An interactive branch-and-bound algorithm for multiple criteria optimization
Management Science
Decision theory: an introduction to the mathematics of rationality
Decision theory: an introduction to the mathematics of rationality
Strategic Decision Making
Solving bicriteria 0-1 knapsack problems using a labeling algorithm
Computers and Operations Research
Dynamic Portfolio Selection of NPD Programs Using Marginal Returns
Management Science
Interactive Multiobjective Optimization Using a Set of Additive Value Functions
Multiobjective Optimization
Building Efficient Product Portfolios at John Deere and Company
Operations Research
Binary Accelerated Particle Swarm Algorithm (BAPSA) for discrete optimization problems
Journal of Global Optimization
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In this paper we present a new framework for identifying preferred solutions to multi-objective binary optimisation problems. We develop the necessary theory which leads to new formulations that integrate the decision space with the space of criterion weights. The advantage of this is that it allows for incorporating preferences directly within a unique binary optimisation problem which identifies efficient solutions and associated weights simultaneously. We discuss how preferences can be incorporated within the formulations and also describe how to accommodate the selection of weights when the identification of a unique solution is required. Our results can be used for designing interactive procedures for the solution of multi-objective binary optimisation problems. We describe one such procedure for the multi-objective multi-dimensional binary knapsack formulation of the portfolio selection problem.