(410e) Management of Supplies and Movements of Tank Containers in Chemical Logistics

Logistics is the glue that binds the entities of a supply chain. More and more businesses have now realized that its effectiveness is critical to the superior performance of the supply chain. For instance, the move towards just in time and agile manufacturing has tremendously increased the impact of the logistics decisions. This is especially true for the chemical industry with its globally distributed plant sites, storage terminals, suppliers, customers, etc. The global transport of chemicals and a variety of other materials (e.g. plant equipment, instrumentation, indirect materials, safety equipment, etc.) is central to its day-to-day operations. Logistics costs can vary from 3.6% of the purchase price for a best-in-class (BIC) site to 20% at the other extreme (Karimi et al., 2002).

The transport of chemicals by pipelines is ideal. However, this is not always possible. Chemical logistics requires a mix of means such as trucks, trains, ships, barges, tankers, etc. Sea transport is the key to global chemical logistics. Bulk shipping of chemicals occurs in huge volumes, and involves very large crude carriers (VLCCs) and a variety of multi-parcel chemical tankers. The former routinely transport crude oils worldwide, while the latter transport clean petrochemical products (CPP) and other chemicals such as vegetable oils (Jetlund & Karimi, 2004). However, there is another equally important segment of chemical logistics, called container shipping, which has received little attention in the literature. Moving and even storing low-volume and/or hazardous specialty chemicals and other food products by tank containers has become the most preferred option from the viewpoints of safety, cost, and environment. Furthermore, containerization facilitates for inter-modal transport over land and sea.

This paper addresses a key management issue faced by chemical logistics companies in managing the supplies and movements of tank containers (loaded as well as empty). For many chemical companies and their third party logistics partners, minimizing the logistics costs arising from the container flow imbalances across the globe and container cleaning is a major issue. In contrast to dry containers, this important problem of managing tank containers in global chemical logistics has not received much attention. Karimi et al (2005) present an innovative, event-based, ?pull? approach for the minimum-cost or the maximum-profit scheduling of the transport and cleaning of multi-product tank containers given a set of projected shipment orders. Compared to the network modeling approach that has been used for traditional dry containers, their approach appears to be more efficient and flexible. Furthermore, their model incorporates several key practical features such as alternate ship schedules, delivery time-windows, and inter-modal transport routes. Their continuous-time linear programming formulation successfully solves large, industrially relevant problems of any deterministic case. In this work, we extend the application of the above model to handle the uncertainty in container demands and their due dates. Container orders may include any or all of three different types, namely deterministic, partially stochastic and fully stochastic. We propose a stochastic programming model to address the above mix of order uncertainty and solve it using sample average approximation (SAA) scheme (Kleywegt et al., 2001). We illustrate the performance of our model using a few examples.

Keywords: Tank container management, Chemical logistics

References:

(1) Jetlund, A.S., Karimi, I.A. Improving the logistics of a fleet of multi-parcel chemical tankers. Computers & Chemical Engineering, 2004, 28, 1267-1283.

(2) Karimi, I.A., Srinivasan, R., Han, P.L. Unlock supply chain improvements through effective logistics. Chemical Engineering Progress, 2002, 5, 32-38.

(3) Karimi, I.A., Sharafali, M., Mahalingam, H. Scheduling tank container movements for chemical logistics. AIChE Journal, 2005, 51, 1.

(4)Kleywegt, A.J., Shapiro, A., Homem-De-Mello, T. The sample average approximation method for stochastic discrete optimization. SIAM Journal of Optimization, 2001, 12, 479-502.