LAUSR

Seedling production is important to modern agriculture for its economic value in land utilization and yield promotion. Three crucial decisions for seedling production are new order acceptance, backlogged order fulfillment, and growth rate control. The first decision is made in an on-line fashion whenever a new order arrives, whereas the subsequent two decisions are made periodically. Given the interplay between the three operations and requirements on the heterogeneous frequency of the decisions, many analytical methods lack sufficient flexibility to deal with the problem and difficult to apply in practice. In this letter, we propose a simulation-based metaheuristic optimization approach to identify the appropriate policy for the above decisions, including a fine-grained simulation model for a representative seedling production process, heuristic decision rules on each of the decisions, and a particle swarm optimization algorithm embeds an optimal computing budget allocation scheme to explore the rules combination space efficiently. Through numerical experiments, we justify the viability of our metaheuristic algorithm; show the superiority of our backlog order fulfillment rules over two benchmark sequential dispatching rules; also demonstrate the economic benefit to periodically regulate the production rate rather than keep in invariant rate. Our work presents a novel application of simulation optimization to smart seedling production operations management.