In today’s competitive environment, industrial units are seeking to reduce costs and increase customer numbers; if their business involves perishable products, achieving these goals is even more important. By integrating scheduling and vehicle routing problems for perishable products, this study tries to minimize costs and maximize customers’ purchase probability. In the scheduling stage, a flexible flowshop scheduling problem considering production quality is studied. After completion of the last job, the distribution stage begins and each product must be delivered in its time-window. This problem is formulated as mixed-integer linear programming and solved by the GAMS solver. Owing to the NP hardness of this problem, a hyper-heuristic is proposed to solve large-size instances. In the proposed algorithm, the acceptance of the solution is based on the Monte Carlo criterion. Finally, the numerical results and analysis demonstrate the proposed algorithm’s superiority on some criteria compared to the non-dominated sorting genetic algorithm-II (NSGA-II).
