Abstract

Decision making in modern forest management planning is challenged by the need to recognize multiple ecosystem services and to address the preferences and goals of stakeholders. This research presents an innovative a posteriori preference modeling and multi-objective integer optimization (MOIP) approach encompassing integer programming models and a new technique for generation and interactive visualization of the Pareto frontier. Due to the complexity and size of our management problems, a decomposition approach was used to build the Pareto frontier of the general problem using the Pareto frontiers of its sub-problems. The emphasis was on the approximation of convex Edgeworth–Pareto hulls (EPHs) for the sub-problems by systems of linear inequalities; the generation of Edgeworth–Pareto hulls by the convex approximation of the Pareto frontier evinced a very small discrepancy from the real integer programming solutions. The results thus highlight the possibility of generating the Pareto frontiers of large multi-objective integer problems using our approach. This research innovated the generation of Pareto frontier methods using integer programming in order to address multiple objectives, locational specificity requirements and product even-flow constraints in landscape-level management planning problems. This may contribute to enhancing the analysis of tradeoffs between ecosystem services in large-scale problems and help forest managers address effectively the demand for forest products while sustaining the provision of services in participatory management planning processes.

Highlights

  • Societies face complex ecosystem management problems due to competing and complementary social values and interactions between these social values and classical timberproduction objectives [1,2]

  • The Pareto frontier is defined by a set of Pareto optimal solutions, whereas globally Pareto optimal solutions are always located on its convex boundary [14]

  • This paper presents an approach for the generation and visualization of Pareto frontiers when dealing with complex problems with a large number of variables using multi-objective integer programming (MOIP)

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Summary

Introduction

Societies face complex ecosystem management problems due to competing and complementary social values and interactions between these social values and classical timberproduction objectives [1,2]. Multi-objective optimization or multiple-criteria decision making is the most computationally demanding category among the approaches [6,7] since it considers problems with multiple conflicting objectives (or goals or criteria). These techniques have been used in an intertwined manner, and the ultimate aim of solving a multi-objective optimization problem has been characterized as supporting the decision makers in finding the solution that best fits their preferences [8]. The need to optimize multi-objective integer programming (MOIP) problems complicates the development and application of Pareto frontier methods since they are often very complex, such that they can take a lot of time to complete when dealing with real-world computationally expensive optimization problems [15]

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