Capacity planning with technology replacement by stochastic dynamic programming

Technology replacement is capital intensive and highly risky in fast-paced high-tech industries along lumpy demand. This article proposes a solution to decision-making related to (i) technology replacement policy and (ii) capacity plan of resources to satisfy customer demand under technological changes. The addressed problem is modeled by stochastic dynamic programming for technology replacement in conjunction with an integer programming for simultaneous capacity planning. The overall objective is to maximize the expected net present profit over a finite time horizon. The problem is solved by a pattern search-genetic algorithm. Experiment results indicate that a near optimal solution is achieved in finite time.