Decision Support Systems (DSS)

Introduction

A Decision Support System (DSS) is an interactive computer-based system or subsystem intended to help decision makers use communications technologies, data, documents, knowledge and/or models to identify and solve problems, complete decision process tasks, and make decisions. Decision Support Systems (DSSs)are information systems used in decision making and problem solving, enhancing a person or group’s ability to make decisions. Also, Decision Support Systems refer to an academic field of research that involves designing and studying DSSs in their context of use.

• Help end users to make decisions in a given context: the final outcome is under the control of the user and thus the aim is not to replace humans in the decision making process
• Such systems are very specific to a given situation and may be “user-friendly” only in the case of a specific set of experts
• The underlying algorithms of the system may find many solutions which should be filtered and ranked (for example using fuzzy number theory) to generate importances of solutions for operators
• These results should be displayed to the user in an effective and informative interface to aid in the final decision
• Such systems are designed to reduce the manual operational load, for example reducing the number of operators required for a swarm for drones or indeed a constellation of satellites
• The underlying algorithms may use AI or machine learning techniques, as well as constraint programming (CSP) or evolutionary computation algorithms (e.g. genetic algorithms) to solve multi objective optimisation problems

Characteristics & Components

Common DSS characteristics:

• Intended to support decision makers rather than replace them
• Supports all phases of the decision-making process
• Focuses on effectiveness of the process rather than efficiency
• Is under control of the DSS user
• Uses underlying data and models
• Facilitates learning on the part of the decision maker
• Is interactive and user-friendly
• Is generally developed using an evolutionary, iterative process
• Can support multiple independent or interdependent decisions
• Supports individual, group or team-based decision-making

In DSSs based on optimization, the decision making process is divided into these three stages:

1. Problem formulation, which will generate a model in an appropriate language for the resolver;
2. Then model resolution using several algorithms, which have been traditionally Operational Research (OR), although nowadays any kind of optimization algorithms are used;
3. And finally the analysis and interpretation of the solution, or set of solutions, for the model.

What a DSS Can and Cannot Do:

• The DSS is expected to extend the decision maker’s capacity to process information.
• The DSS solves the time-consuming portions of a problem, saving time for the user.
• Using the DSS can provide the user with alternatives that might go unnoticed.
• It is constrained, however, by the knowledge supplied to it.
• A DSS also has limited reasoning processes.
• Finally, an “universal DSS” does not exist.

Basic components of a DSS:

1. The data management system
2. The model management system
3. The knowledge engine
4. The user interface
5. The users

DSS examples & application domains:

• Clinical Decision Support System
• Traffic (road) operations
• Air Traffic Management
• Marketing Decision Models
• Decision Support Systems in Architecture and Urban Planning
• Decision-Making in Engineering Design
• Unmanned Aerial Systems (UASs)
• Space!
• etc…

How can I build my own DSS?

Some frameworks can be used for specific tasks (e.g. visualisation, data management, machine learning, data mining, optimization, etc.):

• KDNuggets
• Shiny
• Python: NumPy, Scikit-Learn, SciPy, Dask, JMetalP, PyTorch, etc…
• R: CRAN, R Studio
• Keras

Bibliography

Clemen, R. T., & Reilly, T. (2013). Making hard decisions with Decision Tools. Cengage Learning.

Densham, P. J. (1991). Spatial decision support systems. Geographical information systems. Vol. 1: principles, 403-412.

Marakas, G. M. (2003). Decision support systems in the 21st century (Vol. 134). Upper Saddle River, NJ: Prentice Hall.

O’brien, J. A., & Marakas, G. M. (2005). Introduction to information systems (Vol. 13). New York City, USA: McGraw-Hill/Irwin.

Ramirez-Atencia, C., & Camacho, D. (2018). Extending QGroundControl for Automated Mission Planning of UAVs. Sensors, 18(7), 2339.

Ramirez-Atencia, C., Bello-Orgaz, G., R-Moreno, M. D., & Camacho, D. (2017). Solving complex multi-UAV mission planning problems using multi-objective genetic algorithms. Soft Computing, 21(17), 4883-4900.

Turban, E. (1993). Decision support and expert systems: management support systems. Prentice Hall PTR.