An inventory model to study the effect of the probabilistic rate of carbon emission on the profit earned by a supplier

• Autorzy: Bhattacharjee Nabajyoti , Sen Nabendu

Identyfikatory

DOI

10.37190/ord210401

• Języki publikacji: EN

Abstrakty

EN

Sensitivity analysis of parameters is usually more important than the optimal solution when it comes to linear programming. Nevertheless, in the analysis of traditional sensitivities for a coefficient, a range of changes is found to maintain the optimal solution. These changes can be functional constraints in the coefficients, such as good values or technical coefficients, of the objective function. When real-world problems are highly inaccurate due to limited data and limited information, the method of grey systems is used to perform the needed optimisation. Several algorithms for solving grey linear programming have been developed to entertain involved inaccuracies in the model parameters; these methods are complex and require much computational time. In this paper, the sensitivity of a series of grey linear programming problems is analysed by using the definitions and operators of grey numbers. Also, uncertainties in parameters are preserved in the solutions obtained from the sensitivity analysis. To evaluate the efficiency and importance of the developed method, an applied numerical example is solved.

Słowa kluczowe

EN

inventory modelling; carbon emission; green investment; preservation; PSO; GA

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Operations Research and Decisions
• Rocznik: 2021
• Tom: Vol. 31, No. 4
• Strony: 5--33
• Opis fizyczny: Bibliogr. 39 poz., rys.

Twórcy

autor

Bhattacharjee Nabajyoti

• Department of Mathematics, Assam University, Silchar Dorgakona, Silchar – 788011, Cachar Assam, India

autor

Sen Nabendu

• Department of Mathematics, Assam University, Silchar Dorgakona, Silchar – 788011, Cachar Assam, India

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).