Christopher Garcia is an Associate Professor in the College of Business at the University of Mary Washington.
Areas of Expertise (8)
Jepson Fellow (professional)
Conferred by the University of Mary Washington
Faculty Award in Engineering Management and System Engineering (professional)
Awarded by Old Dominion University.
National Defense Industrial Association (NDIA) Scholar (professional)
Engineering Doctoral Fellowship (professional)
Awarded by Old Dominion University.
GAANN Fellowship (professional)
Conferred by Old Dominion University.
Phi Kappa Phi Honor Society (professional)
Golden Key International Honour Society (professional)
Old Dominion University: Ph.D., Engineering Management 2010
Dissertation Title: Optimization Models and Algorithms for Spatial Scheduling
Florida Institute of Technology: M.S., Operations Research 2008
Old Dominion University: B.S., Computer Science and Mathematics 2001
Media Appearances (3)
Chris Garcia Publishes Chapter in Heuristics, Meta-heuristics and Approximate Methods in Planning and Scheduling
Springer Science + Business print
Chris Garcia, assistant professor in the College of Business, co-authored a chapter with Ghaith Rabadi titled “Approximation Algorithms for Spatial Scheduling” in Rabadi, G. (ed) Heuristics, Meta-heuristics and Approximate Methods in Planning and Scheduling, Vol. 236 of the series International Series in Operations Research & Management Science, Springer Science + Business, New York. (The chapter is available online: http://link.springer.com/chapter/10.1007%2F978-3-319-26024-2_1)
Garcia Publishes in Computational Economics
Eagle Eye online
Christopher Garcia, assistant professor in the College of Business, recently had his article “Winner Determination Algorithms for Combinatorial Auctions with Sub-cardinality Constraints” accepted for publication in Computational Economics...
Garcia Presents at Complex Adaptive Systems Conference
Eagle Eye online
Chris Garcia, assistant professor in the College of Business, attended the Complex Adaptive Systems 2013 conference in Baltimore, Md., and presented a paper titled “Demystifying MapReduce” in the Analytics and Big Data track. The paper will be published in the journal Procedia Computer Science...
ABSTRACT: This work studies a scheduling problem where each job must be either accepted and scheduled to complete within its specified due window, or rejected altogether. Each job has a certain processing time and contributes a certain profit if accepted or penalty cost if rejected. There is a set of renewable resources, and no resource limit can be exceeded at any time....jective is to maximize total profit. A mixed-integer programming formulation and three approximation algorithms are presented: a priority rule heuristic, an algorithm based on the metaheuristic for randomized priority search and an evolutionary algorithm....
ABSTRACT: We examine the winner determination problem for combinatorial auctions with sub-cardinality constraints (WDP-SC). In this type of auction, bidders submit bids for packages of items of interest together with a specific number of items they want. All items in a package ...
ABSTRACT: Spatial scheduling problems involve scheduling jobs that each require certain amounts of two-dimensional space within a processing area of limited width and length. Thus, this requires not only assigning time slots to each job but also locations and orientations within the limited physical processing space as well. Such problems, often encountered in shipbuilding and aircraft manufacturing, are generally difficult to solve, and there is a relatively small amount of literature addressing these problems compared to other types of scheduling. In this paper, we consider a particularly complex class of spatial scheduling problems that involve scheduling each job into one of several possible processing areas in parallel to minimize the total amount of tardy time. In addition, each job has a release time before which it may not be processed. We introduce two methods for solving this type of problem: an integer programming (IP) model and a heuristic algorithm. We perform computational tests and comparisons of each method over a large number of generated benchmark problems with varying characteristics, and also compare these to a more naïve heuristic. Solving the IP model was effective for small problems but required excessive amounts of time for larger ones. The heuristic was effective and produced solutions of comparable quality to the IP model for many problems while requiring very little computational time.
ABSTRACT: Recent innovations in Big Data have enabled major strides forward in our ability to glean important insights from massive amounts of data, and to use these insights to make better decisions. Underlying many of these innovations is a computational paradigm known as MapReduce, which enables computational processes to be scaled up to very large sizes and to take advantage of cloud computing. While very powerful, MapReduce also requires a nontrivial shift in algorithm design strategies. In this paper we provide an overview of MapReduce and types of problems it is suited for. We discuss general strategies for designing MapReduce-based algorithms and provide an illustration using social media analytics.
ABSTRACT: We consider a multiproduct sourcing problem where each finished product must meet explicit reliability requirements. The critical reliability of each product is determined by its components, and the objective is to source the components at a minimum cost from a combination of new and used sources while ensuring each product meets its critical reliability requirement. We develop two models to determine optimal sourcing policies: an exact model that uses mixed integer non-linear programming, and an approximate model that uses integer linear programming. We perform computational tests on a large number of diverse benchmark problems and compare the solution quality and computational time of each model. We find that while the exact model requires significant amounts of time for modest-sized problems, the approximation model provides near-optimal solutions across all problem characteristics in very small amounts of time. We also characterize circumstances whereby cost savings can be realized through used sources.