Developing optimization & robust models for a mixed-model assembly line balancing problem with semi-automated operations

In this study, we present two new mathematical models for mixed-model assembly line balancing. The line is semi-automated and characterized by various alternative formats for the operations performed, which range from human operators and assisting robots to robotics solutions, where the operators may have different skill levels and operators on adjacent stations may cooperate. Given the cycle time, the first model minimizes the fixed and variable costs associated with setting up the assembly line. However, the second mathematical model involves a cycle time, which is variable and it changes between a lower bound and an upper bound. These bounds reflect the expected future changes in demand. The objective is to minimize both the costs and the cycle time. To obtain more accurate and reliable results, we present a robust solution for the different confidence levels (alpha) determined by managers. To verify the mathematical models, we considered different test problems and compared the robust solutions under three different conditions. The results showed that the robust modeling approach obtains a more reliable design than the traditional cost trade-off alone. The model allows decision makers to select assembly operations based on a better understanding of their decision impacts in both the short term and long term under conditions of demand uncertainty. (C) 2019 Elsevier Inc. All rights reserved.