Solving linear design problems using a linear-fractional value function
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.
decision analysis multiple criteria analysis linear-fractional preference structure piecewise linear-fractional model