Handling Sequence-dependent Setup Time Flexible Job Shop Problem with Learning and Deterioration Considerations using Evolutionary Bi-level Optimization

Bi-level optimization is a challenging research area that has received significant attention from researchers to model enormous NP-hard optimization problems and real-life applications. In this paper, we propose a new evolutionary bi-level algorithm for Flexible Job Shop Problem with Sequence-Dependent Setup Time (SDST-FJSP) and learning/deterioration effects. There are two main motivations behind this work. On the one hand, learning and deterioration effects might occur simultaneously in real-life production systems. However, there are still ill posed in the scheduling area. On the other hand, bi-level optimization was presented as an interesting resolution scheme easily applied to more complex problems without additional modifications. Motivated by these issues, we attempt in this work to solve the FJSP variant using the bi-level programming framework. We suggest firstly a new bi-level mathematical formulation for the considered FJSP; then we propose a bi-level evolutionary algorithm to solve the problem. The experimental study on well-established benchmarks assesses and validates the advantage of using a bi-level scheme over the compared approaches in this research area to solve such NP-hard problem.