Compromise programming: A utility-based linear-quadratic composite metric from the trade-off between achievement and balanced (non-corner) solutions

摘要: Abstract In this paper, compromise programming (CP) is viewed as the maximization of decision maker’s additive utility function (whose arguments are criteria under consideration) subject to an efficient frontier and non-negativity constraints in a deterministic context. This equivalent minimizing difference (‘distance’) between at ideal point on map, meaningful statement distances utopia ethos programming. By Taylor expansion around point, distance becomes weighted sum linear quadratic CP distances, which gives us composite metric. While terms pursue achievement, ones balanced (non-corner) solutions. Because some makers fear imbalance while others prefer large achievements even detriment balance, paper defines aversion ratio, so that linear-quadratic metric should conform ratio depending preferences attitudes. As problem selecting appropriate ongoing issue CP, contribution theory practice. For sole purpose suggesting industrial applications, example developed.