Monte Carlo simulation and genetic algorithm for optimising supply chain management in a stochastic environment

2002 ◽  
Author(s):  
O. Jellouli ◽  
E. Chatelet
Keyword(s):  
Genetic Algorithm ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Stochastic Environment ◽  
Chain Management

scholarly journals SUPPLY CHAIN RISK MANAGEMENT BY MONTE CARLO METHOD

2017 ◽  
Vol 7 (4) ◽  
pp. 20-23
Author(s):  
Tomasz Rymarczyk ◽  
Grzegorz Kłosowski
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Supply Chain ◽  
Cost Estimation ◽  
Historical Data ◽  
Supply Chain Risk Management ◽  
Supply Chain Risk ◽  
Chain Management

In this paper, the conceptual model of risk-based cost estimation for completing tasks within supply chain is presented. This model is a hybrid. Its main unit is based on Monte Carlo Simulation (MCS). Due to the fact that the important and difficult to evaluate input information is vector of risk-occur probabilities the use of artificial intelligence method was proposed. The model assumes the use of fuzzy logic or artificial neural networks – depending on the availability of historical data. The presented model could provide support to managers in making valuation decisions regarding various tasks in supply chain management.

scholarly journals Algorithm and mathematical model of supply chain management for raw wood from the regions in Russia: Сomparison and analysis

2020 ◽  
Vol 15 (3) ◽  
pp. 385-404
Author(s):  
Rodion Sergeevich Rogulin ◽  
◽  
Lev Solomonovich Mazelis ◽  
Keyword(s):  
Genetic Algorithm ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Branch And Bound ◽  
Raw Materials ◽  
Mixed Integer ◽  
Branch And Bound Method ◽  
Chain Management ◽  
Industrial Enterprises ◽  
Non Linear

Supply chain management is a burning issue for modern industrial enterprises. To handle this issue, non-linear stochastic models are successfully applied to find the reasonable and efficient solutions. A need to develop a unique method to find the solutions to supply chain management tasks defined as stochastic mixed-integer non-linear programming tasks is determined by the limitations imposed by the general models. The sum of the total raw procurement costs from the Commodity Exchange over the defined planning horizon is taken to be the target function of the unique model, while the binary variables which show whether a purchasing order is included into the procurement plan are used for optimization purposes. Some parameters of model’s limitations are stochastic and consider the uncertainty factor and risks in supplying the required raw materials to the manufacturing site. Branch-and-bound and genetic algorithms are applied at some steps in the developed heuristic algorithm. The algorithm and the model are tested at a major timber processing enterprise in Primorsky Area. Four types of processors over three planning horizons were applied to compare the efficiency of the proposed algorithm with partial application of the genetic algorithm or branch-and-bound method. The findings analysis shows that, unlike the genetic algorithm, the unique one is more stable in terms of uncertainty of the input parameters in comparison with the branch-and-bound method. It provides the solutions in the models with a great number of variables. The algorithm is shown to be universal enough for its further modification in solving more complicated problems of the same class, containing a significantly larger number of probabilistic parameters that describe other uncertainties in the supply of raw materials. Further research is seen to include the development of the proposed algorithm to increase the rate of convergence for its better efficiency.

Genetic Algorithm Approach for Inventory and Supply Chain Management

2018 ◽  
pp. 468-478
Author(s):  
Poonam Prakash Mishra
Keyword(s):  
Genetic Algorithm ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Cost Effective ◽  
Business Community ◽  
Complex Nature ◽  
Chain Management ◽  
Genetic Algorithm Approach ◽  
Intelligence Approach

Inventory and supply chain management is a real concern for business community in today's globally competitive scenario. Various inventory models are proposed, significant parameters are analysed and finally optimized by researchers in order to give managers an insight for the different parameters. Mathematical and logical analysis of different inventory and supply chain models helps mangers in overall cost reduction and further higher revenue generation. Members often encounter conflicting interest and unforeseen scenario. So, all this make supply chain very complex and dynamic process. Complex and uncertain nature of inventory and supply chain, many times either it is not feasible to solve the issue with traditional methods or it is not cost effective. Thus many researchers are using artificial intelligence approach for investigation. Genetic algorithm is one among them that works efficiently with complex nature of the inventory and supply chain management. This article provides an up to date review about the role of GA in overall inventory and supply chain management.

Genetic Algorithm Approach for Inventory and Supply Chain Management

2021 ◽  
pp. 1175-1185
Author(s):  
Poonam Prakash Mishra
Keyword(s):  
Genetic Algorithm ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Cost Effective ◽  
Business Community ◽  
Complex Nature ◽  
Chain Management ◽  
Genetic Algorithm Approach ◽  
Intelligence Approach

Inventory and supply chain management is a real concern for business community in today's globally competitive scenario. Various inventory models are proposed, significant parameters are analysed and finally optimized by researchers in order to give managers an insight for the different parameters. Mathematical and logical analysis of different inventory and supply chain models helps mangers in overall cost reduction and further higher revenue generation. Members often encounter conflicting interest and unforeseen scenario. So, all this make supply chain very complex and dynamic process. Complex and uncertain nature of inventory and supply chain, many times either it is not feasible to solve the issue with traditional methods or it is not cost effective. Thus many researchers are using artificial intelligence approach for investigation. Genetic algorithm is one among them that works efficiently with complex nature of the inventory and supply chain management. This article provides an up to date review about the role of GA in overall inventory and supply chain management.

scholarly journals Gated Recurrent Unit with Genetic Algorithm for Product Demand Forecasting in Supply Chain Management

Mathematics ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 565 ◽  
Author(s):  
Jiseong Noh ◽  
Hyun-Ji Park ◽  
Jong Soo Kim ◽  
Seung-June Hwang
Keyword(s):  
Genetic Algorithm ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Demand Forecasting ◽  
Window Size ◽  
Batch Size ◽  
Chain Management ◽  
Product Demand ◽  
Forecasting Models ◽  
Gated Recurrent Unit

Product demand forecasting plays a vital role in supply chain management since it is directly related to the profit of the company. According to companies’ concerns regarding product demand forecasting, many researchers have developed various forecasting models in order to improve accuracy. We propose a hybrid forecasting model called GA-GRU, which combines Genetic Algorithm (GA) with Gated Recurrent Unit (GRU). Because many hyperparameters of GRU affect its performance, we utilize GA that finds five kinds of hyperparameters of GRU including window size, number of neurons in the hidden state, batch size, epoch size, and initial learning rate. To validate the effectiveness of GA-GRU, this paper includes three experiments: comparing GA-GRU with other forecasting models, k-fold cross-validation, and sensitive analysis of the GA parameters. During each experiment, we use root mean square error and mean absolute error for calculating the accuracy of the forecasting models. The result shows that GA-GRU obtains better percent deviations than other forecasting models, suggesting setting the mutation factor of 0.015 and the crossover probability of 0.70. In short, we observe that GA-GRU can optimally set five types of hyperparameters and obtain the highest forecasting accuracy.

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