CAREER: Analysis of Multi-dimensional Coordination Problems in Service Parts Logistics Systems

University Of Texas At Austin, Austin TX

Investigators

Abstract

This Faculty Early Career Development (CAREER) award provides funding for the development of models and algorithms for coordination problems in Service Parts Logistics (SPL) systems. The challenge in SPL systems is to coordinate decisions across business units, time horizons, and geographical locations at the same time towards high-quality system-wide solutions. The research first will develop a unified framework for integrated logistics network design and inventory management and explore decomposition-based solution methodologies for the associated problems formulated as mathematical models. The logistics network design submodel involves multiple echelons, multiple products and time-based service coverage restrictions. The inventory submodel involves inventory sharing across multiple facilities and response-time based service levels. Motivated by the scale of the problems existing in real SPL systems, the research will also investigate different levels of granularity of the models, and identify the trade-off between computation and solution accuracy. The research will finally address the mechanism design and incentive issues that inherently exist in coordination problems: How does one make sure that the decision entities have the necessary incentives to coordinate their decisions? The closely related educational plan of the award involves the development of a teaching case on modern SPL systems, and the development of a course on mathematical modeling of supply chain coordination problems. If successful, the results of this research will lead to the solution of problems in SPL, both in size and scope, commonly faced by industry, the government and the military. The models and their results will be validated and verified with partners in SPL. The investigations should contribute in finding scalable solution methodologies for large-scale mathematical models with special structure and their application to real problems. The results will also provide insights in aligning the individual decision entities' goals with the system-wide goals. Analyzing these issues simultaneously in a unified framework will improve the understanding of how these factors (different levels of coordination and incentive issues) affect each other in distributed logistics systems. Another contribution will be to undercover the relationship between coordination mechanisms and decomposition-based optimization techniques. The educational component should enhance teaching in applied operations research, supply chain management, and logistics courses.