Adaptive fuzzy vendor managed inventory control for mitigating the Bullwhip effect in supply chains

Kristianto, Yohanes; Helo, Petri; Jiao, Jianxin; Sandhu, Maqsood

doi:10.1016/j.ejor.2011.07.051

Cited by 75 publications

(34 citation statements)

References 52 publications

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“…Hoque (2011) have been developed an optimal solution technique to the single-vendor multi-buyer integrated inventory supply chain by incorporating some realistic factors. Kristianto et al (2012) developed an adaptive fuzzy control application to support a vendor managed inventory (VMI). This paper also guides management in allocating inventory by coordinating suppliers and buyers to ensure minimum inventory levels across a supply chain.…”

Section: A Three Layer Supply Chain Imperfect Production Inventory Momentioning

confidence: 99%

Several Intelligent Techniques to Solve Various Warehouse Problems in Uncertain Environment

Maity¹

2015

Intelligent Techniques in Engineering Management

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In this chapter, several intelligent techniques to solve several warehouse problems have discussed in uncertain environment. In first model, analogous to chance constraints, real-life necessary and possibility constraints in the context of two warehouses multi-item dynamic production-inventory control system with imprecise holding and production costs are defined and defuzified following fuzzy relations. Hence, a realistic two warehouse multi-item production-inventory model without shortages and fuzzy constraints has been formulated and solved for optimal production with the objective of having maximum profit. Here, the rate of production is assumed to be a function of time and considered as a control variable. Also the present system produces some defective units alongwith the perfect ones and the rate of produced defective units is stochastic in nature. Here demand of the units is stock dependent and known and the defective units are selling with reduced prices. The space required per unit item, available storage space are assumed to be imprecise. The budget constraints is also imprecise. The space and budget constraints are of necessity and/or possibility types. The model is reduced to an equivalent deterministic model using necessary and possibility constrained and solved for optimum production function using Pontryagin's Optimal Control policy, the Kuhn-Tucker conditions and mathematica software 9.0. The engineering students in Optimization engineering and M.B.A.s in the business curriculum can used the model easily. In second model, a three layer supply chain production inventory model (TLSCPIM) under conditionally permissible delay in payments is formulated in fuzzy-rough and Liu uncertain environment. Supplier's supply the item at a finite rate. Manufacturer has also purchased the said item from supplier and produced the item in a certain rate which is the decision variable. Manufacturer sale his product to the retailer and also give the delay in payment to the retailer. Retailer purchase the item from manufacture and to sale the customers. Ideal costs of supplier, manufacturer and retailer have been taken into account. Also using expectation of fuzzy rough number and Liu uncertain number, the fuzzy rough

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Section: A Three Layer Supply Chain Imperfect Production Inventory Momentioning

confidence: 99%

Several Intelligent Techniques to Solve Various Warehouse Problems in Uncertain Environment

Maity¹

2015

Intelligent Techniques in Engineering Management

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“…The result, ANN is better than the conventional methods (Kihoro et al, 2004;Fradinata et al, 2015). The simulation of mitigating the bullwhip effect, allocating the safety stock, relates to the VMI to mitigate the bullwhip effect, and loss of sales by minimizing the safety inventory and considers the Burbidge effect and Houlihan effect in the research (Kristianto et al, 2012;Disney and Towill, 2003). The investigate of properties from the ground are examined by combining the stochastic data to be a variable input, using Gaussian random distribution to simulation MC distribution collaborate with the space of the entry of ANN network (Hattab et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…The bullwhip effects where Vo is the variance order and VD is the variance demand in the supply chain (Kristianto et al, 2012;Lee et al, 2004) shows in (21).…”

Section: Bullwhip Effectmentioning

confidence: 99%

Reducing the bullwhip effect from signal demand of hybrid artificial neural network models of supply chain in Indonesia

Fradinata

2017

Int. j. adv. appl. sci.

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The bullwhip effect becomes a problem for the factory to manage the inventory policy in the warehouse. This study proposes to reduce the bullwhip effect through signal demand forecast from hybrid artificial neural network (ANN) models. The original ANN is combined with analytical hierarchy process (AHP), Monte Carlo simulation (MC), and geometric random distribution at the parts of the input weight and input bias from the ANN. The variation of forecast signal demands from the hybrid models are used to reduce the variance from the signal customer demands. The results from this study, AHPiwANNb has the smallest mean square error (MSE) from signal demands, it implys that the variance signal demands should reduce the bullwhip effect (BWE) in the supply chain. It can be concluded that the small variance signal demand should reduce the bullwhip effect in the supply chain.

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“…Hariga et al (2013) considered a supply chain composed of a single vendor and multiple retailers operating under a VMI contract that specifies limits on retailers' stock levels. Kristianto et al (2012) proposed an adaptive fuzzy control application to support a VMI. The methodology implemented fuzzy control to create an adaptive smoothing constant in the forecast method, production and delivery plan to remove, for instance, the rationing and gaming or the Houlihan effect and the order batching effect or the Bullwhip effect.…”

Section: Introductionmentioning

confidence: 99%

Vendor managed inventory in multi level supply chain

Bani-Asadi¹,

Zanjani²

2017

10.5267/j.dsl

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Vendor managed inventory (VMI) is one of the most effective methods for reducing bullwhip effect. This paper presents a mathematical VMI model where there are three levels of central storage, multi distribution centers and various retailors. The problem is formulated as a mixed integer programming by considering uncertainty on different input parameters. To cope with uncertainty, the study uses rectangular fuzzy numbers. We also propose two metaheuristics; namely, genetic algorithm and particle swarm optimization to solve the resulted problems for some large instances. The preliminary results have indicated that genetic algorithm could solve the proposed model faster than particle swarm optimization in terms of CPU time reaching to slightly better objective functions.Growing Science Ltd. All rights reserved. 7

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Adaptive fuzzy vendor managed inventory control for mitigating the Bullwhip effect in supply chains

Cited by 75 publications

References 52 publications

Several Intelligent Techniques to Solve Various Warehouse Problems in Uncertain Environment

Several Intelligent Techniques to Solve Various Warehouse Problems in Uncertain Environment

Reducing the bullwhip effect from signal demand of hybrid artificial neural network models of supply chain in Indonesia

Vendor managed inventory in multi level supply chain

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