Flight scheduling and maintenance base planning
Management Science
Solving real-life vehicle routing problems efficiently using tabu search
Annals of Operations Research - Special issue on Tabu search
Computational Optimization and Applications
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
The strategic landscape investment model: a tool for mapping optimal environmental expenditure
Environmental Modelling & Software
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A PROMETHEE-based approach to portfolio selection problems
Computers and Operations Research
A two state reduction based dynamic programming algorithm for the bi-objective 0-1 knapsack problem
Computers & Mathematics with Applications
GRASP strategies for a bi-objective commercial territory design problem
Journal of Heuristics
Hi-index | 0.01 |
Investment in landscapes to achieve outcomes that have multiple environmental benefits has become a major priority in many countries. This gives rise to opportunities for mathematical programming methods to provide solutions on where investments could be made on the landscape, to maximise multiple environmental benefits. The problem was formulated as a multi-objective integer programming model, with objective functions representing biodiversity, water run-off and carbon sequestration. We applied a multi-objective Greedy Randomised Adaptive Search Procedure (GRASP) as an evolutionary programming method to find solutions along the Pareto front. This allows the decision maker to explore trade-off's between the objectives. A 142,000ha case study catchment in eastern Australia was used to test the methodology and assess the sensitivity of the different and often competing environmental benefits.