Multiobjective record‐to‐record travel metaheuristic method for solving forest supply chain management problems with economic and environmental objectives

Abstract

Abstract Multiobjective optimization is increasingly used to assist decision‐making in forest management when multiple objectives are considered and conflict with each other. Since forest management problems may deal with combinatorial optimization, as the scale of a problem increases, the computation complexity increases exponentially beyond the practical use of exact methods. We propose a multiple‐objective metaheuristic method, referred to as multiobjective record‐to‐record travel (MRRT), to solve such challenging problems. We examined the performance of MRRT and compared it to a mixed integer programming (MIP) optimizer on a forest supply chain multiobjective optimization problem that simultaneously maximizes net revenues and greenhouse gas emission savings from salvage harvest and utilization of beetle‐killed forest stands. Testing on four cases of different problem sizes showed that MRRT performed satisfactorily in approximating the actual Pareto fronts in terms of convergence and coverage, and the distribution of solutions was approximately uniform. The gap between MRRT and MIP solutions increased as the problem size increased. But MRRT produced all solutions within a reasonable computation time, where the computational advantage over MIP was more apparent for large‐scale test cases. Recommendations for Resource Managers Multiobjective optimization shows trade‐offs among conflicting objectives and assists decision‐making to enhance sustainable forest management. Multiobjective record‐to‐record travel (MRRT) has a simple algorithm structure and easy parameterization process so that it is adaptable to solve various multiobjective optimization problems. MRRT produces high‐quality solutions for large‐scale multiobjective optimization problems within a reasonable computation time, which promotes its applicability in practice.