Dynamic Programming: Approach to Efficient Decision-Making
DYNAMIC PROGRAMMING APPROACH TO EFFICIENT DECISION MAKING
Shelly Sachdevaa
Senior Lecturer, CS / IT Dept. JIITU, Noida. shelly.sachdeva@jiit.ac.in Phone No.:09810490704 a b
Gauravb,
Student, CS / IT Dept JIITU, Noida onmail.gaurav@gmail.com Phone No.: 09871277845
Prashant Malthotrac
Student, CS / IT Dept JIITU, Noida first.prashant@gmail.com Phone No:09911867266 c ABSTRACT Decision support systems combine individuals ' and computers ' capabilities to improve the quality of decisions. We have tried to add value to the decision making software by enabling them provide efficient solution through incorporation of Dynamic Programming Techniques. We have integrated various decision support tools e.g. Data warehousing, Data mining, and Dynamic Programming approach to arrive at an actionable solution.
2. Review Present Decision support systems are a cohesive, integrated system of hardware and software designed specifically to manipulate data and enable users to support problem solving and decision making by drawing useful information from disparate sources of data with the help of mining techniques. While data mining can assist in the automated discovery of actionable [5] insights from data, the efficient execution of the actions can only be effected by coupling the output of data mining with mathematical optimization methods. Very often the actionable insights need to be acted upon taking into account business constraints such as budgets and schedules. In this paper, effort is to take the decision support systems up one level. We have tried to discover unknown information by predictive analysis of data. Based on the predicted results, with the help of Dynamic Programming techniques we attempt to find out efficient solution scenario which makes decision making both Dynamic and much more effective. One major role that technology plays is supporting analysis. Yet another technology capability, which is in increasing demand, is to recommend specific actions
Shelly Sachdevaa
Senior Lecturer, CS / IT Dept. JIITU, Noida. shelly.sachdeva@jiit.ac.in Phone No.:09810490704 a b
Gauravb,
Student, CS / IT Dept JIITU, Noida onmail.gaurav@gmail.com Phone No.: 09871277845
Prashant Malthotrac
Student, CS / IT Dept JIITU, Noida first.prashant@gmail.com Phone No:09911867266 c ABSTRACT Decision support systems combine individuals ' and computers ' capabilities to improve the quality of decisions. We have tried to add value to the decision making software by enabling them provide efficient solution through incorporation of Dynamic Programming Techniques. We have integrated various decision support tools e.g. Data warehousing, Data mining, and Dynamic Programming approach to arrive at an actionable solution.
2. Review Present Decision support systems are a cohesive, integrated system of hardware and software designed specifically to manipulate data and enable users to support problem solving and decision making by drawing useful information from disparate sources of data with the help of mining techniques. While data mining can assist in the automated discovery of actionable [5] insights from data, the efficient execution of the actions can only be effected by coupling the output of data mining with mathematical optimization methods. Very often the actionable insights need to be acted upon taking into account business constraints such as budgets and schedules. In this paper, effort is to take the decision support systems up one level. We have tried to discover unknown information by predictive analysis of data. Based on the predicted results, with the help of Dynamic Programming techniques we attempt to find out efficient solution scenario which makes decision making both Dynamic and much more effective. One major role that technology plays is supporting analysis. Yet another technology capability, which is in increasing demand, is to recommend specific actions
References: [13] Dictionary of algorithms and Data Structures, “http://www.nist.gov/dads/”. [14] The Knapsack Problem, “http://webspace.ship.edu/thbrig/index.html”. 5 6