The use of goal programming and data envelopment analysis for estimating efficient marginal costs of outputs

Giokas, Dimitriοs

doi:10.1057/palgrave.jors.2600376

Cited by 17 publications

(18 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This operation is maximized or minimized by a group of constraints incompatible between equations [5]. The algorithm must be able to choose the constrain to be violated in order to reach a feasible solution.…”

Section: Goal Programingmentioning

confidence: 99%

Direct stockpile scheduling: Mathematical formulation

Souza¹,

Chaves²,

Burgarelli³

et al. 2018

DYNA

View full text Add to dashboard Cite

In a mining context, production scheduling's main objective is to determine the best mining sequence of blocks to achieve the largest net present value and to maximize ore reserve exploitation. Stockpiling and blending procedures may represent very helpful alternatives for mine planning to ensure the ore quality and amount required by the processing plant. In order to satisfy industrial requirements of grades and tones, reducing stockpile fluctuations may represent a very important tool especially for medium and short term mine planning. Classical linear programing has been widely used to model blending problems at the mining industry, however this formulation allows only one objective formulation. The current work describes a system based on goal programing able to reach blending constraints desired by short/medium term planning. The proposed formulation achieves the best schedule scenario, ensuring cost constrains are respected. Hence, this study aims to provide support for both short and long term mine planning.Keywords: stockpile; goal programing; blending constraints; stockpile scheduling; bulk ore blending. Programación directa de pilas de acopio: Formulación matemáticaResumen Según el contexto de industria minera la programación de producción es la mejor metodología para determinar la mejor secuencia de explotación y asi obtener el mejor valor presente líquido y explotar la reserva máxima. La construcción de pilas de acopio y de mezcla representa una alternativa valiosa para la planificación de minado permitiendo garantizar la calidad del concentrado y las especificaciones de la planta de tratamiento. Para satisfacer las especificaciones de leyes y tonelaje, y reducir las variaciones las pilas de acopio y mezcla cumplen un papel importante para la planificación de corto y largo plazo. La programación linear clásica viene siendo ampliamente utilizada en problemas de mezcla presentes en la industria minera entretanto esta formulación permite apenas trabajar con una sola función objetivo en su formulación. El presente trabajo describe un sistema basado en goal programing, capaz de alcanzar las restricciones requeridas en la planificación a corto y largo plazo de forma simultánea. La formulación propuesta obtiene el mejor escenario operacional garantizando que las restricciones de costos sean respetadas. Esta formulación es útil pues da soporte a la toma de decisiones en las actividades de planificación a largo y corto plazo Palabras clave: pilas de acopio; goal programing; restricciones de mezcla; agendamiento de pilas; mezcla de concetrado.

show abstract

Section: Goal Programingmentioning

confidence: 99%

Direct stockpile scheduling: Mathematical formulation

Souza¹,

Chaves²,

Burgarelli³

et al. 2018

DYNA

View full text Add to dashboard Cite

show abstract

“…Sherman (1981Sherman ( , 1982 illustrated that econometric regression techniques are less powerful than DEA in identifying efficient production relationships. The data generated by Sherman were later used for regression analysis (RA) and DEA by Bowlin, Chames, Cooper and Sherman (1985) and Giokas (1997). Specifically, Bowlin et al (1985) extended the study of Sherman (1984) by comparing DEA, RA and ratio analysis.…”

Section: Introductionmentioning

confidence: 99%

“…They noted that the three techniques are not mutually exclusive; it is not necessary to select solely one option for a given case, and a combination of the two approaches can be used. Giokas (1997) utilized DEA to generate efficient costs and then carried out RA to determine new weights (marginal costs) for the independent (output) variables. Giokas showed that the marginal costs of the outputs derived from the combination of DEA at the first stage and either goal-programming or regression at the second appeared to be more accurate than those based on DEA or RA alone.…”

Section: Introductionmentioning

confidence: 99%

“…Giokas showed that the marginal costs of the outputs derived from the combination of DEA at the first stage and either goal-programming or regression at the second appeared to be more accurate than those based on DEA or RA alone. Talluri (2002) used DEA to further extend Giokas's (1997) model by allowing managerial preference for various suppliers to be incorporated into the evaluation process. Talluri's study presented a set of multicriteria models for estimating suppliers' efficient marginal costs.…”

Section: Introductionmentioning

confidence: 99%

“…Similarly to Giokas (1997), we also employ a combination of two models (stages) to determine a supplier's marginal cost. As mentioned above, our approach is based on the fact that e-RA bids reveal some information about the suppliers' marginal costs (Zhang and Jin 2007), which may enhance the knowledge base of marginal costs beyond that introduced in previous research.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Enhancing Strategic Supply Decisions by Estimating Suppliers' Marginal Costs

Eckhaus

Kogan

Perlman‬‏

2013

J Supply Chain Manag

View full text Add to dashboard Cite

The ability to estimate a supplier's marginal cost confers a strategic competitive advantage to the buyer, whether in negotiations with suppliers, in an auction setting, or when an auction is used to initiate the process, which is then followed up with a traditional negotiation. Focusing on electronic reverse auctions characterized by a one‐shot, first‐price, sealed‐bid format, this article proposes an approach for estimating a supplier's marginal cost. Specifically, we suggest a two‐stage model: In the first stage, empirical analysis is used to predict the winning bid. In the second stage, a game‐theory approach is used to refine the outcome of the first stage to provide an estimate of the supplier's marginal cost. To assess the model, we apply it to data from a food and beverages company that carried out electronic auctions to select suppliers for industrial maintenance services. We find that our estimates are very close to those made by the suppliers and compare favorably to the efficient marginal costs determined with the widely used approach of data envelopment analysis. This also implies that after selecting a supplier through an auction, the buyer can enhance follow‐up negotiations with the supplier by contrasting our model's estimates of the marginal costs with the supplier's' inefficiencies detected with the data envelopment analysis.

show abstract

The use of goal programming, regression analysis and data envelopment analysis for estimating efficient marginal costs of hospital services

Giokas

2002

Multi Criteria Decision Anal

View full text Add to dashboard Cite

This paper presents a new composite methodology for estimating efficient marginal costs of outputs. The methodology is based on multi-criteria methods involving Data Envelopment Analysis (DEA), Goal Programming (GP) and Regression Analysis (RA) techniques. Firstly, DEA is used to find an empirical frontier onto which each observed dependent variable (cost) is projected. Then GP or RA is applied to the projected data set to yield the parameter estimates of a cost function. That is, firstly, the amounts of technical inefficiency are removed and then the cost function is estimated. The parameters of the cost function provide the efficient marginal costs of outputs. The filtering of the data can be made by means of DEA as it yields target values with frontier rather than average behaviour. The results clearly indicate the superiority of the combination of DEA and GP over the combination of DEA and RA. The methodology was applied to estimate the efficient marginal costs of hospital services (inpatient days, outpatient visits, ancillary services) of public general hospitals in Greece. Concluding, the analysis proves that the estimation of efficient marginal costs of outputs is in author's view particularly useful for hospitals, and can serve as a theoretical basis for determining the prices of different hospital services.

show abstract

The use of goal programming and data envelopment analysis for estimating efficient marginal costs of outputs

Cited by 17 publications

References 8 publications

Direct stockpile scheduling: Mathematical formulation

Direct stockpile scheduling: Mathematical formulation

Enhancing Strategic Supply Decisions by Estimating Suppliers' Marginal Costs

The use of goal programming, regression analysis and data envelopment analysis for estimating efficient marginal costs of hospital services

Contact Info

Product

Resources

About