Modelling De novo programming within Simon’s satisficing theory: Methods and application in designing an optimal offshore wind farm location system

Abstract

De novo programming (DNP) is an efficient technique for optimal system design. This paper explores the ability to link the DNP technique with Simon's satisficing theory to deal with a system design that is satisfactory rather than optimal. To achieve this aim, the ideal vector is replaced by an aspiration -level vector, and the solutions are determined by minimising the Lp-distance metric between the aspiration level and the feasible objective region. To generate a satisficing solution, we develop two models (weighted DNP (W-DNP) and Chebyshev DNP (C-DNP)) based on goal programming techniques. To achieve equilibrium between the solutions obtained from W-DNP and C-DNP, an extended DNP (E-DNP) model is proposed. Moreover, to deal with uncertainty and give decision makers more flexibility to incorporate their preferences, we consider the concept of penalty function (PF) with DNP and propose DNP type models with penalty functions (DNP-PFs). An illustrative example is adopted to show the usefulness of the proposed approach over the standard DNP. We also conduct a hypothetical application to Italian offshore wind farm locations to assess and validate the proposed formulations for solving real -world problems. To check the stability of the obtained results, the impact of the weights on the obtained solution is detected with a weight-space analysis. The results confirm the proposed methodologies and show that they can assist decision makers in determining the optimal location under uncertain aspiration levels.