Assessing the benefits of production-distribution coordination in an industrial gases supply chain

<p>In this paper, we propose a multi-period MILP model for optimal enterprise-level planning of industrial gas operations with an objective to minimize the total cost of production and distribution. As compared to the fully sequential approach for optimizing production and then optimizing distribution, the proposed fully coordinated model simultaneously considers various trade-offs between production and distribution activities and yields optimal operational decisions for the supply chain as a whole. Through the continuum between fully sequential and fully coordinated approaches, different levels of coordination are investigated. The computational results for small instances show potential savings if a fully coordinated approach is implemented in place of a fully sequential one. This can be attributed to improved coordination of the production schedule with the distribution withdrawal schedule.</p

Simultaneous Production and Distribution of Industrial Gas Supply-Chains

In this paper, we propose a multi-period mixed-integer linear programming model for optimal enterprise-level planning of industrial gas operations. The objective is to minimize the total cost of production and distribution of liquid products by coordinating production decisions at multiple plants and distribution decisions at multiple depots. Production decisions include production modes and rates that determine power consumption. Distribution decisions involve source, destination, quantity, route, and time of each truck delivery. The selection of routes is a critical factor of the distribution cost. The main goal of this contribution is to assess the benefits of optimal coordination of production and distribution. The proposed methodology has been tested on small, medium, and large size examples. The results show that significant benefits can be obtained with higher coordination among plants/depots in order to fulfill a common set of shared customer demands. The application to real industrial size test cases is also discussed</p