Sequence-dependent setup times (SDSTs) scheduling problems, learning effects, transportation times, and availability constraints are significant and appealing issues in production management. Researchers often study these issues in isolation, and these constraints have rarely been considered together. The present paper investigated the SDSTs job shop scheduling problem (JSSP) with positionbased learning effects, job-dependent transportation times, and multiple preventive maintenance activities such that, as a result of the learning effects, the times required for processing the jobs were variable during the planning horizon, and each machine had a predetermined number of preventive maintenance activities. For the formulation of the problem in order to minimize makespan, a new mixed-integer linear programming (MILP) model was proposed. Since the problem was highly complex, Grey Wolf Optimizer (GWO) and Invasive Weed Optimizer (IWO) were employed so that near-optimal solutions to medium- and large-sized instances could be obtained. To evaluate the performance and effectiveness of the proposed solution methods, the computational results were used.
