Robust scheduling for three-machine robotic cell using interval data

Abstract

In reality, due to the lack of adequate environmental information, uncertainty is a common practice. In order to provide good and acceptable solutions, development of systematic methods for solving problems of uncertainty is important. One of these methods is based on robust optimization. This type of planning is to find a solution that is not sensitive to parameter fluctuations. In this article, a new way is represented to solve a three-machine robotic cell problem. An intervallic processing time is concerned as the problem being discussed. Different scenarios are defined by using robust optimization; afterwards, applying min-max regret method, robust counterpart of original problem is specified. Since the problem is NP-hard, a metaheuristic is applied to solve it. Genetic Algorithm (GA) as a population-based metaheuristic is employed. Cycle time and program operating time are calculated for different number of parts. It is demonstrated that by increasing the part numbers, gap between the robust and original cycle time increases. It is observed that both the cycle time and algorithm operating time increase.