Shipment Consolidation and Synchronization of Inbound-Outbound Supply Chain Flows

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A set of suppliers each furnish one or more products to a group of retailers. Direct shipment, from each origin to every destination, is often inefficient. We study, therefore, the distribution consolidation problem, in which products from multiple origins to the various destinations will be transported via a consolidation terminal. The latter is termed "node O" of the supply chain.    As in industry, the flows in this many-to-many supply chain (many suppliers, many retailers) are coordinated by a 3PL (third party logistics provider). We will develop an optimization model, and several extensions, to synchronize these supply-chain flows.    The 3PL does not operate its own transportation assets. Rather, it acts as a broker, to arrange shipments via public carriers, of the goods from each supplier that are ordered by each retailer. Based on demand forecasts for the products, the 3PL commits (signs contracts) for a number of "transportation options." Each such supplier option is a combination of a capacity and an arrival time at the consolidation center O. Similarly, customer transportation options each have a given capacity and time of dispatch from O. Naturally, every option has an associated cost.    The demands for each product can be estimated, but because of randomness in those demands, the amount of required transportation capacity and its timing are uncertain. We will thus model this situation as a two-stage stochastic programming problem. The first stage decisions are the assignment of shipments, inbound to O and outbound from there, to respective transportation options. When the actual demands become known, the second stage decisions involve assignment of loads to spot-market carriers, to the degree necessary.    We will develop an efficient solution algorithm for this problem. Building on already-existing relationships with a supplier and a 3PL, we will refine the basic model as above, and implement it at one or more partner firms. The team of student researchers and I will then expand the applicabili