Efficient location by using data envelopment analysis

Modeling data envelopment analysis (DEA) efficient location/allocation decisions

Many types of facility location/allocation models have been developed to find optimal spatial patterns with respect to location criteria such as: cost, time, coverage, and access. In this paper we develop and test location modeling formulations that utilize aspects of the data envelopment analysis (DEA) efficiency measure to find optimal and efficient facility location/allocation patterns. Solv...

Combining data envelopment analysis and multi-objective model for the efficient facility location–allocation decision

This paper proposes an innovative procedure of finding efficient facility location–allocation (FLA) schemes, integrating data envelopment analysis (DEA) and a multi-objective programming (MOP) model methodology. FLA decisions provide a basic foundation for designing efficient supply chain network in many practical applications. The procedure proposed in this paper would be applied to the FLA pr...

Data Envelopment Analysis, Genetic Algorithm, Hyperplane, Efficient Unit

Data Envelopment Analysis (DEA) is actually to obtain the efficiency using inputs and outputs, which can determine efficient and inefficient units with the help of performance calculations such that the efficiency for efficient DMUs is one and less than one for inefficient DMUs [1]. In some cases, the ranking of the decision-making units are not important for decision-makers, and they are only ...

Efficient DMUs improvement based on input expenses reduction using data envelopment analysis

Network nowadays, the main purpose in the models designed by Data Envelopment Analysis (DEA), is to improve the outputs. In this method which is expressed by Khodabakhshi, with an output oriented BCC model, the output increases when the input increases. In this article we will discuss the efficient Decision Making Units (DMUs) in the input oriented BCC model to reduce the input expenses signifi...

Multi-objective Efficient Design of np Control Chart Using Data Envelopment Analysis

Control charts are the most important tools of statistical process control used to discriminate between assignable and common causes of variation and to improve the quality of a process. To design a control chart, three parameters including sample size, sampling interval, and control limits should be determined. The objectives are hourly expected cost, in-control average run length, power of th...

Failure Mode and Effects Analysis Using Data Envelopment Analysis