Optimal Production Planning, Sourcing, Distribution and Routing for Complex Energy Intensive Manufacturing Companies Using High Performance Computing

Award Information
Department of Energy
Solitcitation Year:
Solicitation Number:
Award Year:
Phase II
Agency Tracking Number:
Solicitation Topic Code:
Small Business Information
Optimal Solutions, Inc.
17 Kershaw Ct., Bridgewater, NJ, 08807-2595
Hubzone Owned:
Woman Owned:
Socially and Economically Disadvantaged:
Principal Investigator
 Vijaykumar Hanagandi
 (908) 393-1316
Business Contact
 Vijaykumar Hanagandi
Title: Dr.
Phone: (908) 393-1316
Email: hanagandi@gmail.com
Research Institution
Todays approaches to production planning and inventory management in a distribution network for large industrial gases and chemicals manufacturers are based on static snap-shot data and are not suitable for real-time use in tactical demand fulfillment. Companies have yet to harness the potential of powerful new computing technologies to find solutions for energy and other costly inefficiencies persisting in their supply chains many of which must be solved in real-time. This project addresses DOEs interest in turnkey solutions advancing the use of HPC in manufacturing and is intended to result in increased supply chain efficiency, reduced costs, and job creation. The overall objective is to develop a next generation production planning and distribution optimization algorithm together with a data-integration platform to provide timely and accurate data required for the optimization model. We take a two-pronged approach to address the problem: (1) leverage HPC technology to parallelize and rapidly solve the production planning and distribution problem and (2) leverage Big Data technology on a HPC platform to address the required data-integration. Phase I R & amp;D resulted in (1) a comprehensive model formulation that provides vehicle routing, production optimization and inventory optimization simultaneously and (2) a Big Data analytics framework to support the generation of the required input data. We demonstrated that using HPC, we can achieve 1080- times faster performance (vs. non-HPC implementation) without loss of optimality, thus proving feasibility of our innovation. We used industrial data and benchmarking to prove optimality and scalability. Our results were reviewed by several prospective customers and we received encouraging feedback (see support letters). We plan to build on the formulations and the test-bed created in Phase I and improve our algorithms. Our research activities will also focus on overcoming challenges including scalability of the solution, data security, and total cost of ownership. Our target is to be ready by the end of Phase II with a prototype turnkey application running on a hosted-HPC infrastructure, which allows us to start commercialization of our product as a service over the Internet to a variety of customers. Commercial Applications and Other Benefits: The envisioned software system will be used to obtain optimal production planning and distribution at multi-product, multi-depot manufacturing companies which utilize many fleets of vehicles. It is expected to result in significant efficiencies and reductions in energy and other operating costs. It is intended to increase the global competitiveness of the U.S. manufacturing sector and result in job creation.

* information listed above is at the time of submission.

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