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.

中文翻译：

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多目标记录到记录旅行元启发式方法，用于解决具有经济和环境目标的森林供应链管理问题

当考虑多个目标并且相互冲突时，多目标优化越来越多地用于协助森林管理决策。由于森林管理问题可能涉及组合优化，因此，随着问题规模的增加，计算复杂度将成倍增加，超出了实际方法的实际使用范围。我们提出了一种多目标元启发式方法，称为多目标记录到记录旅行（MRRT），以解决此类难题。我们检查了MRRT的性能，并将其与森林供应链多目标优化问题上的混合整数规划（MIP）优化器进行了比较，该问题同时使通过打捞收获和利用甲虫杀死的林分产生的净收益和温室气体排放节省最大化。对四种不同问题大小的案例进行的测试表明，MRRT在收敛和覆盖范围方面逼近实际的帕累托前沿，效果令人满意。解决方案的分布大致均匀。MRRT和MIP解决方案之间的差距随着问题规模的增大而增大。但是MRRT可以在合理的计算时间内生成所有解决方案，在大型测试案例中，MRT的计算优势更为明显。