Abstract
Previous papers developed a method to easily elicit a decision maker's (DM) preferences and account for changes in the DM's preference structure. Those preferences are modeled by piecewise linear indifference curves with varying slopes producing a piecewise linear-fractional value function. Compared with traditional optimization problems which traditionally use cost minimization or revenue maximization, this model is DM-specific, it generates a knowledge set (KS) and allows the DM to find an optimal solution based on his/her expertise and preferences. When combined with real world constraints, maximizing the DM's preferences generates a decision support system (DSS) for solving specific organizational problems. This paper develops an efficient algorithm to solve a mathematical programming problem with a linear fractional objective function that models changing DM preferences and linear constraints. A DSS is developed and its algorithm is illustrated by constructing a specific example of the DSS for scheduling a police force when the objective is to maximize the police chief's expertise and preferences regarding law enforcement.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
