Minimizing the bullwhip effect in a single product multistage supply chain using genetic algorith (original) (raw)
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A Fuzzy agent-based model for reduction of bullwhip effect in supply chain systems
Expert Systems with Applications, 2008
This paper addresses the bullwhip effect in a multi-stage supply chain, where all demands, lead times, and ordering quantities are fuzzy. To simulate the bullwhip effect, a modified Hong Fuzzy Time Series is presented by adding a Genetic Algorithm (GA) module for gaining of a window basis. Next, a back propagation neural network is used for defuzzification. The model can forecast the trends in fuzzy data. Then, an agent-based system is developed to minimize the total cost and to reduce the bullwhip effect in supply chains. The system can suggest the reasonable ordering policies. The results show that the propose system is superior than the previous analytical methods in terms of discovering the best available ordering policies.
Inventory Analysis Using Genetic Algorithm In Supply Chain Management
International journal of engineering research and technology, 2013
With the dramatic increase in the use of the Internet for supply chain-related activities, there is a growing need for services that can analyze current and future purchases possibilities as well as current and future demand levels and determine efficient and economical strategies for the procurement of direct goods. Such solutions must take into account the current quotes offered by suppliers, likely future prices, projected demand, and storage costs in order to make effective decisions on when and from whom to make purchases. Based on demand trends and projections, there is typically a target inventory level that a business hopes to maintain. This level is high enough to be able to meet fluctuations in demand, yet low enough that unnecessary storage costs are minimized. Hence there is a necessity of determining the inventory to be held at different stages in a supply chain so that the total supply chain cost is minimized. Minimizing the total supply chain cost is meant for minimiz...
IJERT-Inventory Analysis Using Genetic Algorithm In Supply Chain Management
International Journal of Engineering Research and Technology (IJERT), 2013
https://www.ijert.org/inventory-analysis-using-genetic-algorithm-in-supply-chain-management https://www.ijert.org/research/inventory-analysis-using-genetic-algorithm-in-supply-chain-management-IJERTV2IS70550.pdf With the dramatic increase in the use of the Internet for supply chain-related activities, there is a growing need for services that can analyze current and future purchases possibilities as well as current and future demand levels and determine efficient and economical strategies for the procurement of direct goods. Such solutions must take into account the current quotes offered by suppliers, likely future prices, projected demand, and storage costs in order to make effective decisions on when and from whom to make purchases. Based on demand trends and projections, there is typically a target inventory level that a business hopes to maintain. This level is high enough to be able to meet fluctuations in demand, yet low enough that unnecessary storage costs are minimized. Hence there is a necessity of determining the inventory to be held at different stages in a supply chain so that the total supply chain cost is minimized. Minimizing the total supply chain cost is meant for minimizing holding and shortage cost in the entire supply chain. This inspiration of minimizing Total Supply Chain Cost could be done only by optimizing the base stock level at each member of the supply chain which is very dynamic. A novel and efficient approach using Genetic Algorithm has been developed which clearly determines the most possible excess stock level and shortage level that is needed for inventory optimization in the supply chain so as to minimize the total supply chain cost.
A Multi-Agent Based System for Reduction of Bullwhip Effect in Supply Chain Management
Asian Journal of Computer Science and Information Technology, 2013
Stability of supply chain is one of the major concerns to make the company moving forward and to attain the energetic behavior that describes transforms of inventory into orders over time. The bullwhip effect reveals the magnification of inventory and orders compared to consumer demand. The strategy of control has effects on the stability and bullwhip effect in supply chain system. Managing Supply Chain is very complex as it is a set of connections which involves multiple entities such as suppliers, manufactures, distributors, and retailers, encompassing their activities of moving goods and adding value from the raw material stage to the final delivery stage. Every chain has its own unique set of market demands and operating challenges. Retailing is one such service domain of Supply Chain vulnerable to bullwhip effects. Demand uncertainty is one of the root causes of Bullwhip effects. Finally a thought on new directions in bullwhip research is presented in this paper
Reducing the Bullwhip effect in a supply chain network by application of optimal control theory
RAIRO - Operations Research, 2018
Controlling the bullwhip effect and reducing the propagated inventory levels throughout the supply chain layers has an important role in reducing the total inventory costs of a supply chain. In this study, an optimal controller that considers demand as control variable is designed to dampen propagated inventory fluctuations for each node throughout the supply chain network. The model proves to be very useful in revealing the dynamic characteristics of the chain and provides a proper interface to study decisions taken into account at each node of the supply chain in different periods by decision makers (DMs). In the proposed approach, two feedback loops and online updated values of net stock quantities are used for calculation of the orders. To investigate the efficiency of the proposed approach, a real case of bicycle industry is conducted. The acquired results justify the efficiency of the proposed approach in controlling and dampening the bullwhip effect and reducing inventory lev...
