Fuzzy-genetic approach to aggregate production–distribution planning in supply chain management

Aliev, R. A.; Fazlollahi, Bijan; Guirimov, B. G.

doi:10.1016/j.ins.2007.04.012

Cited by 207 publications

(100 citation statements)

References 29 publications

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“…In [5] a type-1 fuzzy system is used with a Genetic Algorithm (GA) to model a SC. The GA searches for a configuration that maximises profit, while meeting a target fillrate specified by the user; fuzzy sets are used to describe costs, returns, production capacities, storage capacities and forecasts.…”

Section: Fuzzy Resource Model Optimisationmentioning

confidence: 99%

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Inventory optimisation with an Interval Type-2 Fuzzy model

Miller

Góngora

John

2010

International Conference on Fuzzy Systems

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Abstract-The planning of resources within a supply chain can prove to be a deciding factor in the success or failure of an operation. This research continues the authors' previous work using an extended Interval Type-2 Fuzzy Logic supply chain model, with an Evolutionary Algorithm to search for good resource plans. A set of enhanced experiments is conducted to validate our novel approach with optimal configurations, and determine an appropriate Evolutionary Algorithm set up for the given problem.

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Section: Fuzzy Resource Model Optimisationmentioning

confidence: 99%

“…The crisp system is unable to produce a feasible configuration if the actual demand is lower than the forecast. In contrast, the fuzzy model presented in [5] is robust and able to cope with fluctuation in demand and production capacity with little impact on profitability.…”

Section: Fuzzy Resource Model Optimisationmentioning

confidence: 99%

Inventory optimisation with an Interval Type-2 Fuzzy model

Miller

Góngora

John

2010

International Conference on Fuzzy Systems

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“…In order to reflect the collaborative planning issues to their model and to provide a more realistic model structure, decision makers' imprecise aspiration levels for the goals are incorporated into the model using a fuzzy goal programming approach. Aliev et al (2007) developed a fuzzy integrated multi-period and multi-product production and distribution model since there is a need for a joint general strategic plan for production and distribution model in a supply chain. In the current study, our proposed fuzzy parameters and objective functions are very close to Liang (2008Liang ( , 2011.…”

Section: Introductionmentioning

confidence: 99%

A fuzzy solution approach for a multi-objective integrated production-distribution model with multi products and multi periods under uncertainty

Gharehyakheh¹,

Tavakkoli–Moghaddam²

2012

MSL

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This paper considers a multi-objective integrated production-distribution problem (IPDP) for multi-product and multi-production facility with limited capacity vehicles over a multi-period horizon in a two-level supply chain. In order to consider uncertainty of a real supply chain, some fuzzy parameters are considered for costumer demands, machine and labor levels of each manufacturer. The proposed model minimizes total production, inventory and distribution costs and total delivery time simultaneously, and the performance of the proposed model is evaluated on several randomly generated instances. The results show that integrating production and distribution decisions is more efficient than making these two decisions, separately.

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“…Therefore, Fuzzy Logic and especially Type-2 Fuzzy Logic (T2FL) are particularly appropriate for this problem. While traditional (or Type-1 (T1)) Fuzzy Logic (T1FL) has successfully been used many times for modelling supply chain operation (e.g., Petrovic et al (2008) and Aliev et al (2007)), T2FL has been shown to offer a better representation of uncertainty on a number of problems (e.g., Hagras (2004) and Karnik and Mendel (1999)) as it is able to retain more information about uncertainty and, unlike T1, represent the linguistic uncertainty and multiple perceptions of realworld terms. Both of these advantages are particularly appropriate to the problem of inventory management.…”

Section: Introductionmentioning

confidence: 99%

Interval type-2 fuzzy modelling and stochastic search for real-world inventory management

et al. 2012

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Real-world systems present a variety of challenges to the modeller, not least of which is the problem of uncertainty inherent in their operation. In this research, an Interval Type-2 Fuzzy model is applied to a real-world problem, the goal being to discover a suitable optimisation configuration to enable a search for an inventory plan using the model. To this end, a series of Simulated Annealing configurations and the Interval Type-2 Fuzzy model were used to search for appropriate inventory plans for a large-scale real-world problem. A further set of tests were conducted in which the performance of the Interval Type-2 Fuzzy model was compared with a corresponding Type-1 Fuzzy model. In these tests the results were inconclusive, though, as will be discussed there are many ways in which Type-2 Fuzzy Logic can be exploited to demonstrate its advantages over a Type-1 approach. To conclude, in this research we have shown that a combination of Interval Type-2 Fuzzy Logic and Simulated Annealing is a logical choice for inventory management modelling and inventory plan search, and propose that the bene-

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Fuzzy-genetic approach to aggregate production–distribution planning in supply chain management

Cited by 207 publications

References 29 publications

Inventory optimisation with an Interval Type-2 Fuzzy model

Inventory optimisation with an Interval Type-2 Fuzzy model

A fuzzy solution approach for a multi-objective integrated production-distribution model with multi products and multi periods under uncertainty

Interval type-2 fuzzy modelling and stochastic search for real-world inventory management

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