Solving production scheduling optimization integer programming problems based on simulated annealing algorithm

Abstract

This paper addresses the optimization of crop production scheduling through a comprehensive integer programming framework, with a particular focus on maximizing total sales revenue. We initially formulate a linear programming model that incorporates various constraints, including expected demand, costs, production volumes, and pricing strategies. Specifically, two scenarios are considered: overproduction leading to unsold waste and overproduction necessitating price reductions. For each scenario, mathematical models are developed using integer programming techniques. To solve these models efficiently, a simulated annealing algorithm is employed, which is tailored to explore the solution space and converge to optimal production schedules. The proposed methodology is applied to generate optimal crop production plans for the next six years, demonstrating significant improvements in revenue maximization and resource allocation.