Multi-objective programming (MOP) can simultaneously optimize multi-objectives in mathematical programming models, but the optimization of multi-objectives triggers the issue of Pareto solutions and complicates the derived answers. To address these problems, researchers often incorporate the concepts of fuzzy sets and evolutionary algorithms into M
Introduction. CONCEPTS AND THEORY OF MULTI-OBJECTIVE DECISION MAKING: Multi-Objective Evolutionary Algorithms. Goal Programming. Compromise Solution and TOPSIS. De Novo Programming and Changeable Parameters. Multi-Stage Programming. Multi-Level Multi-Objective Programming. Data Envelopment Analysis. APPLICATIONS OF MULTI-OBJECTIVE DECISION MAKING: Motivation and Resource Allocation for Strategic Alliances through the De Novo Perspective. Choosing Best Alliance Partners and Allocating Optimal Alliance Resources Using the Fuzzy Multi-Objective Dummy Programming Model. Multiple-Objective Planning for Supply Chain Production and Distribution Model: Bicycle Manufacturer. Fuzzy Interdependent Multi-Objective Programming. Novel Algorithm for Uncertain Portfolio Selection. Multi-Objective Optimal Planning for Designing Relief Delivery Systems. Comparative Productivity Efficiency for Global Telecoms. Fuzzy Multiple Objective Programming in Interval Piecewise Regression Model. Bibliography. Notes.