We consider scheduling issues in different configurations of supply chains. The primary focus is to integrate production and distribution activities in the supply chain in order to optimize the tradeoff between total cost and service performance. The cost may be based on actual expenses such as the expense incurred during the distribution phase, and service performance can be expressed in terms of time based performance measures such as completion times and tardiness. Our goal is to achieve the following objectives: (i) To propose various integrated production-distribution scheduling models that closely mirror practical supply chain operations in some environments. (ii) To develop computationally effective optimization based solution algorithms to solve these models. (iii) To provide managerial insights into the potential benefits of coordination between production and distribution operations in a supply chain.

We analyze four different configurations of supply chains. In the first model, we consider a setup with multiple manufacturing plants owned by the same firm. The manufacturer receives a set of distinct orders from the retailers before a selling season, and needs to determine the order assignment, production schedule, and distribution schedule so as to optimize a certain performance measure of the supply chain. The second model deals with a supply chain consisting of one supplier and one or more customers, where the customers set due dates on the orders they place. The supplier has to come up with an integrated production-distribution schedule that optimizes the tradeoff between maximum tardiness and total distribution cost. In the third model, we study an integrated production and distribution scheduling model in a two-stage supply chain consisting of one or more suppliers, a warehouse, and a customer. The objective is to find jointly a cyclic production schedule at each supplier, a cyclic delivery schedule from each supplier to the warehouse, and a cyclic delivery schedule from the warehouse to the customer so that the customer demand for each product is satisfied fully at minimum total production, inventory and distribution cost. In the fourth model, we consider a system with one supplier and one customer with a set of orders placed at the beginning of the planning horizon. Unlike the earlier models, here each order can have a different size. Since the shipping capacity per batch is finite, we have to solve an integrated production-distribution scheduling and order-packing problem. Our objective is to minimize the number of delivery batches subject to certain service performance measures such as the average lead time or compliance with deadlines for the orders.