An aggregate production planning model for two phase production systems: Solving with genetic algorithm and tabu search

Aggregate production planning (APP) is a medium-term capacity planning to determine the quantity of production, inventory and work force levels to satisfy fluctuating demand over a planning horizon. The goal is to minimize costs and instabilities in the work force and inventory levels. This paper is concentrated on multi-period, multi-product and multi-machine systems with setup decisions. In this study, we develop a mixed integer linear programming (MILP) model for general two-phase aggregate production planning systems. Due to NP-hard class of APP, we implement a genetic algorithm and tabu search for solving this problem. The computational results show that these proposed algorithms obtain good-quality solutions for APP and could be efficient for large scale problems.