Genetic algorithms in supply chain management: A critical analysis of the literature
Genetic algorithms (GAs) are perhaps the oldest and most frequently used search techniques for dealing with complex and intricate real-life problems that are otherwise difficult to solve by the traditional methods. The present article provides an extensive literature review of the application of GA on supply chain management (SCM). SCM consists of several intricate processes and each process is equally important for maintaining a successful supply chain. In this paper, eight processes (where each process has a set of sub-processes) as given by Council of SCM Professionals (CSCMF) are considered. The idea is to review the application of GA on these aspects and to provide the readers a detailed study in this area. The authors have considered more than 220 papers covering a span of nearly two decades for this study. The analysis is shown in detail with the help of graphs and tables. It is expected that such an extensive study will encourage and motivate the fellow researchers working in related area; to identify the gaps and to come up with innovative ideas.
A Multi-Stage Supply Chain Network Optimization Using Genetic Algorithms
In today's global business market place, individual firms no longer compete as independent entities with unique brand names but as integral part of supply chain links. Key to success of any business is satisfying customer's demands on time which may result in cost reductions and increase in service level. In supply chain networks decisions are made with uncertainty about product's demands, costs, prices, lead times, quality in a competitive and collaborative environment. If poor decisions are made, they may lead to excess inventories that are costly or to insufficient inventory that cannot meet customer's demands.
2016
The Bullwhip Effect is a major problem in supplier and forecast driven industries. The aim of this paper is to understand the nature of bullwhip effect and reduce the bullwhip effect in supply chain of manufacturing industries which dependent highly on many numbers of Suppliers. The fast moving or highly demanded Product which has highest annual consumption is selected for bullwhip effect analysis from all the products produced in one of the glass manufacturing industry (OEM) by using ABC analysis. For ABC analysis last 12 months demands of all products are used. Different forecasting methods accuracy is checked and accurate forecasting method is selected for the Product. Bullwhip effect is simulated for OEM demand of finished Products with respect to Customer demand. Due to bullwhip effect upper of stage of OEM means raw material demand of OEM is highly fluctuated. Selected accurate forecasting method is implemented in actual production of Product. After applying accurate forecasting method OEM demand of finished Products and demand of each raw of materials for the Product both Simulated. Results shows that after choosing accurate forecasting method for the Product the fluctuations in demand of finished Products and demand of raw of materials both are highly reduced means demands become stabilized which is most profitable for OEM suppliers as well as for OEM.
Supply chain optimisation using evolutionary algorithms
International Journal of …, 2008
This paper describes the application of Evolutionary Algorithms (EAs) to the optimisation of a simplified supply chain in an integrated production-inventory-distribution system. The performance of four EAs (Genetic Algorithm (GA), Evolutionary Programming (EP), Evolution Strategies (ES) and Differential Evolution (DE)) was evaluated with numerical sumulations. Results were also compared with other similar approach in the literature. DE was the algorithm that led to better results, outperforming previously published solutions. The robustness of EAs in general, and the efficiency of DE, in particular, suggest their great utility for the supply chain optimisation problem, as well as for other logistics-related problems.
Inventory parameters for a serial supply chain with lost sales through genetic algorithm approach
International Journal of Enterprise Network Management, 2018
Supply chain is a network of facilities with varying conflicting objectives and decision making is a complex process. The uncertainty in demand increases complexity in inventory control mechanism. Backordering, partial backordering and lost-sales are considered in the inventory management to characterise the excess demand. In the present competitive scenario, mostly consumers have no patience to wait and show urgency to buy their goods failing which the management has to meet a huge loss of supply chain members and hence the profit. There are very few research works regarding lost-sales parameter in the area of multi-echelon inventory systems. It is felt that the proposed modified gene-wise genetic algorithm (MGGA) supply chain model which so far not applied in this process may help to determine best base stock levels and review periods with lost sales particularly at retailer end which minimised total supply chain cost.