A matheuristic for flowshop scheduling with batch processing machines in textile manufacturing

Abstract

Inspired by a real-world textile packing process, this paper investigates a novel hybrid flowshop scheduling problem with batch processing machines to minimize the makespan. Geometry sizes of textile products, non-identical capacities of packing machines, and the capacity of the needle detector are addressed in parallel-batching modes. To make batching and sequencing decisions simultaneously, a mixed integer linear programming model (MILP) is formulated. Then, a matheuristic method, Tabu Search with Fixing and Optimization (TSFO), is developed. Using tabu search to determine which variables to be fixed, the MILP is solved through a fix-and-optimize process. The results of extensive computational experiments show that the TSFO yields better solutions within much less computation time than solving the model directly in a commercial solver. Furthermore, the TSFO also outperforms an existing method in a special case from literature.