A Fuzzy agent-based model for reduction of bullwhip effect in supply chain systems

Zarandi, M. H. Fazel; Pourakbar, Morteza; Türkşen, I.B.

doi:10.1016/j.eswa.2007.01.031

Cited by 56 publications

(26 citation statements)

References 36 publications

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“…De la Fuente and Lozano (2007) presented an application of Distributed Intelligence to reduce the Bullwhip Effect in a supply chain, based on a genetic algorithm. Zarandi et al (2008) introduced Fuzzy Logic in the analysis. Wu et al (2011) applied the multiagent methodology to establish a supply chain model and to analyze in detail the Bullwhip Effect created along the chain, considering the non existence of information exchange among different members.…”

Section: Multiagent Systems In the Supply Chain Managementmentioning

confidence: 99%

Multiagent Methodology to Reduce the Bullwhip Effect in a Supply Chain

Ponte

Pino

García

2014

Transactions on Computational Collective Intelligence XVII

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Abstract. There are several circumstances which, in recent decades, have granted the supply chain management a strategic role in the search for competitive advantage. One of the goals is, undoubtedly, the reduction of Bullwhip Effect, which is generated by the amplification of the variability of orders along the chain, from the customer to the factory. This paper applies multiagent methodology for reducing Bullwhip Effect. To do this, it considers the supply chain as a global multiagent system, formed in turn by four multiagent subsystems. Each one of them represents one of the four levels of the traditional linear supply chain (Shop Retailer, Retailer, Wholesaler and Factory), and it coordinates various intelligent agents with different objectives. Thus, each level has its own capacity of decision and it seeks to optimize the supply chain management. The problem is analyzed both from a non collaborative approach, where each level seeks the optimal forecasting methodology independently of the rest, and from a collaborative approach, where each level negotiates with the rest looking for the best solution for the whole supply chain.

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Section: Multiagent Systems In the Supply Chain Managementmentioning

confidence: 99%

Multiagent Methodology to Reduce the Bullwhip Effect in a Supply Chain

Ponte

Pino

García

2014

Transactions on Computational Collective Intelligence XVII

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“…Other studies, which have used fuzzy approaches for improving supply chain ordering or reducing the bullwhip effect can be found in Xiong and Helo (2006), Balan et al (2007), Zarandi et al (2008), Lin et al (2010), Wangphanich et al (2010), Cannella and Ciancimino (2010), Kristianto et al (2012) and Cannella et al (2012).…”

Section: Introductionmentioning

confidence: 97%

Fuzzy Estimations and System Dynamics for Improving Manufacturing Orders in VMI Supply Chains

Bolarín

Peidro

2014

Supply Chain Management Under Fuzziness

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In this chapter, we evaluate the behavior of fuzzy estimations of demand for releasing manufacturing orders in a Vendor-Managed Inventory (VMI) supply chain, which is based on a collaborative deal between retailer and manufacturer, and focuses on the interchange of information about demand and inventory levels. The supply chain considered consists of an end consumer, a retailer and a manufacturer. A system dynamics model with fuzzy estimations of demand has been constructed for supply chain simulation. Fuzzy numbers are used to model fuzzy estimations of demand. With a numerical example, we show that the bullwhip effect can be effectively reduced at the level where fuzzy orders exist and that the fill rate reached improves at the retailer level.

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“…The GA procedure begins with determination of the population of chromosomes [12] . One evaluates these structures and allocate reproductive opportunities in such a way that those chromosomes, which represent a better solution to the target problem, are given more chance to "reproduce" than those chromosomes, which are poorer solutions.…”

Section: Genetic Algorithm Proceduresmentioning

confidence: 99%

Study of the bullwhip effect in Chinese coal supply chain under fuzzy environment

Yun

Zhang

2016

MATEC Web Conf.

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Abstract. Supply chain management is important for coal companies and organizations to improve their business and enhance competitiveness in the Chinese marketplace. The bullwhip effect problem of coal supply chain systems with all demands, lead times, and ordering quantities in an uncertain environment is addressed in this paper. To simulate the bullwhip effect, the Hong Fuzzy Time Series approach and Genetic Algorithm module are preferred as a superior forecasting model. And then a back propagation Neural Network module is added to defuzzify the output of the proposed model. So the bullwhip effect is calculated and analyzed here. The effectiveness and flexibility of proposed method is verified through simulation study.

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A Fuzzy agent-based model for reduction of bullwhip effect in supply chain systems

Cited by 56 publications

References 36 publications

Multiagent Methodology to Reduce the Bullwhip Effect in a Supply Chain

Multiagent Methodology to Reduce the Bullwhip Effect in a Supply Chain

Fuzzy Estimations and System Dynamics for Improving Manufacturing Orders in VMI Supply Chains

Study of the bullwhip effect in Chinese coal supply chain under fuzzy environment

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