Simulation-based multi-objective optimization to support delivery performance decisions in harvest scheduling and transport

Abstract

Harvest scheduling and transport are crucial for the delivery performance of a wood supply chain, ensuring that product volumes are delivered on time and in the right quality. This paper suggests three delivery performance objectives for the wood supply chain: service level, lead time, and throughput. It presents a framework for optimizing these objectives by finding trade-off solutions using simulation-based multi-objective optimization. Due to the complexity of the wood supply chain, discrete-event simulation is used to evaluate delivery performance from harvesting to customer delivery. The harvest scheduling problem is formulated as a permutation optimization solved by a customized NSGA-II algorithm with a comparison of three crossover mechanisms implemented: Random Key Simulated Binary Crossover, Order Crossover, and Partially Mapped Crossover, specifically designed for general forestry permutation optimization problems. Analyzed with a heatmap for the visualization of the mapping of the decision space to the Pareto-optimal solutions, the results indicate that the Partially Mapped Crossover performs best. Other simulation-optimization generated data are processed and visualized in an interactive, web-based dashboard for decision-makers, such as forest managers, allowing them to analyze meta-heuristically optimized solutions in both the solution and decision spaces, guiding them to find the most suitable harvest schedules.