An interval programming approach for developing economic order quantity model with imprecise exponents and coefficients

Omrani, Hashem; Keshavarz, Mehdi

doi:10.1016/j.apm.2013.11.060

Cited by 8 publications

(2 citation statements)

References 29 publications

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“…The company's objective is to determine optimal pricing, lot-sizing and marketing strategies based on the profit maximizing inventory models presented by Liu (2008Liu ( , 2011, Omrani and Keshavarz (2014) and Sadjadi et al (2015). Specifically, we consider three decision variables: selling price in dollars (p), order quantity in units (q), and marketing expenditure per unit (m).…”

Section: Numerical Examplesmentioning

confidence: 99%

Integrating geometric programming with rough set theory

Shiraz

Fukuyama

2016

Oper Res Int J

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Geometric programming has been applied in the problems of engineering design, economics and management science. The conventional deterministic geometric programming method requires precise single values for the coefficients and exponents of decision variables. However, there may exist uncertainty and impreciseness about the parameters as well as data in complex real-life problems. In such situations, the deterministic geometric programming method is inappropriate. In this paper, we integrate the deterministic geometric programming with rough set theory to propose a rough geometric programming method. Our proposed method has mainly three characteristics. Firstly, the coefficients in the objective function and constraints are rough variables. Secondly, the expected-value operator of rough variables is implemented. Thirdly, the method can determine both lower and upper bounds of the objective function at a specific trust level.Three illustrative examples are presented to demonstrate the efficacy of our novel method.

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Section: Numerical Examplesmentioning

confidence: 99%

Integrating geometric programming with rough set theory

Shiraz

Fukuyama

2016

Oper Res Int J

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“…Ghosh and Roy (2013) applied a GP technique to solve a nonlinear goal programming problem. Omrani and Keshavarz (2014) proposed an uncertain EOQ model with interval exponents and coefficients. They maximized the profit and found the lower and upper bounds for the objective function and decision variables.…”

Section: Introductionmentioning

confidence: 99%

Global optimisation for a developed price discrimination model: A signomial geometric programming-based approach

Moradi

Omrani

Emrouznejad

2020

Journal of the Operational Research Society

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This paper presents a price discrimination model for a manufacturer who acts in two different markets. In order to have a fair price discrimination model and compare monopoly and competitive markets, it is assumed that there is no competitor in the first market (monopoly market) and there is a strong competitor in the other market (competitive market). The manufacturer objective is to maximize the total benefit in both markets. The decision variables are selling price, lot size, marketing expenditure, customer service cost, flexibility and reliability of production process, set up costs and quality of products. The proposed model in this paper is a signomial geometric programming problem which is difficult to solve and find the globally optimal solution. So, this signomial model is converted to a posynomial geometric type and using an iterative method, the globally optimal solution is found. To illustrate the capability of the proposed model, a numerical example is solved and the sensitivity analysis is implemented under different conditions.

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