The Assembly Line Balancing Problem in the Presence of Task Learning and Demand Fulfilment (ALBLDP)

The assembly line balancing problem (ALBP) involves assigning the tasks required to assemble a completed product to the workstations along a serial production line. Discovering an optimal solution that minimizes the number of workstations while meeting demand and integrating realistic assumptions has been the subject of some research. In this study, we propose a model that considers learning curve effects and dynamic demand. In instances of a new assembly line, new employees, introducing new products or simply implementing engineering change orders, task processing times may change continuously due to the increasing repetitions and learning until they reach near steady-state levels. Similarly, demand per period may vary due to ramp-up factors, market demands or order commitments. To achieve a cost-based optimal solution, we propose an integer mathematical formulation to minimize the production line's total cost including the fixed cost and operational cost (variable cost) per unit time related to the workstations under the impact of learning and demand fulfillment. The proposed model assigns tasks and cycle times to minimize total line cost while meeting demand which is assumed to be increasing with time. To assess the model's performance, data sets from the literature are employed with different problem sizes and precedence structures. The model performs well in terms of computational time, which varies from seconds to a few minutes for problems of up to 50 tasks.