Manufacturing rescheduling after crisis or disaster-caused supply chain disruption

In this paper, we study the problems a repair shop has with rescheduling after major supply disruptions. The repair shop provides repair and maintenance services to its customers. After a major disruption to production, the repair shop faces delays in production and order delivery due to shortages in materials and/or labor, which requires rescheduling of all the unfinished parts. We observe that the finished parts incur high holding costs until the entire order is completed, while any unfinished parts (in the form of raw material or work-in-progress) incur low holding costs until production starts. Moreover, the repair shop incurs a setup cost when switching between different types of parts. Considering these new features, we formulate the rescheduling problem for the repair shop under a coordinated supply chain as an integer program to minimize the total tardiness, setup cost, and holding cost. To solve the model, we propose an innovative two-stage genetic algorithm, which utilizes the estimation of distribution algorithm (EDA) to improve the search process of the optimal solution. We test the performance of this algorithm on a dataset generated from the order data of a heavy machinery maintenance provider. The numerical results show that our model generates solutions that outperform the initial schedule, which was obtained by minimizing holding and setup costs without disruption. In addition, using other closely-related genetic algorithms as benchmarks, we show that our algorithm outperforms the benchmarks without sacrificing the computational time. We also discuss an extension of the main model by considering the recovery of productivity in terms of processing time.