Goal Programming Approaches for Multi-Criteria Decision Making

Goal programming is a specialised optimisation technique designed to address decision problems involving multiple objectives that often conflict under limited resources. Building on the linear programming paradigm, it introduces deviational variables to quantify under-achievement and over-achievement against predefined targets. Decision-makers assign priorities or weights to these goals, configuring the model to either respect a hierarchical ordering or balance objectives through weighted sums. Over recent decades, this methodology has evolved to incorporate fuzzy sets, stochastic parameters and behaviour-driven utility functions, enabling practitioners to capture uncertainty and risk attitudes within the decision process. Applications span public policy, sustainable development planning, supply chain deployment, energy strategy and financial portfolio design. Advances in computational algorithms—including heuristic search, decomposition methods and machine-learning-assisted weight elicitation—have extended goal programming to large-scale real-world scenarios. By framing trade-offs in an explicit, transparent manner, these approaches furnish stakeholders with actionable insights into the compromises required to satisfy diverse criteria simultaneously, thereby underpinning evidence-based governance and strategic planning on a global scale.

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Goal Programming Approaches for Multi-Criteria Decision Making publication trend

The graph below shows the total number of articles in goal programming approaches for multi-criteria decision making across all publications each year (not limited to Nature Index journals).

Technical terms

Goal Programming: An optimisation method that minimises deviations from multiple predefined targets simultaneously.

Deviational Variable: A variable representing the shortfall or excess relative to a specific goal in the model.

Weighted Goal Programming: A variant in which each goal’s deviation is multiplied by a weight reflecting its importance.

Meta-Goal: A higher-level objective that governs or aggregates subordinate goals within a nested goal programming structure.

Fuzzy Sets: Mathematical constructs that model vagueness by assigning degrees of membership, used to handle uncertainty in goals or weights.

Utility Function: A representation of decision-maker preferences, sometimes behaviourally adjusted to incorporate risk aversion into goal programming.

References

1. Incorporation of poverty principles into goal programming. Omega (2024).
2. Multi-criteria mapping and prioritization of Arctic and North Atlantic maritime safety and security needs. European Journal of Operational Research (2023).
3. A generalized behavioral-based goal programming approach for decision-making under imprecision. Operations Research Perspectives (2024).