Data Envelopment Analysis, DEA
Reza Fallahnejad; Sanaz Asadi Rahmati; Kayvan Moradipour
Abstract
To evaluate the performance and estimate the efficiency of Decision-Making Units (DMUs) in Data Envelopment Analysis (DEA), the available data are used. These data are usually divided into two categories of inputs and outputs based on their natures. If the price data is also available for inputs, it ...
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To evaluate the performance and estimate the efficiency of Decision-Making Units (DMUs) in Data Envelopment Analysis (DEA), the available data are used. These data are usually divided into two categories of inputs and outputs based on their natures. If the price data is also available for inputs, it is necessary to calculate another type of the efficiency called Cost Efficiency (CE). Since the efficiency of units in such a framework is depended on the both quantities of inputs and outputs and also the prices of inputs, it is important to find the sources of cost inefficiency related to each of the factors and plans to address them. In this paper, we intend to present a new decomposition of CE and observed cost versus optimal cost, which are raised from each of the factors involved in the cost inefficiency, in a non-competitive pricing environment which the input price vector for different DMUs can be different. Moreover, for the first time, in parallel with using the PPS based on input and output quantities and introducing some cost inefficiency factors related to this set, we will introduce new sets called price and cost production sets that the first is based on the prices of inputs and output factors, and the second is based on the optimal vectors of inputs and prices obtained from two previous PPS, and then we will introduce other factors of cost inefficiency in the sets. Accordingly, new decomposition for cost inefficiencies will be presented. Also, in the previous analyzes, congestion inefficiency has not been considered as one of the important factors in cost inefficiency. In this study, we also intend to consider the impact of this factor on CE.
Transportation
Sarow Saeedi; Omid Poursabzi; Zaniar Ardalan; Sajad Karimi
Abstract
Hub location problems (HLP) have multiple applications in logistic systems, the airways industry, supply chain network design, and telecommunication. In the HLP, the selected nodes as hubs perform the principal role in processing the inflow arising from other nodes. So, congestion would be inevitable ...
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Hub location problems (HLP) have multiple applications in logistic systems, the airways industry, supply chain network design, and telecommunication. In the HLP, the selected nodes as hubs perform the principal role in processing the inflow arising from other nodes. So, congestion would be inevitable at hub nodes. This paper considers a p-hub median problem with multiple hub node servers delivering service at variable rates. Since the service rates are limited and variable, a queue is formed at each hub server. To tackle this problem, we developed a mixed-integer linear programming model that optimizes the selected hub nodes to reduce congestion under an allowable defined queue length at each server and minimize the total costs of the model, including transportation and hub establishment costs. We utilized the Civil Aeronautics Board (CAB) dataset containing 25 USA cities, which is a valuable source for designing numerical examples in the HLP, to prove the model's efficiency. The results obtained from the designed sample problems show that strategic decisions on defining the number of hubs and maximum acceptable queue length at each hub server will significantly impact the hub location network design.