ABSTRACT
The weighted sum and distance minimization methods are two approaches lie within the group of Scalarization Methods as they reframe the Multi-Objective Linear Model into a single objective problem. A weighted sum is very often the first approach that comes to mind when dealing with several perspectives in a decision-making process. This method asks the Decision Maker (DM) to assigned weights to each objective, reflecting its importance to the overall problem. This assignment may be hard task to the DM and inadequate weights might bias the result, leading to decisions that do not reflect the DM’s preferences. Other issues need to be handled with care since they may undo the hard work to come up with the perfect weights (e.g. objective function scaling, normalization). The distance minimization approach seeks to find the compromise solution that is closer to some desirable reference point. For this method, the question is how does one measure “close to”? This chapter addresses these two methods highlighting the issues influencing the computing of the compromise solution. For each method, several examples are solved. A set of exercises covering all topics are proposed at the end of the chapter.
