Some comments on normal approximation for stochastic demand processes

Girlich, Hans-Joachim

doi:10.1016/0925-5273(94)00130-8

Cited by 7 publications

(5 citation statements)

References 8 publications

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“…The normal distribution is widely used in the literature to model demand uncertainty. − Clients’ behavior in general corresponds to this type of function. , Additionally, the central limit theorem supports the idea of using a normal distribution to represent demand under uncertainty . For those reasons, this distribution was assumed in this article to model the demand parameter as a random exogenous variable.…”

Section: Problem Descriptionmentioning

confidence: 96%

Multicriteria Optimization Model for Supply Process Problem under Provision and Demand Uncertainty

Rodríguez

Vecchietti

2011

Ind. Eng. Chem. Res.

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Supply processes play an important role in customer satisfaction and company costs. The main characteristics of this problem are given by several decisions that follow a hierarchical structure and a very uncertain context, conditioning the success of the solutions proposed. Two significant sources of uncertainty are considered in this work, namely, provision and demand, both modeled as exogenous variables with random behavior. An optimization model is formulated to reduce the effects of the uncertainty in the company supply process. Because of the problem complexity, a multicriteria model is required to bring a comprehensive solution. Several Pareto-optimal solutions are obtained through application of the ε-constraint technique. The original formulation is a nonconvex one that is then transformed to obtain a disjunctive linear model that guarantees a global result.

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Section: Problem Descriptionmentioning

confidence: 96%

Multicriteria Optimization Model for Supply Process Problem under Provision and Demand Uncertainty

Rodríguez

Vecchietti

2011

Ind. Eng. Chem. Res.

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“…Effective business strategies play important roles in the successful operation of modern supply chain management, and can result in increase profit and high customer satisfaction. In this direction, some researchers (see Olsson, & Hill, 2007;Grosfeld-Nir, Anily, & Ben-Zvi, 2006;Nair, & Tarasewich, 2003;Krishnan, Kapusciniski, & Butz, 2004;Szmerekovsky, & Zhang, 2009;Ramanathan, & Muyldermans, 2010;Xie, & Wei, 2009;Lagodimos, 1993;Girlich, 1996;Wang, & Su, 2007;You, & Grossmann, 2008;Hosoda, & Disney, 2012) investigated inventory models under various demand rates. Olsson and Hill (2007) framed a two-echelon base-stock inventory model with Poisson demand and the sequential processing of orders at the upper echelon.…”

Section: Journal Of Management Analytics 73mentioning

confidence: 99%

Two-echelon production-inventory system with fuzzy production rate and promotional effort dependent demand

Priyan

Palanivel

Uthayakumar

2015

Journal of Management Analytics

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Intelligent manufacturing design of a complex production-inventory system becomes a key issue for the organization of responsiveness to uncertainties. This paper addresses a two-echelon production-inventory model for a non-repairable product where the system consists of single manufacturer and single retailer. The manufacturer procures raw material (which also contains imperfect raw materials) from an outside supplier then proceeds to convert perfect-quality raw material as a finished product, and finally delivers to the retailer. In this study we assume that the demand is sensitive to promotional efforts/sales teams' initiatives and the production rate is uncertain but possible to describe with a triangular fuzzy number. Then we use the signed distance method to defuzzify the fuzzy joint total cost and an analytical method is employed to achieve the optimal solutions so that the total costs of both manufacturer and retailer are minimized. An efficient algorithm is developed to design an intelligent manufacturing strategy such as optimal production lot-size, backlogging and the initiatives of sales teams. A numerical example and sensitivity analysis are given to demonstrate the application of the proposed model.

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“…According to research conducted by Girlich (1996) at the University of Leipzig in Germany [6], stochastic demand processes must first establish the domestic demand processes which can be approximated by special Gaussian processes and then review the single product or service model, through a Gaussian process, specifically Wiener processes as a demand process.…”

Section: Stochastic Demand Processesmentioning

confidence: 99%

“…Compound Poisson demand process [6]: In products that have low demand, these demand occurs in (0, )in sizes 1 , … , ( ) , where ( )denotes the random nu mber of demand cases until . We say that is a Poisson process with the parameter if for the increments ( , ) = ( ) − ( ), 0 < < , it holds: .…”

Section: Stochastic Demand Processesmentioning

confidence: 99%

See 1 more Smart Citation

Dynamic Structure to Define a Corporate Channel for Courier Companies

Rey¹,

Triviño²

2012

AJOR

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The document presents a stochastic linear programing model, providing a network structure to meet corporate clients to courier co mpanies as a strategy of upkeep and increment in revenue. By reviewing the state of art about location models in different outlets in retail businesses over time, we take a courier co mpany to define a location model of retail sales points, based on major revenue customer, the result g ives optional strategies for network structure, where the model outputs are validated under changes in demand and network capacity in d ifferent scenarios, generating strategic options to be imp lemented in co mpanies with the same characteristics. The pro ject finds a form to eliminate the subjectivity that exists in the decision-making with location sales points problems in retail services companies.

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Some comments on normal approximation for stochastic demand processes

Cited by 7 publications

References 8 publications

Multicriteria Optimization Model for Supply Process Problem under Provision and Demand Uncertainty

Multicriteria Optimization Model for Supply Process Problem under Provision and Demand Uncertainty

Two-echelon production-inventory system with fuzzy production rate and promotional effort dependent demand

Dynamic Structure to Define a Corporate Channel for Courier Companies

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