Order acceptance and scheduling in a two-machine flowshop

We study the order acceptance and scheduling problem in a two-machine flowshop. The firm receives a pool of orders before a planning period, each of which is characterized by revenue, processing times on machines 1 and 2, a due date, and a tardiness penalty. The firm seeks to decide on the orders to accept and schedule the accepted orders so as to maximize the total net revenue. We formulate the problem as mixed-integer linear programming models, and develop a heuristic and a branch-and-bound (B&B) algorithm based on some derived dominance rules and relaxation techniques. We assess the performance of the B&B algorithm and the heuristic via computational experiments. The computational results show that the B&B algorithm can solve problem instances with up to 20 jobs within a reasonable time while the heuristic is efficient in approximately solving large instances of the problem.