Formulating Ordering Policies in a Supply Chain by Genetic Algorithm

Chan, Chu Chai Henry; Cheng, Chun-Yuan; Huang, Sue-Fen

doi:10.1080/02286203.2006.11442360

Cited by 15 publications

(7 citation statements)

References 14 publications

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“…These types of costs are important in practice. The ordering cost exists when a player places an order [18]; the distribution cost, which consists of the shipping cost, item cost, and cargo cost [19], arises when a player delivers items to the buyer and the buyer pays for it. Finally, the production cost [20] is applied only to the factory when it orders from its product lines.…”

Section: Methodology and Implementationmentioning

confidence: 99%

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Employing genetic algorithms to minimise the bullwhip effect in a supply chain

Humphreys

Mclvor

et al. 2007

2007 IEEE International Conference on Industrial Engineering and Engineering Management

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There has been considerable research interest in the last number of years demonstrating the effectiveness of Genetic Algorithms (GAs) to reduce the Bullwhip Effect in supply chain management. One criticism of this research is that the supply chain models employed have been unrealistic and consider only a few stages within a supply chain. In this paper, the authors present an improved supply chain model, which is based on the beer game and includes additional cost factors including ordering cost, distribution cost, production cost. GAs are then employed to determine the optimal ordering policy for each member in the model. Through the experimental results, this paper demonstrates that GAs can reduce the bullwhip effect and determine the optimal ordering policy even in more complex supply chains.

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Section: Methodology and Implementationmentioning

confidence: 99%

“…This formula is provided by Chan, et al [18]. Han and Damrongwongsiri [19] suggested the following formula to calculate the distribution cost in supply chains.…”

Section: Methodology and Implementationmentioning

confidence: 99%

Employing genetic algorithms to minimise the bullwhip effect in a supply chain

Humphreys

Mclvor

et al. 2007

2007 IEEE International Conference on Industrial Engineering and Engineering Management

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“…Gill and Abend (1997) stated that the most celebrated implementation of demand information sharing is Wal-Mart's retail link program. Chan et al (2006) noted that the BWE would not exist if there were no forecast based ordering that only attempts to capture the latest demand information. Dejonckheere et al (2004) and Kelle and Milne (1999) reached the same result.…”

Section: Introductionmentioning

confidence: 99%

Minimizing the bullwhip effect in a single product multistage supply chain using genetic algorith

Mahmud¹,

Rahman²,

Hasan³

et al. 2016

10.5267/j.uscm

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Supply chain management is important for companies and organizations to improve their business and lead competitiveness in the global marketplace. But demand variations in the supply chain are significant problem for most practitioners, planners, demand managers, and operations managers. Demand variations make forecasting and inventory management more difficult and tend to increase inventory levels. The supply chain (SC) profitability can be affected by the cost associated with large inventories, transportation, and production due to the bullwhip effect. Only bullwhip effect can lead to reduce the supply chain profitability in great amount. This paper represents a computational intelligence approach, which addresses the bullwhip effect in multistage supply chain. As a computational intelligence approach, Genetic Algorithm (GA) is employed to reduce the bullwhip effect. Through this approach, optimal order quantity in each stage is to be calculated by considering cost associated with bullwhip effect. Distorted information from one end of a supply chain can lead to tremendous inefficiencies to other end. In this paper it is shown that if each player of the supply chain orders or transfers optimum quantities for the upcoming period then the bullwhip effect can be reduced significantly.

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“…Tactical and operational level decision making functions are distributed across the supply chain [2]. The main objective of SCM is to integrate all the parts in a supply chain, so as to deliver products to customer with minimal total cost of the whole system [3]. In order to optimize performance, supply chain functions must operate in an integrated manner.…”

Section: Introductionmentioning

confidence: 99%

A new diamond shape architecture based on multi agents for supply chain in an uncertain environment

Zarandi

Bahrami

Turksan

2013

2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS)

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This paper illustrates a new diamond shape architecture based on multi agents for supply chain in an uncertain environment. There are many situations in which a convincing answer to a query cannot be found in the supply chain of a company such as disruptions or sanctions, etc. Thus there is a need of connection between different supply chains of different companies in a particular industry. The proposed architecture addresses a Meta information agent as the core of the architecture in order to connect information agents of various supply chains in a same industry and keep information flowing through them. The brokering method has been used to establish this connection. Fuzzy query approach is considered to tackle uncertainty problems. The results show that our proposed approach is more reliable than other architectures and can increase the flexibility and responsiveness of a supply chain.

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Formulating Ordering Policies in a Supply Chain by Genetic Algorithm

Cited by 15 publications

References 14 publications

Employing genetic algorithms to minimise the bullwhip effect in a supply chain

Employing genetic algorithms to minimise the bullwhip effect in a supply chain

Minimizing the bullwhip effect in a single product multistage supply chain using genetic algorith

A new diamond shape architecture based on multi agents for supply chain in an uncertain environment

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