Estimation of Marginal Productivity of Supply Chains for Capacity Planning and Resource Allocation: A Case Study of the Power Industry
محورهای موضوعی : مجله بین المللی ریاضیات صنعتی
1 - Department of Applied Mathematics
Islamic Azad University, Lahijan Branch, Lahijan,Iran
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کلید واژه: Climate Change, Demand Fluctuation, Directional Marginal Productivity, Marginal Profit Maximization, Capacity Planning,
چکیده مقاله :
Marginal productivity (MP) estimation is utilized to plan maximum output levels and allocate resources to address fluctuating demand for supply fuel in the power plant sector as well as adjust transferring and dispatching in the transmission and distribution networks. In this paper, a data envelopment analysis (DEA) model is introduced for estimating the directional marginal productivity of supply chain divisions. The proposed model for estimating the directional marginal productivity in the supply chain tries to find the optimal direction of efficient divisions on the frontier so that marginal profit is maximized. This model measures efficiency by maximizing marginal profit for multiple outputs in predetermined directions based on multiple inputs. The purpose of this study is to develop acceptable techniques for responding to demand fluctuations, especially in the energy and power plant sectors. This is when confronted with efficiency losses from climate change and critical conditions. The results suggested that the oil field division of one of the supply chains had fundamental capacities to respond to peak demand. Furthermore, the power plant division of this supply chain also had a considerable structure for the marginal profit maximization of outputs. Additionally, there were transmitters and distribution lines that obtained marginal profit maximization by adding one extra unit to the line's length in the determined direction.
Marginal productivity (MP) estimation is utilized to plan maximum output levels and allocate resources to address fluctuating demand for supply fuel in the power plant sector as well as adjust transferring and dispatching in the transmission and distribution networks. In this paper, a data envelopment analysis (DEA) model is introduced for estimating the directional marginal productivity of supply chain divisions. The proposed model for estimating the directional marginal productivity in the supply chain tries to find the optimal direction of efficient divisions on the frontier so that marginal profit is maximized. This model measures efficiency by maximizing marginal profit for multiple outputs in predetermined directions based on multiple inputs. The purpose of this study is to develop acceptable techniques for responding to demand fluctuations, especially in the energy and power plant sectors. This is when confronted with efficiency losses from climate change and critical conditions. The results suggested that the oil field division of one of the supply chains had fundamental capacities to respond to peak demand. Furthermore, the power plant division of this supply chain also had a considerable structure for the marginal profit maximization of outputs. Additionally, there were transmitters and distribution lines that obtained marginal profit maximization by adding one extra unit to the line's length in the determined direction.
