Conference Agenda

Since production environments have become more dynamic, flexible job-shop scheduling problems have become increasingly significant. Manufacturers have elevated the idea of flexible manufacturing to new heights, exploring the implementation of flexible assembly layouts, also called matrix production systems. This study considers the NP-hard optimization problem of flexible job shop scheduling (FJSP), where automated guided vehicles (AGVs) transport bodywork along individual routes between assembly stations. We propose a mathematical model using mixed integer programming to formulate the problem. The performance of the proposed model is analyzed through a comprehensive analysis and solving test problems. In addition, we extend our study to consider the impact of process time uncertainty on scheduling robustness.

We are considering scheduling problems where jobs are required to reenter machines. The presented research focuses on problems where jobs traverse machines in a cyclic loop pattern. We extend previously established work on minimizing the total completion time to minimizing a weighted sum of completion times. We give a structural property about optimal schedules and relate the problem to a parallel machine problem with machine-dependent processing penalties. It allows us to show NP-hardness for the considered scheduling problem with reentry and weighted total completion time objective. Afterward, we analyze an approximation algorithm with a provably small approximation ratio for this problem.