In the following section, the implementation and utilization of the MCDM tech-niques in application fields is discussed. In general, implementation is interpreted as the realization of an application, or execution of a plan, idea, model, or algo-rithm. In computer sciences, an implementation means a realization of a technical specification or algorithm as a program, software component, or other computer system. The development of computers and computing capacity have made it possible to produce more sophisticated software for solving multiobjective opti-mization problems and to implement many kinds of algorithms and computer softwares for MCDM problems. At the same time, these implementations are be-ing applied to many more varied applications as is done also in this thesis. These kinds of software packages for MCDM and multiobjective optimization problems are termed multiobjectivedecision support systems [4, 95].
2.2.1 Decision support systems
Decision support systems (DSSs) are usually depicted as a specific class of com-puterized information systems that are designed for supporting business and or-ganizational decision making activities [95]. A DSS is a software based system
intended to help decision makers, for example, in compiling useful information from raw data, documents, personal knowledge, and/or models to identify and solve problems and, more importantly, make decisions.
A typical decision support system might gather and present information such as:
• An inventory of all of your current information assets
• Comparative graphs, figures and tables between solutions
• Consequences of different decision alternatives.
This thesis will consider so-called model-based or model-driven decision support systems [114] which are usually based on statistical data, a simulation model, and/or optimization. Model-based DSSs use data and parameters provided by an individual called an analyst to assist decision makers in analyzing a situation and coming to a decision.
By ananalyst we mean an individual (or in some cases a computer program) responsible for the mathematical side of the solution process [95]. He/she is an expert in using this kind of software, and sometimes he/she is responsible also for implementation and programming. In addition to the decision maker who has responsibility for the decision, an analyst is on hand to guide and assist the decision maker in reaching a desired decision. The analyst works in co-operation with the decision maker: he/she generates information for the decision maker to consider, and then the final solution is selected by the decision maker.
It has been demonstrated that DSSs increase the understanding of the problem, they contribute to progress in solution process, and thus, reduce frustration in problem solving [114]. In general, DSSs should be easy to use and they should follow the decision maker’s thinking. Moreover, they should be able to support different decision styles, problem structures, and applications [151].
Software specifically implementing MCDM methodology can be divided into three groups [151]:
• Commercially available software packages
• Software packages developed primarily for research purposes
• Programs written for experimental implementation and testing of new MCDM techniques.
Commercially available softwares can be true application oriented simulators which are designed for supporting decision making in some certain application or problem (e.g. MIRA [145] which is however an open source program). Simultaneously, some of those software packages are very generic systems which can be implemented to solve almost any problem which have been modeled in a reliable enough manner (e.g. modeFRONTIER [36] or NIMBUS [97, 99]). Most of the implementations are designed for research and testing purposes.
A list of some software products designed for supporting MCDM can be found onwww.mcdmsociety.org/soft.html which is the webpage of the International So-ciety on MCDM. Many kind of macros can be used in supporting MCDM. Thus, the above list does not claim to be complete.
2.2.2 Supporting MCDM in real world problems
In order to support MCDM in solving of a real world problem, the following steps are considered in a model-based DSS in this thesis:
• Simulation model
• Optimization tool (or optimizer, solver)
• Interface between the model, the optimizer, and the user (decision maker or in some case analyst).
Here, the interface refers to input, output and exchange of information, and pre-sentation of results for the decision maker. The cooperation of simulation model, optimization tool, interface and user is clarified also in Figure 2.2.
Figure 2.2: An illustration of model-based decision support system.
Simulation model
The role of simulation (and optimization) model building was already discussed in Section 2.1.2. A simulation model forms the cornerstone of model-driven DSSs, because it is the way the system acquires information and data about the system considered. Thus, if the simulation model is unreliable, then the information given by the DSS is unreliable. Optimization models to be solved are built up by using simulation models.
Optimization tool
Model-based DSSs are usually constructed in such a way that many optimization tools or optimizers can be used to solve the optimization model or problem in
question. The selection of the optimizer depends mostly on the problem struc-ture: problems can be continuous or integer-valued, linear or nonlinear, convex or non-convex, differentiable or non-differentiable, or problem can be single or multiobjective optimization problem. There are different methods suitable for dif-ferent kinds of problems, and choosing the appropriate optimizer case-specifically is important for maintaining the efficiency of the DSS.
Interface
It is important to highlight the role of the interface. In many cases, a great effort has been spent in developing the methodological and computational aspects of the system (i.e. modeling work and developing the optimizer), but the interface between the model, the optimizer, and the user is not so well clarified [4]. This can cause problems, since no matter how exact is the model used or how efficient is the optimizer it will fail if the interface between those features and the user does not work as expected. Graphical user interfaces with illustrative visualizations, graphs and figures play an essential role in DSSs nowadays [62, 95].
In a nutshell, examples of the requirements for computer implementation of a DSS can be listed as follows:
• Simulation model must be validated
• Flexible to test and analyze different model parameters and set-ups
• Possibility to add models to the system
• Proper optimizer and possibility to modify optimization problem solved
• Possibility to add optimizers to the system
• Appropriate interface
• Fast enough results.