
Production Schedule ◽  
Service Levels ◽  
Simulation And Optimization ◽  
Starting Point ◽  
Master Production Schedule ◽  
The One

Risks arising from the effect of disruptions and unsustainable practices constantly push the supply chain to uncompetitive positions. A smart production planning and control process must successfully address both risks by reducing them, thereby strengthening supply chain (SC) resilience and its ability to survive in the long term. On the one hand, the antidisruptive potential and the inherent sustainability implications of the zero-defect manufacturing (ZDM) management model should be highlighted. On the other hand, the digitization and virtualization of processes by Industry 4.0 (I4.0) digital technologies, namely digital twin (DT) technology, enable new simulation and optimization methods, especially in combination with machine learning (ML) procedures. This paper reviews the state of the art and proposes a ZDM strategy-based conceptual framework that models, optimizes and simulates the master production schedule (MPS) problem to maximize service levels in SCs. This conceptual framework will serve as a starting point for developing new MPS optimization models and algorithms in supply chain 4.0 (SC4.0) environments.

scholarly journals A Comprehensive Study on the Optimal Design of Magnetorheological Dampers for Improved Damping Capacity and Dynamical Adjustability

Actuators ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 64
Author(s):  
Liankang Wei ◽  
Hongzhan Lv ◽  
Kehang Yang ◽  
Weiguang Ma ◽  
Junzheng Wang ◽  
...  
Keyword(s):  
Optimal Design ◽  
Optimization Methods ◽  
Design Stage ◽  
Damping Capacity ◽  
Damping Force ◽  
Simulation And Optimization ◽  
Damping Characteristics ◽  
Hysteretic Characteristics ◽  
Gap Width ◽  
Comprehensive Study

Purpose: We aim to provide a systematic methodology for the optimal design of MRD for improved damping capacity and dynamical adjustability in performing its damping function. Methods: A modified Bingham model is employed to model and simulate the MRD considering the MR fluid’s compressibility. The parameters that describe the structure of MRD and the property of the fluid are systematically examined for their contributions to the damping capacity and dynamically adjustability. A response surface method is employed to optimize the damping force and dynamically adjustable coefficient for a more practical setting related to the parameters. Results: The simulation system effectively shows the hysteretic characteristics of MRDs and shows our common sense understanding that the damping gap width and yoke diameter have significant effects on the damping characteristics of MRD. By taking a typical MRD device setup, optimal design shows an increase of the damping force by 33% and an increase of the dynamically adjustable coefficient by 17%. It is also shown that the methodology is applicable to other types of MDR devices. Conclusion: The compressibility of MR fluid is one of the main reasons for the hysteretic characteristics of MRD. The proposed simulation and optimization methods can effectively improve the MRD’s damping performance in the design stage.

SISCO: An object-oriented supply chain simulation system

2006 ◽  
Vol 42 (1) ◽  
pp. 422-434 ◽  
Author(s):  
Dean C. Chatfield ◽  
Terry P. Harrison ◽  
Jack C. Hayya
Keyword(s):  
Supply Chain ◽  
Object Oriented ◽  
Simulation System ◽  
Supply Chain Simulation

Experiences in Modeling, Simulation, and Optimization of Chemical Industry Supply Chains

Manufacturing ◽  
2002 ◽  
Author(s):  
Charles R. Standridge ◽  
David R. Heltne
Keyword(s):  
Supply Chain ◽  
Simulation Model ◽  
Production Planning ◽  
Simulation Optimization ◽  
Process Industry ◽  
Plant Production ◽  
Optimization Models ◽  
Individual Plant ◽  
Simulation And Optimization ◽  
Combined Simulation

We have developed and applied simulation as well as combined simulation – optimization models to represent process industry plant logistics and supply chain operations. The simulation model represents plant production, inventory, and shipping operations as well as inter-plant shipments. When a combined simulation-optimization approach is used, the simulation periodically invokes a classical production planning optimization model to set production and shipping levels. These levels are retrieved by and used in the simulation model. Process industry supply chain operations include stochastic elements such as customer demands whose expected values may vary in time as well as transportation lead times. The complexity of individual plant operations and logistics must be considered. Simulation provides the methods needed to integrate these elements in a single model. Periodically during a simulation run, production planning decisions that require optimization models may be made. Simulation experimental results are used to determine service levels to end customers as well as to set rail fleet sizes, inventory capacities, and capital equipment requirements for logistics as well as to assess alternative shipping schedules.

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