Two-Stage Adaptive Large Neighbourhood Search for Team Formation and Worker Assignment Problems in Cellular Manufacturing Systems

Abstract

We present a novel mathematical programming model to address a team-oriented worker assignment problem, called the team formation and worker assignment problem (TFWAP). The model establishes a multi-skilled team with high group cohesion to meet cell operational requirements. To this end, we developed a two-stage decision methodology based on an adaptive large neighbourhood search (ALNS) method as a solution approach. The first stage was a team formation problem that maximised workers’ skills. The second stage was a worker assignment problem that minimised the total inventory level and variations in the average cell worker’s idle time. The performance of the two-stage ALNS method was assessed on ten cell formation benchmarks selected from the literature. The computational results show that the two-stage ALNS method could provide a solution equivalent to the exact method based on the heuristic-based brute force search (HBBFS) for small instances in the team formation stage. Moreover, the two-stage ALNS method outperformed the non-dominated sorting genetic algorithm-II (NSGA-II)-based single-stage decision methodology on all ten cell formation benchmarks in the worker assignment stage. Finally, the two-way analysis of variance (ANOVA) test highlighted the impact of the cell-cohesion requirement on performance when forming a team in a cell.