This study considers a customer order scheduling (COS) problem in which each customer requests a variety of products (jobs) processed on a single flexible machine, such as the computer numerical control (CNC) machine. A sequence-independent setup for the machine is needed before processing each product. All products in a customer order are delivered to the customer when they are processed. The product ordered by a customer and completed as the last product in the order defines the customer order’s completion time. We aim to find the optimal schedule of the customer orders and the products to minimize the customer orders’ total completion time. We have studied this customer order scheduling problem with a job-based processing approach in which the same products from different customer orders form a product lot and are processed successively without being intermingled with other products. We have developed two mixed-integer linear programming models capable of solving the small and medium-sized problem instances optimally and a heuristic algorithm for large-sized problem instances. Our empirical study results show that our proposed tabu search algorithm provides optimal or near-optimal solutions in a very short time. We have also compared the job-based and order-based processing approaches for both setup and no-setup cases and observed that the job-based processing approach yields better results when jobs have setup times.

This study considers an integrated inventory planning and distribution problem based on an applied case at the Turkish Red Crescent’s Central Anatolian Regional Blood Center. We define two echelons, the first echelon being the regional blood center and the second echelon being the districts. The blood products are perishable so that the outdated products are disposed of at the end of their lives. We aim to minimize the cost of inventory keeping at both echelons, the shortage, and disposal amounts at the second echelon. We consider two distribution strategies: all deliveries are realized by the regional blood center (current implementation), and the deliveries are directly from the regional blood center or the other districts. We develop a mixed-integer linear programming model for each strategy. Our experimental results show that the decentralized strategy brings significant cost reductions over the centralized strategy. The mathematical model for the centralized distribution strategy can handle large-sized instances. On the other hand, the model for the decentralized distribution strategy is more complex and could not handle large-sized instances in our pre-specified termination limit of two hours. For large-sized instances of the decentralized distribution strategy, we design a decomposition-based heuristic algorithm that benefits from the optimal solutions of the original model and finds near-optimal solutions very quickly.