Indian Journal of Research and Development Systems in Technologization (IJRDST)

ISSN: 2584-2579 (Online)

OPTIMIZATION TECHNIQUES FOR DECISION SUPPORT SYSTEMS

Author(s)

Sivakumar, R.D.* and Brindha, S.** 

*  Assistant Professor, Department of Computer Science, Bell Institute, Sivakasi West.  

** Former Assistant Professor, Department of Business Administration, Sivakasi 

Abstract

Decision Support Systems (DSS) are applied in these different areas like business, healthcare and logistics, they are critical tools that help users to get the best decision makings possible when using them. Optimization methods are paramount constitute the basis of the DSS for provisioning relevant and useful decision-making while maximizing accuracy. This paper starts by elucidating diverse optimization inner workings of DSSs, followed by data management and visualization, describing mathematical programming, heuristic approaches, and metaheuristic fundamentals. Mathematical programming consisting of linear programming, integer programming, and nonlinear programming is providing structured methods of solving information realising problems by optimizing the objective function under many constraints. Such pragmatic strategies as the heuristic approaches (greedy algorithms and local search) represent smart solutions to big problems for which the classical methods may be impossible because of computational infeasibility. These methodologies put their emphasis on expediency and simplicity and most of them are capable of generating solutions of almost optimal quality within the specified period. These include metaheuristic choices like genetic algorithms, simulated annealing, and particle swarm optimization, which integrate strategies intended to help get out of local optima and better the search space. These decision-support techniques play an important role in DSS systems. Through this holistic approach of the system to dynamic and uncertain environments, real-time decision-making and adaptability are facilitated. Additionally this paper explores how multi-objective optimization is applied to DSS as a way of resolving situations where conflicting objectives compete. Advanced methods like multi-technique optimization that simultaneously apply relevant techniques and algorithm learning to improve the resolution process are also discussed. Use of these optimization methods for DSS result in amazing advancement in the field of resource allocation, scheduling, and strategic planning. This paper ascertains the importance of optimization in DSS through the presentation of a holistic review of the current state of DSS. The significance of the continuous research and development of DSS is further highlighted as the decision-makers’ needs change. The study finally talks about future prospects and likely breakthroughs and stresses the necessity for scalable, robust, and flexible optimizations for decisions support systems.

Keywords : Decision Support Systems, Optimization Techniques, Mathematical Programming, Heuristic Methods and Hybrid Optimization.

Volume : 1

Issue : 3

Pages : 30 - 40

Date of Publication : May 2024

DOI

Published By

IJRDST

Published In

Indian Journal of Research and Development Systems in Technologization

(IJRDST)