Multi-objective adaptive large neighbourhood search algorithm for dynamic flexible job shop schedule problem with transportation resource

Strong coupling between autonomous transportation and production activities deeply influences planning and scheduling in smart factories, particularly in dynamic environments with rapid changes. This study addresses the dynamic flexible job shop schedule problem with transportation resources (DFJSPT) in which new job insertion, the most common unexpected event in a manufacturing system, is treated as a dynamic disturbance. A proactivereactive methodology is adopted to respond to dynamic disturbances. Correspondingly, a two -stage multiobjective mixed -integer programming model is formulated for the proposed DFJSPT. In the initial scheduling stage, the model aims to minimize makespan and workload imbalance. In the rescheduling stage, instability minimisation is introduced to deal with the impact of the disturbance. To solve this complex problem, a multiobjective adaptive large neighbourhood search (MOALNS) algorithm is developed. Its novel heuristic operators supporting multi -objective optimization are designed to explore the neighbourhood of a solution. Moreover, the amount of domination between the solutions from the Archive is applied in the acceptance criteria. Overall, we validate the efficiency of the developed model and algorithm through a number of numerical experiments. The computational results verify the accuracy of the mathematical model and demonstrate the superiority of MOALNS in several aspects, including convergence, diversity, and the ability to search for high -quality solutions. In addition to its effectiveness, the algorithm is able to handle dynamic events flexibly, which makes it a suitable option for real -world applications of the DFJSPT.