Energy-efficient scheduling of AGV-assisted robotic flexible flowshops under learning and processing time uncertainty

This study addresses an energy-efficient flexible flow shop scheduling problem (EEFFSP) that integrates automated guided vehicles (AGVs), sequence-dependent setup times, and the learning effect under fuzzy uncertainty in processing times and learning coefficients. The energy consumption is related to both machines and AGVs in such a way that higher speeds result in higher energy consumption. A mixed-integer programming model is developed to simultaneously minimize makespan and total energy consumption, considering the operation of both machines and AGVs. To handle uncertainty, a fuzzy programming approach based on Jiménez's ranking method is employed. The problem's multi-objective nature is tackled using approaches: the classical AUGMECON method, NSGA, and the NSGA-II algorithms. The proposed hybrid optimization framework effectively captures realistic production features while ensuring computational efficiency. Extensive experiments on benchmark instances demonstrate that the fuzzy-based NSGA-II provides high-quality Pareto solutions with a balanced trade-off between energy efficiency and production performance. Compared with other methods, NSGA-II achieves superior solution diversity and robustness while maintaining acceptable computational time. In this study, an integration of fuzziness, learning effects, and AGV scheduling within an EEFFSP context is provided, a combination not previously addressed in the literature, which provides managerial insights for sustainable manufacturing system design.