Self-Organizing Maps Infusion with Data Envelopment Analysis

Abstract

This chapter presents work on the use of an artificial intelligence technique to cluster stratified samples of container terminals derived from Data Envelopment Analysis (DEA). This technique is Kohonen’s self-organizing map (SOM; (Kohonen, 1995)). Data envelopment analysis measures the relative efficiency of comparable entities called Decision Making Units (DMUs) essentially performing the same task using similar multiple inputs to produce similar multiple outputs ((Charnes et al., 1978)). The purpose of DEA is to empirically estimate the so-called efficient frontier based on the set of available DMUs. DEA provides the user with information about the efficient and inefficient units, as well as the efficiency scores and reference sets for inefficient units. The results of the DEA analysis, especially the efficiency scores, are used in practical applications as performance indicators. There are many problems associated with applying the DEA in some applications. One problem is that the improvement projection for inefficient units in DEA analysis is concrete relative to its efficiency score. This means, in DEA, relative performance of any DMU can be contrasted only to the efficient DMUs that register unit efficiency score. There is no influence on the performance of efficient DMUs by presence or absence of inefficient DMUs. Therefore, the classical DEA does not actually provide a direct means to rank DMUs based on their relative degrees of efficiency or inefficiency ((Sharma & Yu, 2010)). The second problem is that the DEA models assume that all DMUs are homogenous and identical in their operations ((Seiford, 1994)). Since various applications have heterogeneous DMUs and there is a high request to evaluate these applications under the DEA due to its acceptance as a performance measurement in different kind of business, we have to modify the DEA to work with these applications. If the heterogeneous DMUs are assessed by DEA without any modifications, the DEA yields a biased performance scores and inaccurate analyses. For example, the resources (land, equipment, and labor) of container terminals varies across the world, which requires to be evaluated in term of its common input characterstics. An essential requirement in analyzing these container terminals is to build a fair referencing system for each container terminal to manage and provide a solid plan that improves all inefficient terminals and supports all efficient terminals. This system can not be assessed under the standard DEA due to the non-homogenous nature of these container terminals in terms of their operations, different standards of equipments, infrastructure, and variety in quay length and area size. These factors will yield unfair benchmarking evaluation 5