Optimizing the trade-off between crucial decisions has been a prominent issue to help decision-makers for synchronizing the production scheduling and distribution planning in supply chain management. In this article, a bi-objective integrated scheduling problem of production and distribution is addressed in a production environment with identical parallel machines. Besides, two objective functions are considered as measures for customer satisfaction and reduction of the manufacturer’s costs. The first objective is considered aiming at minimizing the total weighted tardiness and total operation time. The second objective is considered aiming at minimizing the total cost of the company’s reputational damage due to the number of tardy orders, total earliness penalty, and total batch delivery costs. First, a mathematical programming model is developed for the problem. Then, two well-known meta-heuristic algorithms are designed to spot near-optimal solutions since the problem is strongly NP-hard. A multi-objective particle swarm optimization (MOPSO) is designed using a mutation function, followed by a non-dominated sorting genetic algorithm (NSGA-II) with a one-point crossover operator and a heuristic mutation operator. The experiments on MOPSO and NSGA-II are carried out on small, medium, and large scale problems. Moreover, the performance of the two algorithms is compared according to some comparing criteria. The computational results reveal that the NSGA-II performs highly better than the MOPSO algorithm in small scale problems. In the case of medium and large scale problems, the efficiency of the MOPSO algorithm was significantly improved. Nevertheless, the NSGA-II performs robustly in the most important criteria.

Machine failures during production period may impose thousands to millions of dollars to a manufacturing system. In this paper, the impact of machine failures on production lines in a closed-loop supply chain systems is examined. For this purpose, a new method is proposed for scheduling manufacturing workshops in a supply chain systems. The aim is to determine the best production plans in a manufacturing system by considering alternative preventive maintenance programs while machine failures can affect system performance. To solve the model, a hybrid Ant Colony and Simulated Annealing algorithms is developed and the results are compared with branch and bound method. Our findings show that the condition of emerging machine failure affects machines’ capacity which yields to lost sale. The impacts of using appropriate preventive maintenance on reducing lost sale is also examined. The results indicate that the proposed method can significantly reduce the level of sale variation in supply chain systems.