Mathematical Models Generators of Decision Support Systems for Help in Case of Catastrophes: An Experience from Venezuela

Introduction

The contribution of this chapter is centred in two aspects: the mathematical models, especially emphasizing on the models, particularly those of minor publication and the Decision Support System (DSS) and their application for help in case of catastrophes. The constructed DSS commented here briefly, only reflects a part of the situation in Venezuela, and it is intended for them to be a starting point for future reference in other places and societies, with the necessary adapting.

Among the models involved in the development of DSS can be mentioned: Problems of shorter routes, particularly its use of the Dijkstra algorithm; Problems of flows, especially maximum flow and minimal cost flows; Goal programming (GP); Multiattribute Models (MM) with multiplicative factors; Matrixes Of Weighing (MOW); Structures of decision trees; Inventory models; A, B, C Models, or 80/20 or Pareto model; Decreasing digits (Dd); Transportation and Transhipment problems and Assignment model and Fuzzy set. However, only some of them would be commented, especially those that for some reason appear slightly in the literature.

On the other hand there will be brief comments of at least twelve decision support systems, for aiding in cases of catastrophes, as performed by students of the Metropolitan University in Caracas, Venezuela.

Given that with this chapter it is intended to give a greater coverage to the mathematical models, as to the DSS developed by students of the Metropolitan University in Caracas, Venezuela, the objective of this chapter could be enunciated as: Disclose this experience when presenting some of these mathematical models and its conversion in DSS that supports decision making in the case of catastrophes.

This general objective implies two specific objectives: Construct the mathematical models and integrate them to a support system for decision making that could aide in case of catastrophe.

To achieve the general objective and the specific objectives that are generated, the followed methodology takes the scientific method as a base for the Operations Research or for decision making (Hernández & García, 2006; 2007; Hernández, García & Hernández, 2009), which tackles the problems of making decisions without pass for the exposition of hypothesis, but, does it across the following steps:

• a.

  Defining the problem, as indicated in the objectives, present the mathematical models and DSS that can aide in case of catastrophes,

• b.

  Searching for data, in particular mathematical models and support systems for decision making, that can aide in case of catastrophes,

• c.

  Establishing the alternatives, that would be different mathematical models to use in case of catastrophes,

• d.

  Evaluate alternatives, according to the raised objectives, deciding which of the proposed alternatives is feasible,

• e.

  Selecting the best alternative, as product of previous evaluation process, and based on the secondary objectives, tacit or explicit, being considered,

• f.

  Implementing the best alternative, meaning, choosing the better mathematical models and with them constructing the DSS that can aide in case of catastrophe and

• g.

  Establishing controls, or mechanisms that allow recognizing if the developed systems are still valid over time.

The results will be given by the presentation of some models and brief commentaries of the rest, just like it will be done for the DSS, whom will be summarize (Figure 4) and only one of them will be discussed with a little more depth.

Figure 4.

Graduate special works, its models and characteristics of their DSS